That’s the scenario for the European venture capital landscape. So far this year, US firms have contributed a whopping 42% of the funding for European startups — and their physical footprint on this side of the pond is growing steadily.

For some, this American financial influx might seem a cause for concern. But, actually, couldn’t it be European startups’ golden ticket?

The current landscape

Earlier this year, amidst a global market slowdown and with the US VC market cooling substantially from its 2021 peak, many pundits in Europe predicted a major retreat by US investors. This would have spelt doom for European startups, who have leaned on American capital year after year, particularly at later stages.

On the contrary, 2023 has seen US funds intensifying their focus on Europe, establishing bases and, in relative terms, augmenting the venture capital they funnel into European businesses.

A double-edged sword?

There's a growing debate surrounding this reliance on American capital, especially considering its potential influence on strategic European technologies like deeptech. There are fears that an influx of investment might encourage European startups to pivot westward, adopting a strategy of "build in Europe, sell in the US."

While lucrative, this approach could alter the accountability dynamics of successful companies.

Furthermore, from the UK Brexiting to France boycotting the NATO Innovation Fund (NIF), European countries always seem eager to throw a spanner in the works of a truly united startup market. This fragmentation might be a significant factor behind the sluggish development of cross-border European investment as an alternative to foreign capital.

But let's not demonise this overseas capital influx when it validates the potential of European startups and fosters innovation and expansion of local hubs.

[Lack of] Unity in Europe

As I've previously highlighted, fostering a united European tech and VC market through a supra-national strategy could be the key to nurturing innovation and growth ─ especially in sectors critical to national security such as energy, climate, defence, and advanced computing technologies.

This strategy should encompass government funding and a supportive cross-border legal and tax framework to encourage pension funds and affluent individuals to back pan-European VCs and startups.

While this transformation won't occur overnight, it is not as far-fetched as sceptics might believe.

After all, collaborations extending beyond EU borders are already manifesting, with the UK rejoining Horizon and the NIF showcasing that European investment collaboration doesn't have to end at the EU border.

What now?

Looking ahead, a shift in the balance of power is on the cards.

The increasing US investment in Europe, although beneficial in many ways, could also alter the dynamics of the European tech sector.

In the grand scheme of things, this might not be entirely negative. As US venture capital firms increase their stake in Europe, it's plausible that American Limited Partners (LPs) might follow suit, bringing in a fresh wave of investment and possibly fostering a symbiotic relationship between the two regions.

In my anecdotal experience launching my first fund, I noticed a difference in risk appetite between US and European LPs: with the latter generally more knowledgeable about venture capital, less bureaucratic, and more receptive to companies in sectors like defence or hardware companies.

Moreover, their investment doesn't generate geographical or sectoral restrictions, which fund managers often self-impose when taking on board European government LPs or risk-averse wealth holders. Lastly, as demonstrated by Israel, selling or listing startups in the US can reciprocate benefits to the local economy.

However, navigating this relationship demands caution. If European investors continue to rely heavily on the US for funding and also become increasingly backed by foreign capital, Europe's aspirations for tech sovereignty might remain a distant dream.

A wake-up call?

All in all, the Yanks seem to love European tech even more than Europeans themselves. I don’t see why the situation would change going forward.

At least from the US end. As we stand at this crossroads, it's time to shed the apprehensions and adopt a more balanced, yet assertive stance on European startup funding.

While Europe needs to maintain its unique identity and nurture home-grown innovation, the influx of US investments offers numerous benefits.

Not least as it may act as a wake-up call for individual governments to foster a more unified tech market and for local investors to take bolder bets: particularly in critical national security areas such as energy, climate, defence, or advanced computing technologies.

Also featured on Sifted.eu on 27/09/2023

From the scarcity of resources to the necessity for specialised expertise, the hurdles can be formidable. However, there exists a strategy that can help startups overcome these obstacles and accelerate their tech development whilst keeping their business lean: technology outsourcing. Don’t shake your head just now. Hear me out first.

Aren’t Startups Supposed to Build In House?

Yes, tech outsourcing does come with big risks. Yes, outsourcing is NOT suitable for everything. And yes, I am the first who advocate startups to build their “secret sauce” in-house and have top technical expertise in their founding team.

However: a) as an investor I invest in deeptech and not all startups are deeptech and rely on mind-boggling technology IP as their secret sauce, and b) even in deeptech, they may consider outsourcing non-critical parts of their product offer to free up time and energy for their top-notch in-house experts.

Recently, I had the opportunity to engage in a thought-provoking chat with 2 outsourcing experts: Alastair Copeland, CEO at SmartDev, and Charlie Russell, Practice Lead at Smart81. The first is an extremely successful international outsourcing company, and the latter is a sister company that doesn’t just build tech for startups, it actually takes on certain startups as a ‘build-for-equity’ co-investor that’s helped startups bridge tech development between investment rounds.

As we delved deep into the intricacies of tech outsourcing for startups, the insights gained from this conversation were enlightening, to say the least.

The Outsourcing Advantage

Outsourcing, often associated with cost-cutting, has evolved significantly over the years. Today, it's not merely about saving money, but about accessing a global talent pool, accelerating time-to-market, and fostering innovation.

According to a study by Avasant, an impressive 60% of companies outsourced at least some of their application development in 2020, up from 56% the year before and on a growing trajectory.

This is a statistic that’s hard to ignore. But what does this mean for startups, particularly those in Europe? Can they leverage outsourcing too?

For European startups, the outsourcing landscape offers a unique proposition. Europe, with its diverse talent pool and technological prowess, isn’t short of technical talent. Yet, the challenge lies in harnessing this potential efficiently. Especially in hubs where tech talent is most in demand. Outsourcing can bridge this gap.

A report by Deloitte highlighted that companies are not just outsourcing to cut costs anymore; they are now looking to achieve innovation, tapping into new market trends and accessing intellectual capital. This is precisely where startups can capitalise.

By outsourcing non-core functions, startups can focus on their unique value proposition, ensuring they remain at the forefront of innovation. Outsourcing can also be useful to quickly scale up or down development power, which can be useful for one-off tasks, such as carrying out a specific integration or developing a specific customer UI.

Diversifying Risk and Accelerating Growth

One of the primary concerns for startups is the risk associated with rapid scaling. By diversifying their operations and leveraging global expertise, startups can mitigate these risks.

Charlie pointed out during our chat that startups could use outsourcing as a strategy to scale faster to their next funding round. It can be great for companies when: “they've got some tech, they've got some customers, they've got people paying for their products, but they need to develop their tech further in order to grow what they have, scale their businesses and keep their customers happy. Why not bring in some outsourced talent, short term, to boost your output and get you where you need to be, then scale it back once the goal is achieved?”.

This approach not only diversifies the risk but also provides startups with a broader perspective, tapping into global best practices.

Access to Specialised Expertise

In our discussion, Alastair emphasised the importance of startups having access to specialised expertise, especially when they are in their nascent stages.

Outsourcing allows startups to tap into a vast reservoir of global talent without the overheads associated with full-time hires. This is particularly crucial for startups that might not have the resources to hire specialists for every tech function.

Bringing in an expert as a full-time local contractor can be a big commitment, it can take a long time to recruit and can be expensive too, with higher European wages, employee equity pools to consider and high recruiter fees. By outsourcing that particular task, the company can overcome specific development challenges without overloading its in-house workforce.

Flexibility and Agility

In the ever-evolving tech landscape, agility is the name of the game. Startups need to be nimble, adapting to market changes swiftly.

Outsourcing offers this flexibility.

With the ability to scale up or down based on project requirements, startups can remain lean and agile, ensuring they are always ready to pivot when necessary.

Challenges of Outsourcing: It's Not All Rosy

While there are all these interesting advantages to outsourcing, it's essential to approach it with a clear understanding of its challenges. Both Charlie and Alastair touched upon some critical points that startups need to be wary of:

1. Cultural and Communication Barriers: Engaging with teams from different cultural backgrounds can lead to misunderstandings. It's crucial to invest time in understanding these differences and establishing clear communication channels.

2. Quality Control: Ensuring the quality of work when outsourcing can be challenging. Regular interactions, as highlighted in our chat, are vital to maintaining the quality of work.

3. Data Security: With increasing concerns about data breaches, startups need to ensure that their outsourcing partners have robust security protocols in place.

4. Dependency: Over-reliance on an outsourcing partner can lead to a startup losing its core competencies. It's essential to strike a balance, ensuring that the startup retains its in-house expertise while leveraging external resources. Consider looking at a firm that operates a build-operate-transfer model to allow you to retain resources and effectively bring it in-house, if desirable.

The Road Ahead

Europe, with its rich history of scientific and technological breakthroughs, is a hotbed of innovation. Today, the continent is home to a burgeoning tech ecosystem, with startups sprouting in every corner, from Berlin to Barcelona.

Yet, despite the talent and potential, many European startups face challenges in scaling, especially when jumping from one funding stage to the next. And especially in a challenging fundraising environment. Bridge rounds and convertible notes are increasingly common, for instance.

In this context, whilst not a one-size-fits-all, outsourcing can be a powerful weapon in a startup arsenal. Operators such as Smart81 are even bringing out novel products such as their “build for equity” model, which makes it even more interesting for startups to try to use outsourcing creatively.

However, like any strategy, it's essential to approach it with a clear understanding of its benefits and challenges.

The key lies in understanding when to outsource, what to outsource, and whom to partner with. And as always, it's about striking the right balance.

Here is why: Did you know that venture capital-backed companies account for a whopping 41% of the total US market cap and 62% of US public companies’ R&D spending?

That's the power of venture capital, folks:

1. Among public companies founded within the last 50 years, VC-backed companies account for 1/2 in number, 3/4 by value, and more than 92% of R&D spending and patent value.

2. The US did not spawn top public companies at a higher rate than other large, developed countries before the 1970s ERISA pension reforms, but produced twice as many after it (more on this below).

3. The paper "The Economic Impact of Venture Capital: Evidence from Public Companies" suggests that the US VC industry, itself enabled by a pension money flood, is causally responsible for the rise of one-fifth of the current largest 300 US public companies.

And what's ERISA? The Employee Retirement Income Security Act (ERISA) allowed US pension funds in 1974 to invest in venture capital, leading to a significant increase in the amount of capital available for venture investments.

This policy change played a significant role in the growth of the VC industry and the creation of large public companies.

But what about Europe? Trillions are locked away in pension funds, unable to reach the local venture market.

Europe lags behind, but there are signs of change.

In the UK, the government is looking to channel more pension investments into UK companies.

Auto-enrolment has been a success, with the proportion of private sector eligible staff participating in a pension increasing sharply from 42% in 2012 to 85% in 2019.

However, most pension funds are invested in low-risk equities and bonds, missing out on a vital slice of economic growth: venture capital.

In Sweden, state-owned pension funds are putting aside billions to invest in the growing startup sector through VC funds. Not surprisingly, Nordic pension funds account for 16% of total VC funds raised in the region since 2013.

However, in Germany, pension funds’ investments in VC only account for a small percentage of VC funds raised.

The risk-averse mindset needs to be overcome with top-down policies, even if pensioners and old-school pension fund managers may disagree.

The takeaway? Europe needs to push harder to open the gate for pension funds to ignite a venture capital revolution. The US example shows a clear path of how pension money can fuel a startup revolution through VC investing.

As a deep tech VC, I can't stress enough the importance of policies that foster innovation and growth.

Let's continue to push for policies that support the VC industry and the incredible companies it helps to create.

The average financial commentator in Europe is too risk-averse to accept the clear evidence.

Of course, the average pensioner or pension fund manager in Europe, as represented by the top Financial Times readers, struggles to wrap their head around the perceived risk. But as it happened in the US, over a long time horizon and across the broader market, VC exposure does generate above-market, stable returns.

This is the power of unlocking them for pension funds: creating a huge pool of capital for the European tech sector that is not looking for quick returns and moving in and out of the market with the hype trends (like a family office or a trading fund may do), but is going to provide long-term liquidity over time horizons spanning decades.

On those timelines the top VC-backed companies outperform. And it's time their wealth creation stops being a privilege of the US tech market and benefiting only American pension investors.

The bulk of this article was first published by the author on LinkedIn

Shining a light on an opaque industry

Why bother? Because the VC industry is incredibly opaque and I really struggled to build mine!

Very few venture capital fund decks are out on the open web. And probably none from emerging managers that are complete outsiders to VC and raising their first nano fund. I’m here to challenge that by sharing the very deck that I used for my first closing a few months ago. Just as it was. Before I could start adding details on companies in the portfolio (like my current version has) or build a track record from previous funds (like most examples online).

And I will do it by pitching it to Maria Marques Nunes from Aldea Ventures: a fund splitting their investments 50% directly into companies and 50% into emerging fund managers.

The goal is to pay it forward to fellow emerging VC managers by sharing actionable insights that can hopefully help them improve their pitch decks faster than I did and increase their chances of securing investments from LPs.

TLDR Summary of Key Lessons

1. Showcase your team background and track record

2. Have a clear and concise investment thesis

3. Include your target fund size and allocation strategy

4. Showcase your connections in the VC ecosystem

5. Don't be afraid to include numbers

6. Consider mentioning the fundraising timeline and definitely include the key economic terms

Do we really need more VCs to “emerge”?

In these challenging times, we face global crises, from financial uncertainty and the threat of hi-tech warfare in a de-globalising world to the devastating impacts of climate change. The world urgently needs innovative founders to develop and commercialise new technologies to address these and other issues.

Likewise, it needs bold investors who are willing to support these founders, even if they don't fit the traditional VC-backed startup mould. The tech and venture capital sectors also need to become more diverse.

That’s why I believe we must encourage new fund managers, preferably from non-traditional backgrounds, to establish VC firms and challenge the status quo. In Europe in particular, VC is still far from a zero-sum game, and we should support one another in growing the industry. I, for once, eagerly await the day when I can regularly invest as an LP alongside my VC career ─so far I have joined one LP syndicate. Since backing emerging funds can be as enjoyable and just as financially rewarding as angel investing in startups. But that's another story...

Now, let's dive into the live pitch:

Slide-by-Slide Analysis

This section, the core part, presents a slide-by-slide analysis of our pitch deck “first closing edition”, as I presented it to Maria. The conversation focused on the main presentation and relevant appendix slides, capturing key commentary and insights from both parties.

The transcript was edited to keep only the relevant comments and make it more easily readable:

Slide 1: Title and Contacts Slide

No comment ─it’s just an introduction slide. I chose to have a one-line statement and contacts here. The tip came from the pitch guidance from VCLab, which by the way is an awesome online programme for emerging managers we went through with Silicon Roundabout Ventures and that proved very useful to launch our fund. Their frameworks and peer review groups helped a lot in finessing both our thesis and initial deck.

Readability note: if you see the images with a dark background in dark mode, switch over to light mode (the sun/moon icon in the top right corner)

Slide 2: Investment Thesis

Maria Marques Nunes: "The investment thesis is important information for us. I like that you have the main information in the first slide, like the target size and the focus on deep tech. Institutional investors can often only invest in certain jurisdictions. On the first page, I would look at the target size, where it is based, and in which industries you are looking to invest."

Francesco Perticarari: “Okay, so I didn't do as bad as I thought the first one”

Slide 3: Team

Maria Marques Nunes: "Here understanding relevant background experience and track record is key. On the track record part, you have ‘10 angel investments’. I don't know if you have more information afterwards, but it can be quite useful if we see some numbers, as in what was your returns, or logos, if it's investments in known companies”

Francesco Perticarari: “Okay, and you normally see the team slides coming early in the deck, later in the deck, or you don't care?”

Maria Marques Nunes: “It depends on the funds. If it's fund one, it usually comes at the beginning because there's no portfolio yet. For us, in a fund one, the most important thing is the team. Alongside understanding the track records.

Slide 4: Team

Maria Marques Nunes: "Having a good support team, even if they are not as involved, like the venture partners here, can be important. Its importance ultimately depends on the context and relevance. If they have experience in a specific technology, like here you have in physics and computer science, LPs can understand that you can ask them for advice. Likewise, if they have experience in VC. For a fund one, it can be good to have people that can guide them in their chosen VC ecosystem. So these two ones you have here are important."

Slide 5: The Opportunity

Maria Marques Nunes: "The community aspect is interesting, but again if you have any numbers to validate specific investments, that can help. For the non-investment companies or the broader context, this can be ok as a slide because ultimately we would then expect the GP to explain these sorts of details to us if we progressed further."

Slide 6: The Opportunity

Maria Marques Nunes: "What we usually look for in these sort of ‘context’ slides is the rationale behind the fund thesis, where and at which stage the fund will invest, and if they have the actual knowledge to execute. Was the background operational, VC, entrepreneurial, or scientific?

We must understand the fund strategy and then the opportunity around this. What are the specific characteristics of the fund and is the team able to execute? Mostly we want to see what they've built before. As long as we see “something” interesting we can then ask about the details in the first call to understand better."

Francesco Perticarari: “Okay, so in general for emerging managers who have a hybrid record like mine, where it's not all about investments but also operational, are there any other indicators that you would value as a way to showcase past achievements? I mean, other than investment track record numbers?”

Maria Marques Nunes: “It depends, for example, if you have studies like a PhD or have worked with universities or corporates, or you helped build companies, or you were the first employee of some startups, or you founded one. It all counts if it supports your thesis and strategy. When we are looking for emerging managers, we always ask ourselves: ‘What is their past experience? Worked in a VC and is familiar with the ecosystem, has a scientific and technical background, was a serial entrepreneur or a mix?’”

Francesco Perticarari: “Makes sense. In my case specifically, I used the growth in company value from pitch winners from the community to showcase the value of the community from before I started investing.”

Maria Marques Nunes: “I see. I think it’s okay but you could probably make it easier to understand and differentiate between investments and non-investments.”

Slide 7: Community Track Record

Maria Marques Nunes: "Here I see more of the same, as in not investments done but investments you could have done if the community had had a fund, correct?”

Francesco Perticarari: “Yes, correct”

Maria Marques Nunes: "I think I'd consider bringing the investments forward. Because when you are sending the deck to pitch to investors, the first slides are the ones that they look at the most and not the last ones. So I would make sure those slides come a bit earlier in the deck”

Slide 8: Key Advantages

Maria Marques Nunes: "Here you are saying that through the community you have built you get your unique dealflow. I would be more specific on this either specifying the value it would have brought to the fund or they ways you source deals in detail"

Slide 9: Ecosystem Value Add

Maria Marques Nunes: I would potentially consider merging some of these slides and reducing the information"

Slide 10: Investment Track Record

Maria Marques Nunes: "Finally we get some investment numbers! Here I would put the return on these"

Francesco Perticarari: “On this one, we actually had the returns in a previous version, but because one of the three raised the follow-on round with convertibles and one was a small pre-seed to seed increase, we were suggested to focus on other metrics, like the ones you see here.“

Maria Marques Nunes: "In that case, you could add well-known co-investors that participated in the rounds. In the VC world having a good network is important. And seeing that you can have relevant co-investors and follow-on investors can be a useful alternative indication you can use to showcase your investments”.

Slide 11: Warehouse Investment

Maria Marques Nunes: "This is a good one. Since it’s showing a company in the actual portfolio but I would also add the co-investors. If one of our portfolio funds is invested, it would be a way for us to validate that you invested in good companies. Or if you add as a co-investor someone we know and trust, it's a way to understand that you're picking a good company."

Slide 12: Funding Partners

Maria Marques Nunes: "I’m assuming this is a potential LP you showcase here. We usually do not see this in decks, and it may be a problem if they then don’t follow up into the first closing, but it's something that we ask for anyway afterwards. If you felt it was helpful when you did your first closing, I can see it as a positive”

Francesco Perticarari: “Yeah, we added this slide after Molten Ventures crystallised their commitment. Also to showcase who could be like a potential follow on investor for our deals”

Slide 13: Target Market

Maria Marques Nunes: "The market slide. You dive deeper into what you invest in and why. Here I would be curious about the knowledge needed to invest in this type of markets and industries. Because here you give some examples of industries you will target. In a way, I can see that your background and the venture partners experience can contribute to required knowledge to invest in these industries. However, I would then ask in a first call: ‘how do you have the right knowledge to pick companies in each vertical’."

Slide 14: Operations

Maria Marques Nunes: "In its current form I don't think this slide is necessary in the main deck. Maybe more for the appendix. You are showing your investment process… But I would be more curious about how you get deal flow, and where you look when it comes to sourcing companies. Is it just from your community, from universities, or from other VCs? Also, how do you then think about shaping the investment round? Do you help bring co-investors in? I think these are more important aspects than your back-office processes."

Slide 15: Allocation

Maria Marques Nunes: "It’s a good slide. I may just put the target size of the fund once again. LPs can do the maths of course, but it could help if you are glancing through to see if all adds up.”

Francesco Perticarari: “In reality, this slide can change a bit depending on the market response and how much money the fund ends closing in total, but we wanted to indicate the size and number of cheques we will be writing”

Maria Marques Nunes: “Sure. I agree. I would also put these numbers in.”

Slide 16: Competitive Landscape

Maria Marques Nunes: "Your positioning within the ecosystem. It’s interesting but I think this could also be potentially appendix material. It may be more relevant if you can also show some examples of connections, such as deals introduced to you or that you introduced to others and got done. So that we can see some glimpses of how you are connected within the ecosystem. I get that for what I see here you try to differentiate yourself from the generalist investors and funds investing at different stages."

Francesco Perticarari: “I wish I had done this call back before! Even if the deck changed a bit from this version since we’ve done the first closing, I can still see there are ways to improve it by seeing it more from an LP perspective”

Slide 17: Economics (Terms)

Maria Marques Nunes: "Yeah, that's this! This is super important, especially if it passes the first review. If the investment thesis is aligned with what the LPs are looking for, then this information is something we will look at. Some funds do not have this slide and I don't understand why. We want to understand management fees, the total target, the investment period, if there is a catch-up or a hurdle rate, and the carry structure. It’s fundamental to have this slide. Good job."

Slide 18 & 19: Thank You! + Disclaimer

No comment. We have contacts again and a Calendly link to book a call, should the LP want to use that.

The disclaimer is a legal requirement. We drafted it ourselves based on templates we found online, then we adjusted it during the process of becoming regulated as a small alternative fund manager in the UK.

Slide 20: Appendix

Francesco Perticarari: “That was the main deck. Afterwards, there are a few appendix slides. We added some to showcase more details on a case study deal and others to explain the fund workings, especially to less sophisticated LPs like angel investors that don’t regularly invest in funds. Since they are a major part of our fund 1 LP base.”

Maria Marques Nunes: “Yeah, okay”.

Slides 21 & 22: Liquidity + Projected Returns

Maria Marques Nunes: “The ‘Projected Returns’ one is interesting. Almost none of the decks I’ve seen have this.”

Francesco Perticarari: “ Do you think it could be useful?”

Maria Marques Nunes: “Yes, it's useful. Because it shows that you have a structure in your head and it summarises the fund model. It's good that you have these 3 scenarios computed here. It's not really necessary for a pitch deck as we would want to dig deeper into the fund model as an institutional LP but it’s useful information if you want to add a high-level summary this way”

Slides 23, 24 & 25: Case Study

We picked our pre-fund SPV Ori as a case study company to showcase what the deal was, why we invested, and how we helped the company with intros to relevant third parties in our network

Maria Marques Nunes: “It’s okay as appendix material. As a manager, you can put how and why you chose a company or what was the diligence process. It can be good info to give a general idea. When it comes to institutional ones like us, we would at some point want to see some examples of investment memos, check some references and talk to you to understand the reasoning behind actual investments. So be also prepared for that kind of material in a data room, if you pitch institutional LPs. But of course that comes potentially after the deck and the initial calls.”

Slide 26: Why Nots

Maria Marques Nunes: "These are companies you turned down, along with the reasoning behind the decision. Again I think it’s positive but definitely appendix material if you feel like adding it."

Slide 27, 28 & 29: Fund Structure + Our Values and Principles + LP Investor Onboarding

Maria Marques Nunes: "I see you have here the fund structure, some kind of personal value statement and a process for LP onboarding. It's not fundamental to have this information. Ultimately when we look into a deck, the first thing is just to see if it fits with our thesis. Like if it's a fund that can align with us and with our portfolio strategy. And then if the team has some evidence of a track record and demonstrated capacity to deliver on their thesis. After that, we like to dig deeper in our analysis with the managers, if we feel there is a fit. You can have extra bits of information like these slides in the appendix."

Deck Review Wrap up

Francesco Perticarari: "I guess we went through the whole thing. Any comments on how it's structured or on how it ranks compared to other decks you’ve seen?”

Maria Marques Nunes: “I think it's a good deck. Maybe you could leverage your experience more. Since you have a scientific background, it could better show how that relates to choosing the best-performing companies and how you then source any missing bits of knowledge to get conviction in your chosen verticals. Also, I would showcase better the VC ecosystem and network that you've built so far. But besides that, I think it's a good deck and it has the main information presented in a clear, simple format with neutral, easy-to-read fonts and colours, which sometimes is what many decks lack. Some don’t even have the target size.”

Francesco Perticarari: “Okay. And since we will open this up for other emerging managers to see, what are the top bits of information that LPs like you want to see in a fund 1 deck?”

Maria Marques Nunes: “Having the main information in the first slides and then at the end the terms is important. In terms of the top priorities for us, it’s critical to understand: 1) who is the team and their experience and track records; 2) the thesis, where and why the fund will invest; and then 3) the strategy of the fund and what are the unique features that set them apart from other funds.

Also: don't be afraid to put the numbers in. Because then when we speak to the GP we already know some examples of successful track record investments or other achievements, and we know roughly what the fund target size is and what is their thinking around allocation, follow on if any, and portfolio construction. Of course, we know that the market changes but it’s important to understand what kind of fund people are building. Some indication of timing can be useful too, such as the target for the next closing, although you could add that in the email accompanying the deck.”

Francesco Perticarari: “Great, thank you so much, Maria!”

Conclusion

Ultimately, a deck is just like a CV. It should be in constant evolution and its real goal is just to get you that interview. After that, if you get to make a good impression, it helps the decision-makers carry on with relationship-building meetings and perhaps get to an investment decision.

An effective pitch deck is all about showcasing your team's background and investment thesis and providing the core details on how the fund will operate.

Other key elements LPs will be attracted to are any evidence of your connections in the VC ecosystem and clear numbers and timelines that will make the pitch deck stand out and resonate with potential investors.

It’s also a filter for those who don’t see eye to eye with you. And a great way to get feedback too!

Sometimes helping you get a fast “no” is just as important to help you focus on those LPs that are truly aligned with your investment vision.

For us to get interest for our first closing it was important to focus on telling our story, align it with the brand and try not to make it too long or convoluted. If the key numbers and the right narrative are there, you should be already quite ahead of the game.

The other part of launching a VC is a mixture of patience and a very thick skin to deal with rejection. It can take an awfully long time and a tremendous effort to launch a fund and a deck is ultimately just one element of the whole process. But it’s a useful piece of documentation to have and to constantly keep up to date for future LPs.

Happy raising! 💪

Want to see the first closing deck as google slides? If it helps, go for it. You can check it out here: bit.ly/srv_firstclosingdeck

Europe boasts impressive resources and human capital. Its academic talent is second to none, with the largest global share of highly cited research publications (43%) and a higher percentage of STEM graduates (22-35%) than the US (18%).

That said, Europe hasn’t been able to produce deeptech powerhouse companies like Palantir and SpaceX. That’s because Europe’s deeptech market remains fragmented, a critical problem for security, defence and supply chain sovereignty.

Addressing this fragmentation — and recognising the importance of cooperation and unity — is key to cementing Europe’s place as a third global deeptech powerhouse.

Understanding Europe’s fragmentation

Europe’s deeptech market suffers from fragmentation thanks to a range of factors, including regulation, historical differences and individual national agendas.

While European governments have started to take action, such pledges from the British, French and German governments to invest in quantum technology, these efforts often result in short-sighted competition between nations rather than a unified large-scale effort.

The US and China have the advantage of the sheer economic size, as well as being a single market.

Venture capital has already given the US global leadership in tech. Last year, a member of the US president’s Intelligence Advisory Board made it clear just how much it’d be focusing on deeptech as a matter of national security: “We have to be very serious about how these technologies get built… Not simply thinking that technology’s neutral — because the technology’s not neutral.”

The US’s ambitions build upon initiatives such as the CHIPS Act, which aims to accelerate the creation of a domestic semiconductor industry, and have been mirrored by China’s prioritisation of deeptech in R&D efforts.

Collaboration is possible — simply build on past successes

But saying that Europe cannot build a united deeptech ecosystem and strategy because of national divisions is no excuse. The region has demonstrated the ability to collaborate successfully in the past. Just take the example of prestigious research institutions like the European Organisation for Nuclear Research, CERN, which has drawn on pan-European talent and been able to attract the best and brightest from abroad.

“The fact that NATO had to create this fund also highlights the lack of a truly united and independent, VC-rooted effort to support deeptech in Europe”

Recent initiatives also indicate a willingness to collaborate in deeptech, such as the NATO fund. Launched to support deeptech research and development, particularly in defence, the fund has commitments from 22 countries. The fact that NATO had to create this fund also highlights the lack of a truly united and independent VC-rooted effort to support European deeptech. The fund is still hiring its senior management team; time will be the judge of its true impact.

Another positive development is the emergence of pan-European VC investment firms that don’t focus on just one nation or group of nations, though these are still few and far between.

Solutions to overcoming fragmentation

The question we should ask ourselves, though, is whether we want to accept that many deeptech startups will ultimately outgrow Europe and eye the US or NATO as their main scaleup partner on the world stage — or if our national governments can find the willingness to ignore historical tensions and develop a true pan-European deeptech strategy that supports these critical technology companies beyond their native borders.

From a financial perspective, especially for deeptech seed investors like my VC firm, it may not matter too much. The trend is clearly pointing to Europe being able to create successful deeptech companies. But as Europeans, the question of technology sovereignty should go beyond financial returns and include how we see our role on the de-globalising world’s stage.

“Europe needs to streamline its tangled web of regulations and priorities”

With that said, there are some solutions that Europe can start working on now.

First, encouraging pan-European investment firms can facilitate cooperation among nations by pooling resources and fostering cross-border collaboration. This is going to be challenging if most European funds are backed by the EIF, often with the addition of national government banks (if in the EU) or the British Business Bank (if British), and these LPs force their investees to deploy primarily in a given country or region.

Second, Europe must embrace a shared vision based on a risk-taking mindset, because the US and China have already recognised the strategic value of deeptech and doubled down on their investments. This goes back to finding answers to whether investors and governments should resign themselves to European companies going to the US or trying to keep their headquarters here.

Third, Europe needs to streamline its tangled web of regulations and priorities. To give just a few examples of how broken the system is, the European Space Agency did not actually replace national agencies and each country is building an independent quantum programme. These overlaps create waste and even internal competition.

When it comes to regulations, there is also no unified strategy — Italy even decided to unilaterally ban access to ChatGPT without notice! Brexit didn’t help either, since the UK is the largest VC ecosystem in Europe but alone it still pales in contrast with the US or China.

Europe’s path to deeptech dominance

Europe has the potential to be a formidable player in the global deeptech race, but overcoming fragmentation is crucial for realising this potential. By fostering collaboration and adopting innovative solutions like pan-European investment funds and supra-national defence initiatives, Europe can unite its resources to have a true shot at challenging the dominance of the US and China in critical areas like deeptech.

Francesco Perticarari is general partner at Silicon Roundabout Ventures.

This article was first published on Sifted.eu on Apr 26th 2022

Axiom.ai, a browser automation startup, is making waves in the no-code space by enabling users to build and use browser bots for automating website actions and repetitive tasks without writing a single line of code.

With no-code capabilities and an upcoming AI integration, Axiom is poised to revolutionise the browser automation space and make Robotic Process Automation (RPA) accessible to the masses.

In this blog, we'll explore Axiom.ai's features, its potential impact on the future of work, and how it compares to its competitors.

Axiom.ai: Browser Automation Made Easy

Axiom.ai is a software platform that allows users to automate website interactions, web scraping, data entry, and spreadsheet automation, among other tasks. The software is available as a Chrome extension, and through Axiom.ai, users can create custom "bots" to automate online tasks without any coding knowledge. These can then be run manually, or via automated triggers by connecting Axiom.ai to services like Zapier or Make (formerly called Integromat), or even via custom webhooks.

One testament to their success is their ranking 1st on Google for the search term "automate AI." As well as being the most used browser automation app on the Chrome Web Store (ranking 1st for “browser automation”). This demonstrates the increasing awareness and recognition of Axiom's browser automation capabilities and they're being perfectly positioned to capitalise on their recent AI capabilities, which will launch very soon!

The growth of Axiom.ai can be linked to the increasing popularity of no-code tools, as evidenced by the rising Google Trends search related to no-code. Users with experience in automation and no-code tools, such as Airtable and Zapier, are quickly adopting Axiom.ai as a go-to solution for browser automation.

I’ve actually been using Axiom.ai myself for a variety of tasks in the last couple of years. From helping me scrape data out of websites, to parsing emails and even helping me programmatically interface with a web service that did not have an API. And both the capabilities of its engine and its UX are great and continue to improve month after month!

(Screenshot from one of my demo accounts)

What is Axiom.ai?

Axiom.ai is a cutting-edge browser automation platform that enables users to build and use browser bots for automating website actions and repetitive tasks on any website or web app. Designed with a no-code approach, Axiom makes it easy for individuals and businesses to save time and increase productivity by automating various tasks without the need for programming skills.

Accessible as a Chrome extension, Axiom can run in the cloud, simulating a browser 24/7, providing users with continuous automation capabilities. Some of the key features of Axiom.ai include:

• Visual Web Scraping

• Data Entry

• Spreadsheet Automation

• Automate any website interaction

• Build custom bots, with no-code

• Connect to Zapier, Make (formerly called Integromat), or Webhooks

Axiom.ai is built on the web automation framework Puppeteer, and its easy-to-use interface allows users to automate actions like clicking and typing on any website. With a wide range of customisable bots and templates, Axiom offers users the flexibility to create tailored solutions for their specific needs. The platform also integrates with Zapier, Make, and custom webhooks, enabling users to trigger bots based on external events or set schedules for their automation tasks.

As a comprehensive browser automation tool, Axiom.ai is revolutionising the way businesses and individuals approach automation, making it more accessible and user-friendly than ever before.

AI Capabilities Coming Soon

Axiom's AI capabilities are expected to launch in April 2023, offering features such as:

• Guiding users through bot creation

• Restructuring data from a webpage

• Auto-generating bots for users

These new features will help make RPA more accessible to a wider audience, without requiring coding expertise. Axiom's vision is to enable anyone to automate tasks in their work environment, particularly in office settings where repetitive tasks are commonplace.

I actually got a sneak peek at these by sitting down with the CEO, Yaseer, and these features look truly transformative.

Impact on the Future of Work: Axiom vs. Zapier

As a no-code platform, Axiom.ai makes robotic process automation (RPA) accessible to a broader audience, similar to how Zapier helped users leverage APIs without coding. The integration of AI capabilities with Axiom will further lower the barrier to accessing process automation, making it easier for users to design and execute task automation with little to no human supervision.

While Zapier connects systems with APIs, Axiom excels in automating tasks where APIs are not available. This allows users to interact with data, forms, and processes on web pages or web-based cloud platform interfaces. Startups have successfully built their products on top of Axiom when APIs were unavailable, such as tax filing automation and data-driven startups.

Competition and Differentiation

Axiom's main competitors when it comes to browser automation include Phantom Buster (social automation focused), Magical (Extract, Transform, Load or ETL), and iMacros, a now discontinued pioneer of browser automation. Axiom differentiates itself as an end-to-end RPA automation platform on the browser. A platform that’s entirely no-code but also capable of handling complex processes across different websites and platforms.

Axiom.ai in the RPA and Automation Market: A Game-Changer

The RPA and automation market is booming, with Grand View Research projecting it to reach USD 25.56 billion by 2027, at a CAGR of 40.6% from 2020 to 2027. In this dynamic landscape, Axiom.ai is making waves by tapping into three key trends:

1. No-code revolution: Axiom.ai democratises browser automation, empowering users to build bots without coding expertise.

2. AI and machine learning: Integrating AI capabilities will make Axiom.ai even smarter and more adaptable in automating web tasks.

3. Remote work and digital transformation: Axiom.ai's cloud-based solution is perfect for remote workforces, enhancing productivity through automation.

Axiom.ai is poised to disrupt the RPA market, making automation accessible to everyone and shaping the future of work.

Use Cases and Applications: Unlocking the Power of Axiom.ai

Axiom.ai's versatile and user-friendly platform opens up a world of possibilities for businesses and individuals alike. Here are some of the coolest use cases and applications that showcase the true potential of Axiom.ai:

1. Streamlined Data Entry and Extraction

Axiom.ai can automate data entry tasks, such as filling out online forms for tax filings or e-commerce orders. It can also extract data from various sources for analysis or integration into other systems. Examples include:

• Web scraping for market research or competitor analysis

• Aggregating data from multiple sources for reporting

• Extracting contact information for lead generation

2. E-commerce and Online Store Management

Online sellers can leverage Axiom.ai to automate tasks on platforms like Amazon Seller Central, Shopify, or Etsy. Examples include:

• Managing inventory and orders

• Updating product listings

• Automating customer communication

3. Sales and Business Development Automation

Axiom.ai can help streamline lead generation and prospect outreach by automating tasks across platforms such as LinkedIn, Twitter, and Apollo.io. Examples include:

• Scraping contact information from social media profiles

• Automating connection requests and follow-ups

• Scheduling and sending personalised messages

4. Social Media Management and Monitoring

Axiom.ai can assist in automating social media tasks, from posting and scheduling content to monitoring and engaging with followers on platforms like TikTok, Instagram, or Facebook. Examples include:

• Scheduling and publishing posts across multiple platforms

• Automatically liking or commenting on relevant content

• Gathering insights from social media analytics

5. Automating Data-Driven Startups

Startups that rely on collecting and sourcing data can use Axiom.ai as part of their tech infrastructure. Axiom's bots can be triggered programmatically via APIs, making it a perfect fit for data-driven businesses. Examples include:

• Gathering datasets from different sources for analysis

• Automating data collection for machine learning models

• Integrating with custom applications for real-time data processing

6. Seamless Integration with No-Code Tools

Axiom.ai works in harmony with popular no-code tools like Airtable and Zapier, bridging the gap between website interactions and other cloud-based services. This enables users to create end-to-end automated workflows that save time and reduce manual effort.

With Axiom.ai's ever-growing list of use cases and applications, businesses and individuals can transform their daily tasks and uncover new opportunities for growth and efficiency. The power of automation is now at everyone's fingertips, no coding skills required.

Why I Went from Early Adopter to Investor

Through running my tech community, Silicon Roundabout, I had the opportunity to invest in Axiom.ai during their seed round in 2021. I did so as an SPV that preceded the launch of our community fund: Silicon Roundabout Ventures. And I could not be happier about doing so.

At the time, the platform was just an MVP, but it already showed exciting potential. And its founders, Yaseer, Alex, and Simon, had both the right skills to build the future of automation and a crystal-clear vision of where the market was going and what was needed to ultimately unlock their startup vision.

Since then, the company has experienced remarkable user growth, reflecting the strong demand for their innovative solution in the market.

Their tech development has been relentless, and their SEO strategy, combined with laser-focused customer profiling, helped them build a strong positioning in the market and spur organic growth and adoption. This demonstrates the increasing awareness and recognition of Axiom's browser automation capabilities.

Plus! Axiom becoming the #1 plugin on the Chrome Web Store for browser automation highlights the growing popularity of their Chrome extension among users who seek efficient and user-friendly automation tools.

The strong growth in organic demand and high rankings on both Google and the Chrome Web Store indicate that Axiom.ai is quickly becoming a key player in the browser automation and no-code space, positioning itself as a leader in democratising access to powerful automation tools for businesses and individuals alike.

Axiom's Mission and Vision

Axiom aims to make process automation, once the domain of specialist software development teams, accessible to individuals without coding skills. With AI integration, Axiom is building a platform where users can easily create and customise automations with guidance from the AI itself, opening up automation possibilities for any office worker.

As the Axiom.ai capabilities increase, its platform continues its march towards becoming the leading browser automation platform: by making RPA accessible to a wider audience and by compounding its leadership in the browser automation space.

As more people adopt no-code tools and embrace automation, Axiom is ultimately one to watch to understand what the future of work will look like.

Originally published on Sifted.eu and written by @NicolSchwarzK on 02/02/2023

London-based deeptech VC Silicon Roundabout Ventures is announcing the first close of a pre-seed and seed fund run by one of the founders of UK tech meetup community Silicon Roundabout, engineer Francesco Perticarari. The fund hasn’t disclosed how much it has raised so far but says it’s targeting £10m.

Silicon Roundabout Ventures is looking to address the lack of specialist deeptech VCs with a technical background in Europe — 96% of investors in the UK have never worked in a technical role — which Perticarari says means that potentially groundbreaking tech is often missed.

Founders and investors in Europe say there still isn’t enough money for early-stage companies — let alone technical deeptech companies — in Europe.

So can Silicon Roundabout Ventures help to remedy the situation?

Where will the money be spent?

Silicon Roundabout Ventures will invest between £100k-300k in about 25 deeptech companies.

Perticarari — who is part of a growing cohort of solo GPs in Europe — tells Sifted he’s particularly interested in engineer-led startups working on tech in segments like:

• Future of computing and data processing

• Climate tech and healthtech-focused deeptech

• Security

So far it’s backed two startups, including Anaphite — contributing £250k to its £4.1m seed round last August.

Where’s the money coming from?

The fund is backed by London-based VC firm Molten Ventures, alongside a number of angel investors.

The early-stage deeptech market

While late stage deeptech funding has been on the rise in Europe over the past few years, the money hasn’t trickled down to pre-seed and seed-stage deeptech startups — which saw a third of the number of deals in 2022 than they did four years earlier, according to Dealroom.

But there are signs more investors are looking to take advantage of the gap in the market. In 2022, eight new first-time deeptech-focused funds were raised in Europe, according to Sifted’s count, with the majority of them exclusively backing early-stage startups.

Originally published on Forbes by Renita Kalhorn

“With the release of ChatGPT, Northvolt becoming one of the most valued European unicorns, and SpaceX winning contracts with their Starlink satellite network, we finally have examples proving the power of deep tech at scale. What seemed like crazy moonshots are now bringing to market transformative technologies and new infrastructures — as well as, crucially, financial returns for early backers."

These are the words of Francesco Perticarari, founder of Silicon Roundabout Ventures, a VC fund leveraging a community of 15,000 founders and engineers to back deep tech and big data startups.

We met in London at the inaugural Frontier Deep Tech Conference, founded by investor Cristina Esteban to bring together players from across the deep tech ecosystem.

In his keynote, Francesco declared that now is the moment to invest in deep tech – the engineering and scientific innovations that will revolutionize our world.

I spoke with him afterwards to learn more.

Renita: For many VCs, the barrier to investing is that they don’t know how to evaluate the potential of transformative technologies. How did you make the shift from computer scientist to deep tech investor?

Francesco: While working full-time as a developer, I was getting excited about advancements in computing, from AI to Quantum Computing, as a radical alternative to the way current processors work. Parallel to that, I started building Silicon Roundabout as a deep tech community of scientists and founders. That led me to start angel investing and eventually to launch an angel fund.

Renita: So where was the inflection point in terms of going all in on deep tech?

Francesco: At a certain point, I felt I couldn't keep the deep tech community active, be an angel investor and fulfill my responsibilities as a full-time developer. And when push came to shove, I didn't want to lose the community and I definitely wanted to bring more capital to deep tech. So I realized I was gonna have to quit my job.

Renita: Wow, that took real conviction.

Francesco: Yes, I genuinely believe I'm backing future pioneers and I want to be part of their story. We also need diverse approaches when investing in deep tech.

Renita: Definitely, can you elaborate?

Francesco: Well, as an asset class, venture capital is driven by "power law" returns, by the outliers. That means you've got to explore niche opportunities. And the best way to do this is to scout for unusual combinations of people and technological insights who have the technical expertise to challenge the way things have been done.

Renita: Right, so much of VC is about pattern recognition. If you're in your own little box, it’s difficult to see things differently.

Francesco: Exactly. So for instance, the last investment I made was in Anaphite, where one founder has a physics background and the other a chemistry background. Working together, they had a breakthrough with graphene. They started experimenting with different applications and one of them happened to be batteries. Now they’re building a company to enhance Li-ion batteries with graphene, which has the potential to enable electric vehicles to fast-charge in five minutes – at lower cost and at scale – within this decade.

This unusual combination of physics and chemistry led them to a breakthrough, and now, they've convinced me and other investors to give them money so they can take it outside the lab.

Renita: So they didn't start by saying, “We want to build better batteries.” They had the technological breakthrough first and then asked, "What can we do with this discovery?"

Francesco: Exactly. And, what’s important is, they looked at various potential applications to find an answer before looking to scale. When results were promising with batteries and there was also a compelling problem it could solve in the automotive sector, it was the right mix of a big problem and breakthrough technology.

Renita: And a great example of the power of cross-pollination in deep tech.

Francesco: Yes, not only do deep tech innovators have an edge because they can patent specific breakthroughs, but having a combination of different perspectives is in itself a competitive asset.

Renita: So with software, there isn’t as much potential now for breakthrough innovation?

Francesco: Well, there are areas where new types of algorithms could be created that operate better than what a traditional software company could create.

Roseman Labs, the cybersecurity startup that won the competition at the Frontier conference where we met is a good example. They're developing multi-party computation so that organizations can combine their data sets without revealing sensitive input data. The solution they need is not available out of the box.

Renita: Last question: Deep tech solutions are complex and require integration of multiple technologies — no one company will be able to create a complete solution on its own. Being able to collaborate with other players in the ecosystem is critical to the speed of development and success of deep tech start-ups.

How do you think about building community in deep tech?

Francesco: If we want deep tech to be more scalable and have a huge impact then we have to have a network of investors, founders and other entities – hubs creating a compound effect where investors specialize in different aspects, investing with each other, creating healthy competition. where startups could become customers of each other.

Increasingly, companies and investors are realizing there is more to gain by foregoing short-term profit for long-term partnerships and supporting each other to create a huge exit.

If Deep Mind were a startup today, they might not have been so keen on being acquired by Google if there had been the network potential to grow.

First, let's define what an iframe is. An iframe (short for "inline frame") is an HTML element that allows you to embed one HTML document within another. Essentially, it's a window within a window, and it's a great way to add extra functionality to your website without cluttering up your main page.

Now, let's talk about how to inject code or prefill fields in an iframe. There are a few different ways to do this, but we'll focus on two of the most popular methods: JavaScript and HTML attributes.

Method 1: JavaScript

The first method is to use JavaScript to inject code into an iframe. This is a great option if you need to add some dynamic functionality to your iframe, such as a form validation script. To do this, you'll need to use the contentWindow property of the iframe element to access the iframe's document object. Once you have access to the document object, you can use the innerHTML property to add your code. Here's an example:

<iframe id="myiframe" src="https://example.com"></iframe>
<script>
  var iframe = document.getElementById("myiframe");
  var doc = iframe.contentWindow.document;
  doc.innerHTML = "<p>Hello, World!</p>";
</script>

Method 1b: Embedded Script Editing

In some cases, you might have received a pre-compiled embed script code for an iframe from a service you want to add to your website, an Eventbrite ticket page.

In the latter case, this is what Eventbrite gives you:

<div id="eventbrite-widget-container-YOUREVENTID"></div>

<script src="https://www.eventbrite.co.uk/static/widgets/eb_widgets.js"></script>

<script type="text/javascript">

    var exampleCallback = function() {
        console.log('Order complete!');
    };

    window.EBWidgets.createWidget({
        // Required
        widgetType: 'checkout',
        eventId: 'YOUREVENTID',
        iframeContainerId: 'eventbrite-widget-container-YOUREVENTID',
        // Optional
        iframeContainerHeight: 425,  // Widget height in pixels. Defaults to a minimum of 425px if not provided
        onOrderComplete: exampleCallback  // Method called when an order has successfully completed
    });
</script>

As you can see the original service provider, like in the second option, may have a script available online that it runs from a url. In this case it also already modifies it locally, so we may take advantage of that second script to see if we can inject parameters directly in there.

You can copy and paste the script url in your browser to check the source code. In this case: https://www.eventbrite.co.uk/static/widgets/eb_widgets.js

Let's say we wanted to prefill our embedded checkout with a promotional code. Even though the unformatted javascript code appears jibberish at first, we can search the text for words like "property" or even more specific ones, like "promo" or "promocode" to see if the script defines these form elements.

We stroke luck!

Since we identified the right parameter in the script generating the widget within the iframe, let's go and try to add it directly into the suggested creation method:

<script type="text/javascript">

    var exampleCallback = function() {
        console.log('Order complete!');
    };

    window.EBWidgets.createWidget({
        // Required
        widgetType: 'checkout',
        eventId: 'YOUREVENTID',
        promoCode: 'earlybird',
        iframeContainerId: 'eventbrite-widget-container-YOUREVENTID',
        // Optional
        iframeContainerHeight: 425,  // Widget height in pixels. Defaults to a minimum of 425px if not provided
        onOrderComplete: exampleCallback  // Method called when an order has successfully completed
    });
</script>

Success! Now our eventbrite embedded checkout widget comes pre-filled with a promotional code of our choice.

Method 2: HTML attributes

The second method is to use HTML attributes to prefill fields in an iframe. This is a great option if you want to prefill fields in a form, such as a login form. To do this, you'll need to use the name attribute of the input elements to identify the fields you want to prefill. Here's an example:

<iframe src="https://example.com/login">
  <form>
    <label>Username:</label>
    <input type="text" name="username" value="JohnDoe">
    <label>Password:</label>
    <input type="password" name="password" value="password123">
    <input type="submit">
  </form>
</iframe>

It's important to note that for the second method, it's a bit different as you need to include the entire HTML code inside the iframe, it's not only injecting code to the iframe but also creating it as well.

In conclusion, injecting code or prefilling fields in an iframe is definitely possible, and it's a great way to add extra functionality to your website or streamline the user experience. Whether you choose to use JavaScript or HTML attributes, the process is fairly straightforward and can be done in just a few lines of code.

Note: This article was originally published on UKTN by George Simister with contributions from UK founders and investors including myself

From quantum computing to semiconductors, the UK has a strong deep tech sector. But what will 2023 hold for the deep tech industry amid a looming recession and more cautious investors?

Deep tech is a broad subcategory that includes everything from AI to cleantech. According to Tech Nation, UK deep tech investment has increased 33x since 2011, topping $8.5bn in 2021. Deep tech companies are usually more research-intensive and tend to have a longer time to get to market – but they have the potential to create truly revolutionising technologies.

Here’s what five industry experts predict for UK deep tech in 2023.

More funding for deep tech startups in 2023

“With the release of ChatGPT, Northvolt becoming one of the most valued European unicorns, and SpaceX making headlines and winning contracts with their Starlink satellite network, we finally have early examples proving the power of deep tech at scale.

“What seemed like crazy moonshot projects are now bringing to market transformative technologies and new infrastructures, as well as, crucially, financial returns for early backers.

“We now need to scale funding opportunities for the deep tech entrepreneurs and 2023 can be a great year for that. More investors will need to join us early deep tech backers, but at least now we have examples to point at for others to see.”

– Francesco Perticarari, deep tech angel and founder of Silicon Roundabout

Compliance, geo-political developments and economic stress

“Next year, deep tech trends will be defined by three key challenges: compliance and regulation, geo-political developments and economic stress.

“The rapidly changing regulatory landscape is placing pressure on owners of operational technology (OT) to better plan, prepare and build defences to strengthen their resilience against cyber-attacks. Critical technologies such as dynamic physical network segmentation (DPNS) use an emerging field of innovative hardware air gaps, making businesses a harder target to exploit. Such solutions are also becoming vital for operators of critical national infrastructure, in the face of espionage linked to geopolitical tensions.

“Finally, in light of the ongoing economic crisis, many OT owners are being forced to connect their digital assets to legacy networks in order to realise efficiencies. This will require the deep tech industry to react quickly to mitigate the risks involved in assets being physically connected to the internet when not in use.”

– Stephen Kines, COO of Goldilock

‘Alternative’ quantum computers

“The most striking feature of 2023 will be a ramp-up in innovation and possible disruption within the quantum computing market. New players will enter the market with alternative approaches towards quantum computing. The aim of these newcomers will not be to solely achieve universal computing, but rather more specific computing that can be delivered in a shorter timescale.

“2023 will see a comparison between public and privately owned quantum companies. Public companies will continue to put their capital to work but at the cost of the short-term attention of investors. While they and the rest of the industry push to meet meaningful technical milestones they will only have partial success in shrugging off the short-term pressures to validate the business.

“Geopolitics will continue to shape the quantum computing industry and this shaping could reach a fever pitch. As the race is on to develop quantum computers to gain a strategic lead in cybersecurity, intelligence operations and the economic industry, we should expect increasing restrictions between them to try and limit technological exchange.”

– Richard Murray, co-founder and CEO of ORCA Computing

Battery breakthroughs

“2023 is going to see new breakthroughs in the technologies that will decarbonise our world. Not only will there be an increasing focus on how we ensure our energy security without turning to carbon-intensive technologies, we will also see more attention paid to how we adapt our existing world through the transition.

“Battery technology is key to delivering on the promise of EVs, as well as playing an important part in the future of electrifying our homes and beyond. From new chemistries to the use of advanced materials like graphene to improve performance, there is a lot happening in this exciting space.

“The challenge of the decarbonising industry continues to be very pressing and 2023 will see further progress in delivering hydrogen to these sectors. This is likely to come from a combination of large-scale and point-of-use technologies, including unique systems that will be developed and deployed in the UK.”

– Rebecca Zeitlin, marketing director, Levidian

Long-awaited semiconductor strategy

“At a global level, there are going to continue to be challenges around geo-political tensions and supply chains, but here this is driving renewed focus on the UK’s semiconductor strategy. It is clearly on the government’s agenda and it will be critically important to deep tech startups like ours that the government announces its strategy as soon as possible.

“If we can get the focus and investment support right, I expect to see major opportunities for UK and EU deeptech companies – particularly those delivering a products or services that improve efficiency, sustainability and supply chain resilience.

“As a champion of photonics, I expect to see significant opportunities for Phlux in robotics (e.g LIDAR for factory automation and navigation), climate tech (e.g sensors for monitoring greenhouse gas emissions) and optical communication systems.

“Over the next 12 months, we plan to invest heavily in R&D and building our team to deliver new products into these markets.”

– Ben White, co-founder and CEO of Phlux Technology

US VC heavyweight Andreessen Horowitz’s first fund in 2009 was $300m, the first three Founders Fund vehicles by Peter Thiel oscillated between $200m and $250m, and even as recently as Q3 2022, the median US fund size was still barely $50m.

Yet $214m is how much Sequoia bet and lost in a single investment into the infamous crypto startup FTX. This single investment was as big as many of the earliest VC funds, and the Sequoia fund itself that backed FTX was over $8bn in size.

Sequoia described FTX (in a now-deleted article) as “a company that may very well end up creating the dominant all-in-one financial super-app of the future.” The gushing phrasing recalls what Softbank’s Masayoshi Son said about WeWork in 2017: “We are thrilled to support WeWork as they…unleash a new wave of productivity around the world.” Two years later, in 2019, the $100 Billion Vision Fund took a $4.6bn hit on that investment.

Is there some link between bigger funds — with more cash to splash — and these massive losses?

Capital concentrates into megafunds

It’s not just Sequoia and Softbank that have gone big. VCs of $1 billion or more have received 60% of the capital so far this year — compared to 34% in 2021. Newer, smaller funds, on the other hand, are being squeezed out, despite evidence they normally outperform.

Capital is becoming increasingly concentrated into a few hundred megafunds around the world, with the top 20 VC brands now managing over $200bn, which translates to 10% of total VC assets globally.

The ratio would likely be even more skewed if we counted the so-called “VC outsiders” — companies like Softbank, Tiger Global, Partner Fund Management, Toma Bravo and other private equity and hedge funds that have poured billions into the VC game recently.

And as megafunds grow fatter, they ultimately face the same challenge: how to spend that money and make a return. It’s time to do some maths…

Size matters

In VC size does matter because of (a) maths and (b) access.

Let’s first look at the maths. VC exits follow a power law distribution: 20% of a fund’s portfolio will normally account for over 90% of its returns. 25-50% will return nothing or close to that.

If you had a $100m fund and wanted to triple your money (a benchmark VCs usually aim for) each portfolio company would need to have a shot at returning the entire fund. For example, if you managed to defend a 10% stake in a $1bn exit, that company would net you $100m, which was your original fund. A $20bn outcome, like Figma, would get you $1bn even if your stake was only 5%.

And if you had an $8bn fund? In that case, a $1bn outcome only returns you 20% of your fund. And the $100m from a unicorn exit? A failure that does not even move the needle.

The second problem that comes with size is access.

Data from Dealroom.co suggests the most successful “unicorn hunters” — VCs backing billion-dollar startups — are the ones that first invest in these companies at the seed stage. But to access a seed round, or even a Series A, you need to be able to deploy capital in such stages and the size of the tickets you are writing must be meaningful to your fund.

Herein lies the access problem: seed rounds are usually only a few million dollars in size. The median Series A is about $10m in the US and even less in Europe. If your fund is $50m or even $100m, seed cheques are still meaningful. But if your fund is a billion-dollar megafund, those tickets are not worth your time.

Are megafunds killing VC?

All of this means that we are now in a time when, for large VC funds, a startup achieving a billion-dollar outcome is meaningless. To hit a 3-5x return, megafunds need startups that can:

1. take on hundreds of millions or even billions in investment, and

2. exit at north of $50bn dollars.

If you look at all public tech companies today, less than 50 have achieved that valuation.

So when you meet a founder that’s attracting global PR, is prepared to raise huge piles of cash, and to quote Sequoia’s deleted article again, is pitching you a “vision about the future of money itself—with a total addressable market of every person on the entire planet,” large investors are tempted. They are tempted to jump on the hype bandwagon and “make a Uber” out of it. (Uber’s market capitalisation, by the way, is just over $50bn.)

Masayoshi Son allegedly jumped on the wagon after only 28 minutes of meeting WeWork’s Adam Neumann.

Sequoia did with Sam Bankman-Fried despite the fact that the “fucker was playing League of Legends through the entire meeting”. The observation was an ironic foreshadowing of his disastrous, rules-don’t-apply attitude which led to the company’s collapse.

This mentality and the concentration of capital into only a handful of funds is creating an ever-growing funding gap between the founders whom these funds back and everyone else. What’s more, larger funds are creating a close network of hype in their quest to engineer the next $50bn+ Uber, which means they naturally end up investing in a lot of the same companies.

The slowdown in the tech market will only accelerate these forces.

This article was first published on Sifted.eu on Dec 16th 2022

Cyber attacks all over the world are intensifying and are predicted to keep growing as the world become increasingly more digital and interconnected.

According to the 2022 Cyber Readiness Report by insurance company Hiscox, 48% of companies reported a cyber attack in the last 12 months, up from 43% last year.

Meanwhile, according to Siemens research, we are heading towards over 50 billion physical objects being connected together on the internet.

Yet, cybersecurity teams find that defending their tech infrastructure against this relentless silent war is getting harder and harder. The cybersecurity talent gap grew by 26.2% with around 3.4 million unfilled jobs worldwide, according to according to the International Information System Security Certification Consortium or (ISC)².

What if technology could help multiply the efforts of a cyber team?

In February 2022 I hosted a big data and AI meetup to help showcase startups in the Silicon Roundabout tech community.

The pitch winner back then was the team made up of Chris, Simon and Léa. Their solution, Malizen, combines new data visualisation techniques and machine learning to help cybersecurity managers struggling with hiring, on one side, and cybersecurity analysts tackling both threats and false-positives detected by other software tools, on the other.

Malizen is a cybersecurity software startup focused on speeding up cybersecurity investigations and helping cybersecurity operations scale. They are a based around Rennes, France, have secured some early commercial wins and are combining a rare attention to both AI R&D and human-centred UX in the development of their product.

Based on the founder’s thesis at the French Institute for Research in Computer Science and Automation (INRIA) funded by the French military (DGA-MI), Malizen’s ZeroKit is an all-in-one platform for analysing, understanding, and sorting cyber-security threats efficiently. Its purpose is to enhance the output of cybersecurity professionals whilst minimising the workload of cybersecurity teams.

The software already got initial results with with both institutions and corporations, such as DGA-MI (i.e. the French version of the US DARPA) and Thales Group: a French multinational company building electrical systems and providing services for the aerospace, defense, transportation and security markets.

Based on customer feedback and the company’s own research, ZeroKit can today save around 2 hours of work time per analyst per day and speed up the process of dealing with both threats and false positives by 4 times.

After seeing their platform in action over several zoom calls and following their growth for several months, I've sat down with the CEO, Christopher Humphries, to ask him about who is the team behind Malizen and how the product works.

Here is what Chris has to say:

What is the problem you are solving? And for who?

Regardless of the size of the company, cybersecurity teams are either stretched thin or can't staff up fast enough. In both cases they're overwhelmed by work and need to go faster and/or bring in more people to help.

While initially we were focused only on investigations, today our mission includes related issues cyber teams face such as knowledge management, reporting, collaboration and onboarding new team members. We've used our platform as a foundation to help teams face all these other challenges.

How does the technology work?

We've fine tuned data science technologies for cybersecurity and combined them into an interactive investigation platform for cyber teams. Every component we add is used to overcome obstacles and accelerate the process.

For example, cyber teams need to sift through many different kinds of data from different sources. We start helping their work by centralising this spread-out heterogeneous data into a single knowledge graph.

This serves as the foundation for the rest of the platform, and the more it grows the more powerful it gets.

We started with automatic and interactive data visualisation, which helps teams avoid typing out queries (often in a custom query language) or configuring clunky dashboards to explore data.

We've designed the entire platform for real time collaboration, which nowadays is almost an expected feature. Case management and team decisions also use the graph for storage, which then provides most of the data for automatic reporting.

Our latest trick was to use the graph to power our copilot, a collection of machine learning algorithms which learn in the background from decisions and new data to help out when possible. The copilot can, for example, suggest the next step in an investigation and will soon be able to recognise effective investigation techniques to help the rest of the team out.

Who is the founding team and how did you get to launching this startup?

There's a sci-fi trope about things starting in research labs.

Our story started in a cybersecurity research lab in France. Tools for exploring logs were rare and far from intuitive. Others in defense agreed so much I was able to finance my entire PhD.

The end prototypes were so promising we decided to start Malizen rather than risk letting the tech go to waste.

It's in that team that I met my future co-founder and CTO Simon. We were both into sci-fi and he's a real cybersecurity and machine learning geek. Useful traits to start discussions and aim for the future. He's also a great guy who really cares about the human side of cybersecurity.

After spinning out Malizen, Cris and Simon were joined by Lea, the former Chief of Staff of startup Procsea, as COO.

Personally, what never fails to drive me is witnessing people getting a kick out of our software and that sense that we're giving them superpowers. There's a point where people new to the platform get it and things click into place. Within a few minutes they're in the zone, exploring data just like in the sci-fi films we were inspired by, but in real life.

We love getting to know our community founders: One fun fact about you?

I got into running a few years ago. It's great for disconnecting and it helps me get my thoughts in the right place. I set myself the challenge of running a half-marathon in every new country I visit.

Not always easy to plan!

But I always travel with running shoes, and that's led me to run in places like India, Andorra, Switzerland… Last year I was at Slush, and it was my first time in Helsinki.

Running in -10ºC weather with a frozen bottle of water… quite an experience.

This is not actually Chris but me asking Dall-e 2 to render his experience 🤯

How can readers help you achieve your mission?

We've just launched a community version of our platform. We started out looking for tools like this to search through data and it feels great to be able to help solve that problem for others. It's free and we're fuelled by feedback, so do get in touch!

If you find that the tech is really helping you on a regular basis and you work in a cybersecurity team, give us a ping so we can help you bring the full version home.

If you like where we're going as a company, and you're an investor interested in cybersecurity, software and deeptech, we're also currently looking for seed investors 😉

Free platform trial and founder contacts

If you want to get in touch with Cris, here is his LinkedIn and you can sign up to his Community platform here.

About me: I'm the founder and managing partner at Silicon Roundabout Ventures. A computer scientist by background, I ended up in venture capital through my love for technology, building communities and applied science.

Financial indicators, such as the general direction of public markets, tell us that the current environment is a bearish market.

Yet this is, perhaps, precisely why now is the time to build 'unsexy' startups.

In both public and private equity markets we have effectively seen a 20-year-long bull market unfold, which was briefly hit mid-term by the real estate bubble crash. Over these 20 years, flashy digital platforms proliferated, from social media to fintechs and from mobility and delivery apps to thousands of copycats B2B software companies.

At first, it was a game for bold digital investor pioneers like Marc Andreessen and Peter Thiel that were bucking the gloomy trend post-dot-com crash ─ and made their names in the world of VC with those early bets. However, by 2021, the cycle reached mania stage and investors from all walks of life wanted to back digital platforms and 'low-tech SaaS'.

Public markets were just as hyped with SPACs and IPOs popping up all the time.

Now, after three full quarters into 2022, markets have cooled down significantly. We don't know for sure how long it will last, but the market is definitely experiencing a downturn.

On the flip side, cycles are just that: a period, a season. And a bear season does not necessarily mean businesses can't be started. Nor that businesses won't be funded.

They do mean a shift in the market perspective, but this can be a positive change for founders focused on building hard, complex businesses with strong fundamentals, or in industries that saw less valuation hype in the last few years.

There are at least five reasons why founders of unsexy startups could actually find it easier to build their companies now.

1. Capital is still there

The tremendous amounts of capital that was raised during the bull cycle must be deployed over the next few years.\ In the US alone, the National Venture Capital Association (NVCA) measured dry powder to be as much as $290bn, as of June 2022. Some estimates believe the global VC industry to be sitting on cash commitments of $500bn or more.

When a VC firm raises money, it generally commits its capital to investments over a three to five-year investment period. So, unless a major financial meltdown collapses the economy of their limited partners (investors in VC firms), making them default on their commitments, capital that's been raised must eventually find its way into deals.

Sure, what we saw during Covid pandemic, such as VCs emerging from the woodwork and funds being raised and deployed in 18 months, was abnormal and we are seeing a slowdown that's unluckily to revert back to the recent pace.

However, funds make money both by generating returns and by managing the actual money. The first is how they actually make real money. The latter is what gives them cashflow for their operations. If they don't invest at all, they will need to return the capital, which will kill their bottom line. So if good deals come around at the right valuation, VCs won't hesitate to deploy and demonstrate they are still relevant to their own investors.

2. Investors are more judicious

Due Diligence is back and closing rounds is taking longer. This actually benefits companies with their heads down building real businesses, rather than flashy 'hot round' celebrity startups.

Many investors and entrepreneurs become very rich during bull cycles, where certain types of founders and their early backers can kickstart a game of FOMO. It's a game that goes all the way up the food chain, from high-net-worth writing angel cheques just because they can, to VC funds riding the hype wave. From private equity and hedge funds wanting a piece of the cake at growth stage, to retail investors buying up stocks when companies go public.

Covid, rather than slowing the party, cranked up the volume. Investment decisions made over a single zoom call, fast rounds closed with no real rights for any investors, SAFE notes sent and signed in a week, seed rounds ballooning to tens of millions and lack of referencing -- all this became the norm rather than an exception.

At the peak of their frenzy, bull markets become very noisy. Business valuation turns into a hot potato game in which a company's ability to raise capital is largely separated from business fundamentals.

As markets cool down, businesses that can either point to real underlying technology innovation or simply more solid business fundamentals -- the unsexy startups -- will finally find they are given their deserved air time.

3. Crashing down has its costs

Companies that raise sensibly and have room to grow into huge markets will have an easier time hiring from the flash-in-the-pan companies that raised too much and now have to cut hard.

Over 20 European 'VC darlings' from the covid era have been slashing their workforce in 2022. This is not counting companies that literally shut down (I helped one team connect with a few portfolio companies of mine, for example) and even corporate VC operations being downsized. In the US, over 40,000 workers lost their jobs in scaleups as of September. This means that for once, the tech market is full of people that are looking for new job opportunities and most hypergrowth-type scaleups are not there to compete for talent.

Even publicly listed Big Tech is not immune to market conditions, creating a unique opportunity for early-stage startups with a low, yet sensible budget, to have higher chances to secure great tech talent.

4. Lower valuations no longer a curse

Lower valuation multiples now have their pros for startups. Many complex and harder-to-build startups have had far lower valuation watermarks than vanilla SaaS businesses. A few examples can be product companies, hardware startups and also those companies with higher margins, but in historically unsexy areas for generalist VCs like logistics, defence, and even certain less explored areas of gaming and creators' economies.

Now that the digital wave has come crashing down and that VCs have more time for due diligence (and more interest in actual underlying metrics), businesses that have genuinely deep tech IP or that are able to showcase an easier path to profitability, have become prime investor targets in the current environment.

Whilst it is early days to have definite data to confirm the above, which I mostly infer from the ongoing launch of specialist funds in these sectors and the feeling I get from investors I meet remotely and in London, funded startups indeed look a bit different than last year.

The top 10 VC-backed scaleups list is no longer dominated by fully digital platforms, micro-mobility, last-mile delivery, and B2B SaaS apps but gives space to companies in biotech, cybersecurity, technology for brick-and-mortar retailers, aerospace, and hardware medical devices.

5. Specialists in deep tech are launching at all stages

At the end of 2019 I was just about to start my career as an angel. I was then invited to attend the first edition of Allocate.gp, a non-profit event for emerging VC managers. There, I met only one fully-dedicated deep tech investor. And even they would not really cover hardware.

Europe has historically been playing catch up on tech with the US and has been lagging behind even more specifically in deep tech innovation and deep tech funding.

The data confirmed my gut feeling from running the Silicon Roundabout tech meetup community, that hardware and deep tech were overlooked areas in the VC world. Especially at seed and then later at the late growth stage.

However, the trend is changing and more and more investment is going into deep tech and overlooked sectors and founders.

Anecdotally, in September 2022 I returned from an Allocate.gp retreat for fund managers, this time as the general partner of my own deep tech fund, and out of 40 VC managers, eight covered deep tech and an additional five covered non-deep tech overlooked founders and industries.

In general, the trend across the whole private market space (both in private equity and venture capital) is that new funds tend to be specialists, rather than generalists. This is because the underlying investors in funds are recognising that it's becoming hard for VCs to do well across all of the technology sectors and stages. So they prefer to build baskets of thematic funds they believe to be master in specific geographies, sectors and stages, when it comes to starting new relationships with new VC firms.

This trend is bringing more attention to sectors and types of businesses that have historically been overlooked.

Complex and unsexy is the new sexy

So in short: the unsexy and hard stuff is set to become the new sexy in the startup world. And even for non-deep tech startups, those with real harder-to-build businesses will have an easier time fighting the noise of celebrity 'hot' rounds.

The same market sentiment that's slowing down fundraises and shrinking some unicorns' valuations may just be what other founders needed to get more space to prove themselves with numbers and facts. If you factor in that the biggest hirers for the last decade are now firing people, you also have a much more startup-friendly talent market.

Investors have become more willing to dig into Excel spreadsheets to understand complex business models. To do reference calls with customers and IP experts and explore new markets that can solve huge global problems. With less noise comes more focus.

There is, therefore, no better time than now to build unsexy, complex startups that are hard to do. Because, for once, capital is still there and, whilst it's harder to get it overall, the fall is mostly being felt by those software companies who 'ate the world' for 20 years and shot up during covid. The rest can now take their fair shot.

Francesco Perticarari is managing partner at Silicon Roundabout Ventures.

Originally published as a guest opinion post on UK Tech News

Conflicted between taking notes and focusing on the customer or team member in front of you?

What about realising, after a few months of back-to back online meetings and endless exchanges on Slack, Trello and Google docs, that the core info on what you and your team are doing is buried in a mountain of hard-to-retrieve information?

I tried to use the transcript tool Otter. That helps as a way to retain the transcript of the call but it’s completely passive and more content generated makes it hard to go back to make action points or extract highlights.

The ideal would be to have a human sitting at the meeting and taking notes. And then another human to sift through the notes, organise the content and turn it into a hierarchical and searchable database of highlights, action points and data reports.

In fact, probably a bunch of humans: from secretaries to assistants to data analysts and project managers.

And what about a PA who could prompt suggestions in real time about turning notes into indexable summaries and action points?

Of course, having such a team behind you at any given time is impossible. Even if it were possible, it would add an extra level of management and such organisational complexity that you’d end up at the polar opposite of a lean team, especially if you wanted ALL of your customer-facing team members to have this support behind them!

Long story short: If capturing, sorting and acting upon interaction data between you, your team and customers is key to your business success (but is also an overwhelming task), then… you’re not alone!

Because no one has truly figured out a solution to this problem.

You feel the pain. I feel the pain. Our teams feel it. We all feel it, if we are running a business and rely on digital tools to do it.

TL;DR: In the end I found Kaizan.ai to help me capture and action up customer cand team communications. This article is about what Kaizan is, how I got to start using it as early as its alpha release, and why I even ended up investing in the company's pre-seed round 🤯

A huge global problem

It only gets worse if we look around us: Gartner says that by 2025 80% of client interactions will take place through digital channels. The more interactions between team members or between business teams and client organisations happen, the more information gets generated and shared, but information is not what’s key in all these interactions. What IS key is generating value from this information by turning it into searchable and digestible insights that can be:

(a) shared and re-shared across team members if and when needed, and

(b) actioned upon at the right time and in the right way.

At the end of the day, business organisations exist by solving a need for a certain target demographic and by generating revenue in the process. To achieve this, companies need to find, engage, acquire, satisfy and retain customers all the time.

Ultimately, therefore, their success is constantly measured against the balance they can find between increasing growth and reducing customer churn. That percentage number reflecting how a company’s annual revenue has grown or shrunk within a particular period is called Net Dollar Retention (NDR) and is a key metric for valuing companies. However, an elegant end-to-end solution for managing and scaling clients has yet to be built.

At the moment, companies use a CRM to track the numbers and store interactions, project management tools to track projects’ progress, and a bunch of other tools to help with storing text and even audio-video content. But they leave the rest to the customer rep: namely to pay attention 100% of the time within a call or when exchanging messages, take all the appropriate notes, turn them into insights and actions points, and store all this into the appropriate toolkits (many of which don’t even talk to one another!).

To add some numbers to define how big and painful this problem is:

• Business leaders are desperate for digital solutions to improve team efficiency through technology ─so much so that the global team collaboration software market is expected to reach $24.02 Billion by 2028 with a CAGR of 12.4%, according to Growth Market Reports
• Because client success teams aren’t being enabled, and key information isn’t flowing through an organisation automatically, alignment between product and customer success teams on client health is only 29%
• Forrester Research reported that 83% of businesses surveyed for their 2020 research indicated a desire to digitally transform customer success, according to Smartkarrot
• In 2020, the customer experience management market alone was valued at $7.6 billion (Grand View Research, 2020)

What if there were a better way?

What if there were an AI company that could amplify the abilities of client teams? What if there were a tech platform that could provide you and your customer-facing team members with ongoing support with little to no need for your input during your exchanges?

Enter Kaizan.ai.

Founded in 2021, Kazan automates your tasks using conversation intelligence and highlights what will increase client success (and therefore revenue!).

Kaizan is the brainchild of Glen Calvert and Pravin Paratey, the first a successful AdTech entrepreneur and former COO of esport startup Fnatic, and the latter a 4-time co-founder and CTO of various tech startups, including one with Glen, and an ex-Engineering Manager at Facebook.

Together, Glen and Pravin built Kaizan’s software leveraging advanced natural language processing (NLP) and conversation intelligence models to extract the key tasks and actions you’ll need to enable client success.

I’m not the only one to think it’s pretty awesome, with testimonials on Kaizan’s website all being enthusiastic about the tech. To give you an example, Lisa Hau, COO at Bidstack PLC, said that, with respect to customer service, Kaizan is giving their team "an edge in efficiency and service."

The goal of Kaizan is guiding clients in retaining and growing their clients with its Client Intelligence Platform. The cloud software currently has an in-browser add-on plus a stand-alone web platform. It automatically listens to client calls and proactively summarises their content in a searchable and indexable way, as well as noting down all the highlights and action points.

Yes, you read that right: automatically!

That’s the beauty of their NLP engine, which is attuned specifically to business verbal exchanges. It deciphers the context of each exchange, extracting not only the gist of what’s being said but also what you should be doing about it: from reminding you about action points to flagging any important note that’s key to taking next steps for that client.

In the words of Glen himself, when I sat down with the two founders for a virtual coffee the other day: “Any of us [in the team] get super excited by where we move in, with NLP, and these massive language models, and by what language interfaces will look like in the future”. Glen and Pravin firmly believe they are building something that was bound to come to life anyway, sooner or later. According to Glen, soon “you’ll be able to ask questions” to NLP-powered work-enhancing tools like Kaizan. They will then be able to “surface information to you… be able to read for you, write for you, [and to] share information [with the rest of the team] for you.”

Needless to say, I am a super early adopter (since the alpha pre-release in fact!) and a happy one at that. Its current “minimum viable product” (MVP) is helping me take notes during my meetings without keeping separate tabs open. It automatically understands what action points are to be prioritised and extracted from the conversation, which are then fed back to me!

Oh yes, I also invested in the company ─ Here is why:

As they say, I put my money where my mouth is on this one. Whilst my venture fund was not live when I had the opportunity to invest, I still decided to back Glen and Pravin as an angel investor.

Here is why:

The team

I met Glen, the CEO, when he was still COO at esport scaleup Fnatic and we connected immediately. At the time, he told me he was working on something new and we exchanged contacts because… you never know. After some time he actually left and he could finally tell me more about what that “something” was.

Enter Pravin, the CTO, and together the three of us chatted about Kaizan as it was starting to take form. With Pravin, we went through his career building tech stacks and data models in the advertising industry, which culminated in his work at Facebook using user data to increase ad effectiveness. We talked extensively about the data models and strategies used inside Kaizan. We discussed, for example, how they planned to diverge from the general purpose NLP of Open.ai and the likes, to create a “smarter” (albeit more “focused”) NLP AI. Something unable to generate novels perhaps, but capable of making sense of business exchanges and providing responsive focused support.

Besides the initial conversations, I did my research on their backgrounds to see if they could deliver on what they wanted to be. The answer was a resounding yes, at least in terms of potential.

For a start, both of them were entrepreneurs and had co-founded a business before, Affectv, an advertising technology company that runs media campaigns for global brands and that is still going strong under the new name Hybrid Theory.

They exited the business at different stages, taking different paths. Pravin ended up becoming Engineering Manager at Facebook, where he managed the teams responsible for launching Brand Awareness Objective, Reach Objective, Brand Polling and Creative Hub. Glen became COO of Fnatic, where he saw the organisation raise significant amounts through funding rounds and close partnerships with the likes of BMW, OnePlus, and L’Oreal, among others.

All together, I loved the sales and marketing approaches Glen was proposing to scale the business, as they fit with his track record. As a developer myself, I loved the tech chats with Pravin about NLP strategies and models even more. And I loved how what he was proposing not only made sense in my own view of technology development, but also fit with his background and experience.

Finally, I made the assumption that, since they had managed and scaled businesses and teams before, they would have a good handle on how to take Kaizan forward after finding the perfect product market fit.

Compelling market opportunity:

As a software engineer, I worked with machine learning systems before.

I found Kaizan’s approach interesting because it felt like the right balance between leveraging existing NLP technologies and building something new in that space. I would not have wanted to back someone who simply slapped the GPT-3 API on top of a call transcript. For me, there is no innovation or computer science advancement there. But I know that machine learning will not give us Star Trek’s Data or Star Wars’ C-3PO any time soon. Hence, abstracting, synthesising and indexing information and creating prompts that focus on specific niches and verticals seemed a much more achievable task that made a lot of business sense.

Since I am also a business owner dealing with customers, I was (and still am!) someone who feels the pain of having a tech solution to be my “portable PA” and sales support assistant in my calls with customers. I can’t afford to have “minions following me anywhere” and I probably would hate to have them anyway!

As a technologist AND a tech investor I feel that their “monkey job” function is not something humans should be doing. In that spirit, I was an early adopter of Otter.ai, I use Zapier, Calendly and RPAs regularly, and I even coded scripts to support me in dealing with customer-facing stuff. But a solution that helps me in a genuinely smart way? I could see myself as an early adopter AS MUCH as I could see myself as a believer.

In short, I felt I could relate both to the solution they were building and to the pain they were addressing in the market. I was convinced that the SME and corporate spending on customer support intelligence and automation tools is a large enough market for Kaizan to have a shot at going big, either becoming a unicorn company (billion-dollar valuation) or being acquired for a significant amount of money by larger tech corporations.

Value Add

I run the Silicon Roundabout tech meetup community, which is a network of 15,000 tech founders, engineers and startup operators, and I am a software engineer. I wanted to try out Kaizan for myself as a potential user, so I thought I could help out.

Glen and Pravin invited me to join the alpha testing phase from the very beginning, so my investment decision was influenced by playing with the product and exchanging feedback and ideas. In parallel to that, I offered to help with marketing, branding and spreading the news within the community after the beta phase, which we all agreed would be a useful option. Ultimately, though, I felt that being that nerdy tech guy who believes in both the vision and in the implementation strategy was in itself valuable, because I know from experience that being an entrepreneur can be an extremely lonely and difficult path.

Lastly, I know a bunch of investors in the community, especially at VC level, to whom I could introduce the startup when the time is right for late seed and series A funding.

So I felt I could be a useful shareholder, even if my investment ticket was small.

How do NLP systems like Kaizan’s work?

Engaging with the Kaizan founders from the very beginning, deciding to become an alpha tester and then an angel investor, was an expression of my passion and bullishness for deep tech systems.

Do AI “software companies” belong to the realm of deep tech? When they work on genuinely innovative algorithm systems and models I believe they do.

Most NLP apps out there are simply skin-on frontends built on top of Open-AI or some other APIs. This is great, but can only innovate on the business model and UX side.

Some, like DeepMind in the “grown up” world and Kaizan in the seed startup world, want to achieve noticeable advancement in the state of the art. For that, they need to develop their own AI models.

In the NLP world, transformers are all the rage these days. Kaizan uses them amongst other models to capture the context of a conversation, extracting the key highlights and action points.

A transformer is a neural network for sequence learning. It was introduced in a seminal 2017 paper by Google Brain titled Attention Is All You Need.

They do semi-supervised learning, which comprises of unsupervised pretraining followed by supervised fine-tuning. Pretraining is the core part of the model and is done in a self-supervised way. The training is typically done on a huge dataset with no labelling provided. Instead, the algorithm is left to come up with its own rules and labels to predict unobserved or hidden parts of the input.

For example, in NLP, the words of a line can be predicted using the remaining words in the sentence. Or, like in the case of Kaizan, you may want to perform an information abstraction process. In that case, you’d want the model to absorb a large body of conversational text and isolate bits of information, which need to then be re-encoded in a human-readable set of notes or action points. To build a model for a specific task, you’d start with the pretrained model then fine-tune it using training data specific to that task.

Transformers use a framework of encoders and decoders to construct the neural network. The encoder converts the input sequence into a higher dimensional vector. This vector is fed to the decoder to produce the output sequence. Their architecture uses a mechanism called attention, along with positional encoding and normalisation to deliver state-of-the-art results. The idea behind the attention concept is to use a function, such as a scaled dot product, to connect encoders and decoders in a way that helps the model make sense of context within the sequence.

In practice, any given model framework is just a starting point for a startup that wants to develop state-of-the-art systems within a given niche, because its purpose is to deliver a useful product, not to publish a paper. Multiple models and strategies within the overall product architecture have to come together and be constantly trained and improved if the system is to be truly effective.

In terms of the stack used by Kaizan at the moment, Pravin, the CTO, told me “It is Python Django on the back end, with some Celery”, and “on the front end it’s Svelte”.

In Pravin’s words, a great engineer looking to work for an AI startup like Kaizan would be able to answer positively to questions such as:

“Have you built systems where you've had to deal with unstructured data? Have you had to deal with masses of data? How do you create a real time feedback loop? How do you train models? How do you run models in real time? As there are more architectural challenges than [language optimisation challenges]... the [coding] language [we picked] is just a tool that we use to code”.

How did Kaizan come to be?

As Glen, the CEO, explained to me, when it comes to Kaizan, a lot really “can be told from the name”. “Kaizan”, says Glen, “is a play on the word Kaizen, with Kaizen being the Japanese term for continuous improvement”.

According to Glen, both he and Pravin have been fascinated by thinking about what the future of work will look like, when you want to call on very advanced machine learning models and NLP models inside the workspace

They started off with that concept, built an initial prototype, and onboarded a bunch of users from a variety of different departments.

Thanks to their previous entrepreneurial bruises and successes, both cofounders knew that to start a company they had to find product-market fit; not just solving a problem, but solving a problem that customers would be willing to pay for.

In Glen’s words “you have a vision and you need to prove some of the points out and disprove some of the points as well”.

"Through many iterations with the prototype and working with a bunch of alpha users, we came across the insights that we needed that would lead us to the next iteration of the journey”

For the first 12 months, Glen and Pravin worked intensely with a bunch of free alpha users find the target persona within different companies for whom Kaizan would be a problem solver. Basically, they only did R&D and early customer feedback. As part of the process, they secured some angel cheques, including mine, as well as strong pointers on what market to focus on.

“Those Alpha users produced some insights for us, which was super useful”, Glen told me, “in particular: the Client Success teams”.

Initially, the two co-founders were working with salespeople, marketing teams, product people, engineers, and Client Success teams. As they figured out where the opportunity was for their AI system, it became clear that the users working in Client Success were the ones coming up with more questions and pain points that Kaizan could solve.

As Glen said:

“It dawned on us very quickly that if you're starting up a business, there are many go-to products that you'll lean into”.

“You're probably gonna go to Xero for finance…to Hubspot or Salesforce for your CRM… JIRA for development and product… Figma for product design… [But what] if you ask the CS team ‘what do they use for growing the client base?’”

Glen told me their answer was consistently: “We kind of use a spreadsheet for this, we use a project management tool for that, we use a CRM for this, but the pain points are huge for us. We don't really know how happy our clients are, if they’re going to stay, if they're going to go and it's very, very difficult to manage all the tasks”

That was what led them to focus the ensuing product development phase on building an AI that would understand client health, risks, opportunities, sentiment and agreed action items. Kaizan could then take that data and generate cues for what a Client Success executive might want to do next. It could take information from calls and messages and start to automate their workflow, from writing follow-up email ideas to creating summaries of activity with a particular client.

That’s how Kaizan moved from the initial alpha to a private beta and now to its public beta, which you can access on their website, if you’re interested in trying it.

They also raised a full pre-seed round, which was joined by more angels and a couple of small VCs.

What’s on the roadmap for Kaizan from here?

Kaizan started with conversation. A lot of what the Kaizan team has built for their alpha and beta users has so far been around calls, because there are a lot of live conversations happening with clients. According to the team’s current roadmap, they will next look to integrate emails and, in the future, any communication about clients, from Slack to project management software.

As customer-focused and problem-driven founders with previous startup experience, the Kaizan guys built their business thesis by speaking loads to their target users. For example, the CEO, Glen, put their Client Success users in front of a bunch of problem statements and solution statements, to hear their responses. This helped the team refine their focus by ranking aspects of the problem and revealing new ones.

Through this work, Pravin and Glen figure out that customer-facing teams don't currently have, but desperately need, a true “clients’ intelligence platform”: a layer on top of their CRM that helps them retain and find what to do with their clients.

Ultimately, what Kaizan is building is a software intelligence platform that can use big data and proprietary R&D work in the NLP field to enhance human work. It reduces errors and internal information search time, as well as automating customer acquisition, retention and management tasks.

“What we have done,” said the CTO, Pravin, ”is finding a niche within which we're now trying to understand communication. A lot of the models out there [that can attempt this] are quite generic. [They are] based on the World Wide Web corpus. Extracting action items… extraction decisions… These are really tricky things to extract. There's still a long way for us to go, but we are significantly better than… open source state of the art”.

On the difference between what they are doing and the research in the field by tech titans like Google and Facebook, Pravin also has an idea.

“Everything that we are working on right now is based on the work that giants have done. But that niche that we're tackling, nobody's quite worked on just yet.”

Of course, not all work is Machine Learning R&D and optimisation.

“When it comes to building… we had to be very thoughtful around the user experience,” in Glen’s words, “because we have to… [come up with an] interesting output. We have to think about the UX because there is an opportunity not only for us to automate downstream workflow from upstream communication, but also to build a solid solution that our human, mostly non-technical clients can interface with”.

"Among the 10 best VC funds of a given year, a disproportionate number of investment firms are represented with their first or second fund"

Mark Schmitz, Equation Capital

New venture capital firms are often particularly successful in startup investments. This is the result of the analysis carried out by the Munich-based investment platform Equation, which provides capital to venture funds: what the industry calls a Limited Partner or LP. "Among the ten best start-up funds of a year, a disproportionate number of investment firms are represented with their first or second fund," says Equation founder Mark Schmitz.

Competition among venture capital firms in Europe has increased enormously. There are now 550 active European venture capital firms according to the European Capital Report 2022 by the consultancy firm i5invest.

48 new venture capital firms were launched in 2021 alone and, statistically speaking, the probability of top returns is particularly high amongst these. "The results contradict the common perception," says Schmitz, "Because investors are historically more likely to rely on established firms".

Venture capital firms raise money from high-net-worth individuals, family offices, pension funds and other institutional investors and, with this capital, they invest in startups. If their portfolio companies are later sold or go public, or if late-stage investors buy shares at a premium via secondary market transactions, the money flows back. Sometimes the returns can be spectacularly high, with the best funds in the world, which were launched about ten years ago, now repaying their investors 25 times their money and more. These prime yields are the focus of Equation's research.

Because there are good and bad startup years, VC funds are always compared on the basis of their launch time. As with wine, the venture capital industry speaks of "vintages".

Equation has evaluated figures from the Cambridge Associates database for the US market: Between 2004 and 2016, significantly more new venture capital firms made it into the ten highest-yield funds of their year. In eleven out of 13 vintages, more newcomers with their first or second fund came into the top ten than established competitors. Twice they were tied ─although there were always significantly more established investment firms contending the crown for any given vintage.

New venture capital firms are more successful: According to data by Cambridge Associates (2019), VCs in their first or second fund are disproportionately more likely to make it into the top 10 performers

Schmitz's co-founder at Equation, Reiner Braun, has been researching venture capital for years as a professor of corporate finance at the Technical University of Munich (TUM). Based on the research, the two assume that the pattern is transferable to Europe.

An example of a German premiere hit is Point Nine. The first fund of the company, which was launched in 2011, has resulted in the Dax group Delivery Hero, among others, and the banking software provider Mambu, which is currently valued at €4.9 billion. In the UK, the first proto-fund for pre-seed investor Entrepreneur First recently announced cash-on-cash return to its LPs worth 17x the capital invested.

In Europe too, therefore, investors in first-time funds have a higher chance of exceptional returns. But why is that?

The Equation team suggests three reasons for this.

1. Young funds invest particularly early

First of all, the funds differ in size. A first-time fund holds an average of $46 million, the fourth edition a good $190 million, and for the seventh funds it is an average of $300 million. The fund size correlates in which startup phase investments are made. Small funds tend to invest in startups early, large funds in the growth phase when the need for capital increases.

One thing applies in principle: the earlier an investor gets involved, the greater the risk and return opportunities. But TUM Professor Braun says that the higher probability of achieving particularly high returns "cannot be explained solely by structural differences such as the investment phases." However, recent data from Dealroom, suggests the most successful "unicorn hunters" (VCs capturing billion dollar startups) are the ones backing these companies at seed.

Furthermore, a small fund is "easier to multiply".

Let's say a $10m VC invests an average of $500k in early seed rounds of $2m at an average of $8m pre-money valuation: this investor now should own about 5% of their investee companies ($500k is 5% of $10m). Let's assume this investor spends 15% of the fund in fees and does not have any mechanism to recycle these and that all the fund is invested in first cheques with no follow-on capital reserved for later rounds. This VC would now have invested $8.5m across 17 companies.

Let's say now that one of the these investments becomes a unicorn after 5 investment rounds and exits at that valuation. Let's model 20% dilution at each round. The VC would own 1.64% of the company at exit: \( 5\% * 80\%^5 = 1.64\% \)

That's $16.4m back to the VC out of a single company! The fund is already over 60% up from its initial capital because of a single unicorn. With 16 more companies to go, this fund could very well be on the path to the top-quartile returns VC funds aspire to.

Conversely, a $150m fund backing the same 17 companies would have barely spent ~5.6% of its capital and, despite capturing a unicorn, it would have recovered just over 10% of its fund from this "success". It would therefore probably avoid doing $500k cheques and look for bigger rounds, where it could deploy a few millions per ticket, which means coming in later and enjoying less of the upside. It would then need to reinvest more money into the best performing companies of its portfolio, needing bigger and bigger exits to justify the the cost of its deployed capital. Suddenly this type of funds needs decacorns (startup worth $10 billion or more) rather than unicorns ─and ideally several of them. This makes it practically impossible for large funds to achieve the 17x level of returns generated by Entrepreneur First with its first cohort.

On top of this, as Schmitz's partner Andrea Casasco points out, investors in the very early rounds tend to be more insulated by the hyper-inflation that affected the growth stage valuations in recent times, and that is likely to cause the biggest losses in case of a market downturn.

2. Emerging venture capital firms focus on trends at an early stage

New venture capital firms are also betting on new trends, according to another part of their analysis in which Equation compared venture capital funds in Europe founded since 2016 with older ones. For example, the younger funds have made 6.1% of their investments in decentralised ledger technologies such as the blockchain, the older ones only 1.6%. "We even see funds that specialise in the areas of climate technology or aerospace, for example," says Schmitz.

New funds raised for any given vintage: Chart from Equation on Handelsblatt using Crunchbase data

The investor's explanation: "If there are new trends and sectors to open up, then those who are already active in the industry must first rethink." New firms can put together teams that are familiar with these trends from the outset and may have even been driven to venture capital in the first place to capture a trend early where they have unique expertise.

3. New fund managers have different work experience

TUM and Equation have also found differences among decision-makers, especially in terms of work experience. 72% of fund managers at emerging venture capital firms have previously founded their own companies or worked for startups in various departments, from finance to technology, and can pass on this experience. For the established, this only applies to half.

• Data Equation 2022 based on Crunchbase*

Nevertheless, investors should be cautious when investing in new funds: there is also an increased risk of default. The stronger the competition, the harder it is for new investment firms to win the best deals. For startup founders choosing a VC partner to receive funding from, especially the best ones who often have multiple options, a good network and references are often crucial when making the choice. Whether a new team is plugged into the ecosystem and has a good reputation in the market is therefore a key aspect to diligence for someone looking to back emerging VC managers, Schmitz argues.

Emerging managers: an opportunity for the European venture capital scene

Across Europe, there is a clear institutional push to encourage new VC funds to join the market. In the original German article where Equation decided to publish their research findings above, it was reported that the German government deliberately wants to help the birth of new venture capital firms, which often lack the trust of private investors. Germany has even its own government-backed LP investor to invest in funds who are heavily focused on investing in Germany: KfW Capital. This is the same across Europe: where each large nation has a similar institutional LP fund, such as Italy's CdP Ventures or the UK's British Business Bank. There is then a EU-wide initiative too, the European Investment Fund, which really kickstarted the whole government-backed fund of funds brigade and often pairs up with national funds in backing new VCs ─other than with the UK one of course, because... Brexit.

Government-led initiative want to make targeted investments in areas "where the European market still needs to be pushed," says Anna Christmann, startup representative at the German Federal Ministry of Economics. "Especially if the risk for the market is too great, we can use public money to provide incentives," she notes. Investments by KfW Capital are therefore often used to support first-time funds. According to Christmann, this was most recently the case for 20 percent of all fund investments. Other national funds have similar agendas, with the British Business Bank for example running a specific programme for first-time funds called the Enterprise Capital Fund.

Yet, programmes of this nature often come with strings attached and, on top of being quite bureaucratic to navigate and manage, they normally tend to back only people with established traditional track record in venture capital and with a very specific geographic focus. Therefore, whilst 10 years ago it was almost unthinkable to start a fund in Europe without government support, today more and more emerging VCs chose a different route to market, which often comes with more freedom to operate across geographies and with more diverse team backgrounds.

Not for laymen: Great opportunities, high risk

According to Greenspring Associates, an LP with $11 billion assets under management, of the 180 VC funds analysed under its portfolio management program, emerging fund managers edged out more established GPs (Net Multiple +0.10x; Net IRR +0.87%). A Cambridge Associates report agrees: “[T]oday’s market is not the same as 20 years ago. Broad-based value creation across sectors, geographies, and funds means success is no longer limited to a handful of (often inaccessible) fund managers. Moreover, top returns are not confined to a few dozen companies. New and developing fund managers consistently rank as some of the best performers.“

As we have seen, there are 3 reasons why emerging fund managers might have slightly better performance than established VCs in today’s environment:

• First, most emerging firms raise small funds. Smaller funds generally outperform, as they can participate meaningfully in early rounds and a single outlier has the potential to generate strong fund-level performance, even if the fund is only able to garner modest ownership.
• Second, emerging fund managers tend to be made up of a smaller group of GPs or solo capitalists with strong industry insights and personal brand in a specific industry or trend, often when this is still nascent and overlooked by the established players.
• Third, most emerging fund managers do not have to worry about succession plans or manage an existing brand and track record. They can be focused on deliverying on their chosen strategy,
• Finally, data shows that the venture capital industry has strong reversion-to-the-mean effects, which implies that the Midas Touch tends to rub off over time—after about 60 portfolio company investments (See The Persistent Effect of Initial Success: Evidence from Venture Capital, § 3.2): “Little, if any, persistence [occurs] beyond the 60th portfolio company, implying long-term convergence to a common mean”.

The bottom line is that emerging fund managers have indeed an edge compared to established VCs in terms of fund performance. On the other hand, as with most investment opportunities, greater upside also means a greater risk. Therefore any investor looking to back successful new firms ultimately needs to be able to assess whether the managers of such VC funds have the ecosystem connections and unique insights to deliver on their investment thesis.

Credit/Thanks: A big part of content for this English article was based on a German interview by Luisa Bomke and Larissa Holzki from Handelsblatt with Equation Capital's founder Mark Schmitz and data from his firm's research

Originally published on 'Digital Rubicon', Feb 2022:

In discussion with Silicon Roundabout Ventures co-founder, Francesco Perticarari

Since Silicon Roundabout hosted its first London meetup for tech entrepreneurs in 2011, it has grown to become one of Europe’s largest innovation hubs boasting over 15,000 members. Such has been its success that the platform’s founders, Francesco Perticarari, a Computer Scientist, and Paul Dinulescu, a tech entrepreneur, are now launching their first VC Fund, Silicon Roundabout Ventures.

It is a propitious time to be backing Europe’s digital/tech leaders of the future. Last year, the region recorded $116 billion of investment dollars according to Crunchbase. A decade ago, that figure was a measly $8 billion. More importantly (as far as investors like Silicon Roundabout Ventures are concerned), $40 billion of last year’s investment total went to seed and early-stage start-ups.

The community-based Silicon Roundabout Ventures fund will invest in European start-ups developing next generation technologies across computer science (i.e. advanced software) and physical sciences (i.e. hardware and material sciences).

As part of the fund launch, the team invested its own capital in companies that are being transferred at cost into the fund portfolio. Companies such as: Axiom.ai, a browser automation solution; ORI Industries, an edge cloud technology provider; ApTap, a subscription management platform leveraging Machine Learning; and EcoSync, an AI-powered energy management system.

When asked about browser automation, Perticarari explains: “You've already got successful RPA companies such as UiPath, that have gone public and mostly target large corporates whereas Axiom.ai is trying to do what Zapier did for API's, by using a bottom-up approach where even people with no coding experience can start to build custom bots.”

The mission at Silicon Roundabout Ventures, in keeping with the ethos of the Silicon Roundabout hub, is to help nurture tech entrepreneurs and help them develop their business models and products from until they are ready for their Series A.

What makes the fund unique is the close affinity it has to the community, which not only provides a reservoir of potential investment opportunities, but also enables tech founders to connect with other technologists and companies through meet-ups and networking events, as well as seek out talent and investors.

Back in 2016, the team started running pitch competitions focused on big data and frontier technologies. Some of its community start-ups include companies such as Monzo, Crypto Quantique and CausaLens.

On 28th January 2022, London-based CausaLens announced a $45 million Series A fund, led by Dorilton Ventures and Molten Ventures.

“Over the years we have selected Silicon Roundabout competition winners and helped launch what, at the time, were very early stage, that is seed/pre-seed companies. Two of them have become billion dollar companies,” says Perticarari.

“Between 2017 and 2019, we started to focus ever more increasingly on those sectors that we found struggled the most with brand visibility and capital in the early stages, like robotics, advanced machine learning and artificial intelligence. These niches had potentially even more long term potential, in terms of disruption of society and helping the planet and making an impact but struggled in the very early stages to raise capital.

“Silicon Roundabout was a way for us to give them brand visibility, and to help them reach out to engineers, potential investors and potential business partners.”

When Covid-19 struck two years ago, it necessarily led to a cessation of physical meet-ups and it was during those early months of living through the pandemic that Perticarari started to back companies as an angel investor. That led to the idea of launching a VC fund.

“Fast forward to today, and we're preparing to launch the first externally backed pool of capital. We're still in the process of getting FCA regulatory approval. Hopefully that’ll come in the next couple of months,” he says.

The primary focus of Silicon Roundabout Ventures is the UK but with over 5,000 companies using the Silicon Roundabout platform, there is plenty of scope for the team to explore wider European opportunities over the coming months and years.

“I think this year is going to be figuring out how the community integrates with the fund,” says Perticarari. “We want to keep the brand and media presence of the platform but equally, we're probably also going to ramp up our investments into the ones that we think might be future leaders. Our focus is on advanced technologies, and the more advanced parts of computer science, creative new ways of using algorithms, and I would say mostly data-driven solutions – AI-driven solutions, cloud computing. We don't really do anything related to biotech or genetics.”

I ask Perticarari how he views the progress of Europe’s VC industry, especially in relation to backing pre-seed and seed stage companies. With the US having created the modern version of venture capital – indeed, Silicon Valley inspired the name of Perticarari’s platform (Silicon Roundabout is an area of East London home to a cluster of technology companies) – it is fair to say that Europe still lags behind. This is in part due to its more conservative mindset and less of a willingness among Europe’s academia to aggressively pursue commercial avenues for their ideas and innovations.

“I think Europe is now perfectly placed to play a leading role. The only caveat is there’s still a little bit of a hole in the UK and Europe in the pre-seed and seed stage. If you look at AI mega rounds, the amount of money flowing into deep tech finally started to go up. But for funding rounds that are $5 million or below, they are still in the minority of investments; the latest figure I saw suggested they only account for 13% of deals.

“There is still a lot of fear and conservatism when it comes to backing spinouts from universities. This is a field that existing venture capitalists and family offices are still not accustomed to investing in. There’s a knowledge gap and risk gap in respect to early-stage venture investing,” opines Perticarari.

In his view, if VC funds can create more awareness at the academic level, such that when academics come up with a new technology they can go the spinout commercialization route, it will help put Europe’s VC industry in an even stronger position.

Looking ahead for the rest of 2022, one industry trend that the Silicon Roundabout Ventures team will be keeping an eye on is next generation technology developments in cybersecurity; for example, how companies are using AI and specific machine learning applications to provide more than merely threat detection.

“I think we're going to see something different, where cybersecurity companies start using AI for things such as threat forecasting to anticipate attacks before they happen. What I'm looking for are companies that can use AI in a different way than threat detection per se,” concludes Perticarari.

Below I'm going to do a quick intro on Machine Learning per sé and then jump straight into this Top Machine Learning Libraries collection, which includes embedded links to their GitHub repos.

To put together this list, I've used both their popularity according to GitHub and my own experience as a software engineer and as someone running a startup community and a tech meetup. It is not a 'ranked' list but the 'top' libraries tend to be either more popular in terms of usage or more generalist when it comes to their domain of application.

Most of these are built using a low level language like C++ under the hood, but with an interface for higher level languages: chiefly Python —which has become the de-facto standard for implementing Machine Learning models.

What is Machine Learning and how does it work?

Computer scientist use Machine learning as a term to define the study, development and use of mathematical models of data to help a machine learn without direct instruction.

Computer Scientist Arthur Samuel first used the term in a 1959 research paper, whilst Tom Mitchell's 1997 book Machine Learning famously defined it as programs that gets better with experience:

A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.

If you were a software engineer and you went by that definition, Machine Learning would basically mean: the building and training of a data model that generalises a certain decision against a performance measure.

What can you do with Machine Learning?

Fundamentally, modern day machine learning has two aims:

1. To classify data based on a given model, and
2. To make predictions for future outcomes based on such models.

For example, a machine learning algorithm specific to classifying data may be trained on an image dataset in order to recognise images containing a certain given pattern of pixels, which may indicate the presence of a certain object. Whereas an algorithm for stock trading may inform the trader of predicted price movements based on historical behaviour.

Some crazy and cool stuff I've recently seen done with it include establishing self-driving vehicles policies and helping map the surface of Mars.

Approaches to Machine Learning

Machine learning approaches are traditionally divided into three broad categories, depending on the nature of the "signal" or "feedback" available to the learning system:

1. Supervised learning: The program is presented with example inputs and their desired outputs, which must be therefore previously labelled, and the goal is to learn a general rule that maps inputs to outputs.
2. Unsupervised learning: No labels are given to the learning algorithm, leaving it on its own to find structure in its input. Unsupervised learning can be a goal in itself (discovering hidden patterns in data) or a means towards an end (feature learning).
3. Reinforcement learning: A computer program interacts with a dynamic environment in which it must perform a certain goal (such as driving a vehicle or playing a game against an opponent). As it navigates its problem space, the program is provided feedback that's analogous to rewards, which it tries to maximise.

There are, of course, programs that don't fall neatly into these categories, such as semi-supervised learning, topic modelling or genetic algorithms.

Today, however, we don't actually have to re-implement from scratch all the algorithms that have already been discovered! That's because there are many frameworks around, which we engineers can import into a project as libraries and which can speed up the process of building and training your own data model.

So, without any further ado, here are what I think to be 15 Top Machine Learning Libraries you should try out this year:

Top 20 Machine Learning Libraries to Try in 2022

1. PyTorch

PyTorch is a Python package that provides two high-level features:

• Tensor computation (like NumPy) with strong GPU acceleration
• Deep neural networks built on a tape-based autograd system ─i.e. a system that uses reverse-mode automatic differentiation

You can also extend the framework with other packages for scientific calculations, such as NumPy or SciPy.

It has been primarily developed by Facebook's AI Research lab (FAIR) and released under the open source 'Modified BSD' license.

PyTorch is an extremely flexible library that has several easy-to-use built-in features, but that you can also extend by writing your own functions and integrating with other libraries, such as if you want to perform computations that are not differentiable.

2. Tensorflow

TensorFlow is an open source library that was originally created by Google. It is used to design, build, and train deep learning models.

It has a comprehensive, flexible ecosystem of tools, libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications. If you'd like to try it out, you can check out its official website, which is full of resources to get you going in no time.

TensorFlow now offers also support for federated learning, which means algorithms that can learn collaboratively without centralised training data.

TensorFlow provides stable Python and C++ APIs, as well as non-guaranteed backward compatible API for other languages.

3. LightFM

LightFM is a Python implementation of a number of popular recommendation algorithms for both implicit and explicit feedback, including efficient implementation of Bayesian Personalised Ranking (BPR) and Weighted Approximately Ranked Pairwise (WARP) ranking losses. It's easy to use, fast (via multithreaded model estimation), and produces high quality results.

It also makes it possible to incorporate both item and user metadata into the traditional matrix factorisation algorithms. It represents each user and item as the sum of the latent representations of their features, thus allowing recommendations to generalise to new items (via item features) and to new users (via user features).

It was developed by UK' startup Lyst, so kudos to their dev team for open-sourcing it! If you'd like to try it out quickly, you can check out Siraj Raval's quickstart tutorial on it.

It is beautifully simple to use but not simplistic and you can develop extremely powerful models thanks to it.

4. Pandas

Whilst not really a Machine Learning framework, Pandas is an extremely useful library to do Machine Learning with. It is a Python library that is used for faster data analysis, data cleaning, and data pre-processing.

Pandas is built on top of the numerical library of Python, called numPy.

5. NumPy

NumPy is a Python library used for working with arrays. It also has functions for working in the domain of linear algebra, fourier transform, and matrices, which makes it pretty much the fundamental package for scientific computing with Python ─and a requirement for many higher-level data manipulation libraries.

6. SciPy

SciPy is an open-source Python library used for scientific and technical computing. Built with NumPy, SciPy provides algorithms for optimisation, integration, interpolation, eigenvalue problems, algebraic equations, differential equations, statistics and many other classes of problems.

7. Keras

Keras is an open-source software library that provides a Python interface for artificial neural networks. Keras acts as an interface for the TensorFlow library.

It was developed with a focus on enabling fast experimentation. Its key thesis being that enabling developers to go from idea to result as fast as possible is key to doing good research.

Keras is the high-level API of TensorFlow 2: an approachable, highly-productive interface for solving machine learning problems, with a focus on modern deep learning.

Keras allows you to take full advantage of the cross-platform capabilities of TensorFlow 2. For example, you can run Keras on TPU or on large clusters of GPUs, and you can export your Keras models to run in the browser or on a mobile device.

8. Scikit-learn

Scikit-learn (formerly scikits.learn) is anothr great open-source Python machine learning library.

It includes various implementations of classification, regression and clustering algorithms, such as support-vector machines, random forests, gradient boosting, k-means and DBSCAN, and is designed to interoperate with the Python numerical and scientific libraries NumPy and SciPy.

9. Aesara

Aesara is the recently developed successor to the popular Theano library.

Just like Theano, Aesara is a Python library that you can use to power large-scale computationally intensive scientific processes. It allows you to define, optimise, and evaluate mathematical expressions involving multi-dimensional arrays efficiently. Because of this, it is a key foundational library for Deep Learning in Python that you can use directly to create Deep Learning models or through wrapper libraries based on it or on its predecessor Theano.

10. Gym

Gym is a toolkit for developing and comparing reinforcement learning algorithms. It supports teaching agents to improve via game-like environments that provide them with feedback and benchmarks.

You can write your agent using your existing numerical computation library, such as TensorFlow or Aesara/Theano.

Gym has been developed by OpenAI, the company behind the GPT-3 Natural Language Processing algorithm.

11. XGBoost

XGBoost implements machine learning algorithms under the Gradient Boosting framework. It supports multiple languages including C++, Python, R, Java, Scala, Julia.

XGBoost provides a parallel tree boosting (also known as Gradient Boosting Machine [GBM], or Gradient Boosting Decision Trees [GBDT]) that solve many data science problems in a fast and accurate way.

Boosting is an ensemble technique where predictors are assembled sequentially, one after the other. Therefore gradient boosting means that they predictors are assembled using the gradient descent optimisation technique.

12. MLFlow

MLFlow is a framework to manage the machine learning lifecycle, including experimentation, reproducibility, deployment, and the keeping of a central model registry.

MLflow is library-agnostic. You can use it with any machine learning library, and in any programming language, since all functions are accessible through a REST API and a command line interface (CLI).

MLflow's tracking URI and logging API, collectively known as MLflow Tracking is a component of MLflow that logs and tracks your training run metrics and model artifacts, no matter your experiment's environment.

13. LightGBM

LightGBM, short for Light Gradient Boosting Machine, is a free and open source distributed gradient boosting framework for machine learning originally developed by Microsoft.

14. MindsDB

MindsDB enables advanced predictive capabilities directly inside databases. Anyone who knows the basics of SQL can therefore build data models.

It works with pretty much all the main databases out there, from MySQL and PostgreSQL to MongoDB and Kafka, as well as with the main ML frameworks, including PyTorch, TensorFlow and Scikit-Learn.

15. OpenNN

OpenNN (Open Neural Networks Library) is a software library written in C++ that implements neural networks with an emphasis for advanced performance.

This library is probably less user-friendly that TensorFlow or PyTorch, but is constantly optimised and parallelised in order to maximise its efficiency in execution speed and memory allocation.

OpenNN is an open-source library developed by the company Artelnics.

15. OpenCV

OpenCV has become almost an industry standard when it comes to Computer Vision.

It includes several hundreds of computer vision algorithms, which play a major role in self-driving cars, robotics as well as in photo correction apps.

By using it, one can process images and videos to identify objects, faces, or even handwriting of a human. When it integrated with various libraries, such as NumPy, python is capable of processing the OpenCV array structure for analysis. It has C++, C, Python and Java interfaces and supports Windows, Linux, Mac OS, iOS and Android.

16. NLTK

NLTK (Natural Language Toolkit) is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) written in the Python programming language.

It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning.

17. spaCy

spaCy is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. It is published under the MIT license and, unlike NLTK, which is mostly used for experimenting, teaching and research, spaCy focuses on providing software for production usage.

SpaCy also supports deep learning workflows that allow connecting statistical models trained by popular machine learning libraries like TensorFlow or PyTorch through its own machine learning library Thinc.

18. MLPack

MLPack is a fast, flexible machine learning library, written in C++, that aims to provide fast, extensible implementations of cutting-edge machine learning algorithms, such as Hidden Markov Models, Clustering and Regression models, and Tree-based searches amongst others. MLPack provides these algorithms as simple command-line programs, Python bindings, and C++ classes which can then be integrated into larger-scale machine learning solutions.

It was built on top of the linear algebra library Armadillo by NumFOCUS, a nonprofit dedicated to supporting the open source scientific computing community.

19. Chainer

Chainer is an open-source Deep Learning framework written in Python on top of NumPy.

It the first Deep Learning framework to introduce the define-by-run approach. In this approach, you first need to define the fixed connections between mathematical operations in the network (for instance, matrix multiplication and nonlinear activations). Then you run the actual training computation.

It supports GPU training via CUDA/cuDNN using the numPy array compatibility library CuPy.

20. Caffe

Caffee is an open-source deep learning framework developed for Machine Learning. It is written in C++ and Caffe’s interface is coded in Python. It has been developed by the Berkeley AI Research, with contributions from the community developers.

This software has been designed keeping in mind the expressions, speed, modularity, openness and full community support to enable seamless creation of Deep Learning models.

Concluding strategy to try ML frameworks out

There you have them. The top 20 libraries to keep an eye on or try out for yourself this year in Machine Learning.

My advice, if you're looking to get your hands dirty with a ML framework this year, is the following:

1. Build first a few models with a high-level easy-to-use library that has plenty of tutorials around to follow and pre-built algorithms to experiment with, such as LightFM, TensorFlow or PyTorch. This will help you have fun and learn without worrying too much about complex environment setups. I'd stick with Python libraries.
2. Once you get a sense of how machine learning works and have built functioning tools, try playing around with maths frameworks like numPy, Pandas and Aesara to manipulate the data in your models and understand what's going on under the hood.
3. Get creative with powerful wrapper libraries: now you can 'go back up' and use higher level multi-purpose wrapper libraries like Keras and TensorFlow, which you can now mold to your needs, or specialised libraries that are better suited to develop more complex production-ready models.

Ultimately, however, when progressing your journey into machine learning, only you can answer the question of what are the top frameworks to learn and use. Make your selection based on your interests and the type of applications you want to develop, such as pure data crunching vs computer vision. If you don't have a clear picture of what you will be looking to learn and develop, then let your curiosity decide:

I have no special talents. I am only passionately curious. ─Albert Einstein

We wanted flying cars, instead we got 140 characters.

Peter Thiel

TL;DR

We have come full circle around in the Startup and Venture Capital world. Twice.

From the early days of semiconductor and computer pioneers in Silicon Valley to the internet startup boom of the 90s, when having a dot-com domain was often enough to receive venture funding. And from the post dot-com bubble burst, which saw the birth of the cloud infrastructure and first global software platforms, to today's inflated market driven by cheap capital chasing a host of me-too SaaS applications.

As this latter financial cycle comes to an end too, a new class of entrepreneurs and venture capitalists are needed to once again take bold bets and solve hard problems through breakthrough engineering and commercialised science.

How Venture Capital Made Its Name And Why It Matters

In the 60s and 70s, when modern Venture Capital started, investors made money backing risky semiconductor companies that gave Silicon Valley its name. As I reported in a previous article on the history of VC, the combination of post-war US Government incentives, late 20th century regulatory changes and investment firms' embrace of limited liability partnerships, helped the very first professional VC firms outperform public markets. Their strategy of giving first cheques to a portfolio of science-driven corporate spinouts and startup teams produced huge financial returns and helped create a host of world-leading technology companies such as Fairchild Semiconductor, Intel, Apple, Cisco, Genentech, and Juniper Networks.

Thanks to the returns generated by these investors, as well as the innovation created by these startup companies, a whole new host of funds launched to join the game. The ensuing VC industry growth fuelled the birth of personal computing in the 80s and the internet in the 90s.

When It Seems Too Good To Be True

The success of those engineers-turned-entrepreneurs, and the fringe investors who backed them, kept feeding the Venture Capital industry, which ballooned in size over the last decade of the 20th century.

The newly available capital and risk-taking approach helped create the technology infrastructure that gave us the internet as we know it, with companies like Nvidia, Amazon, eBay, Google and PayPal all being founded and receiving venture funding in the 90s.

As more and more investors flocked to the game, however, healthy growth turned into hype and mania. Thanks to a cheaper and cheaper internet infrastructure and to an exponential increase in available capital, the end of the decade saw the mushrooming of hundreds of new "internet startups" ─most of which not at all worthy of the money they received. It was the infamous dot-com bubble.

After a couple of years of seemingly never-ending party, the bubble crashed. Bankruptcies spread like wildfires across both private and public companies, the stock market lost billions in value, and Venture Capital took a step back.

The Software Reboot

Software is eating the world ─Marc Andreessen (2000)

At the turn of the century, former Netscape co-founder Marc Andreessen and his business partner Ben Horowitz were interviewed in “CRN: The Newsweekly for Builders of Technology Solutions” when they were running Opsware, then called Loudcloud. He boldly stated his belief that "software is eating the world" right in the midst of the market downturn, which would bottom out two year later, in 2002. This belief helped him grow and sell Opsware as one of the first cloud-computing businesses and drove his subsequent enter in Venture Capital by founding Handreessen Horowitz and investing in Facebook, Groupon, Skype, Twitter, Zynga, and Foursquare, among others. He reiterated his belief in 2011 in his famous "Why Software is Eating the World" essay, which, in hindsight, proved correct: from the hashes of the dot-com mania would raise new software-driven companies that would shape a new era of businesses "on the cloud".

As it turned out, whilst the dot-com crash wiped out plenty of businesses and overall investor returns, some endured on both fronts and quickly began thriving. Those that did shaped a new phase of the internet, powered by cheaper server hosting and new web frameworks and algorithms. The early pioneers that invented the tools to build and then actually launched these first platforms created a huge amount of value, both for their companies and for the investors who stuck to investing in this new digital service world.

Value meant returns and such returns fuelled another investment mania that was only partly cooled by the 2008 financial crisis. Instead, it kept drawing investors to the VC world to this date.

All That Glisters Is Not Gold

Capital got cheaper and cheaper, but also launching a SaaS Platform became easier and easier. The very internet pioneers of the 90s and early 2000s, in fact, helped build both an online cloud infrastructure, which allows developers to quickly and easily launch apps, and the frameworks and algorithms that people can re-use in their own software. Therefore, an ever bigger number of investors and entrepreneurs kept chasing the dream of building billion dollar companies through "asset light" digital platforms.

What is not obvious, however, is that when Facebook revolutionised social media and when Amazon launched AWS in the midst of the 2000s, they did not simply "took a business online". They, instead, solved a host of hard technical problems that gave them a tremendous edge over any potential competition. You might have heard that "Facebook was not the first social media" and that "Amazon did not invent the cloud". Yet most people simply ascribe previous failures to "market timing" issues. This view, whilst containing some truth, ignores the fact that having a business idea does not equal to having the right technology to deliver it and succeed with it. General Magic might have had the idea of the smartphone, but it was Apple that built it and profited from it. Facebook, for example, pretty much single-handedly took PHP to its highest level of serving speed and scalability, before ditching it and inventing React) and pioneering the development concept of mobile-first Single Page Applications, to create an online experience for millions of users that just never existed before. Amazon, on the other hand, launched AWS on the back of pioneering innovation in building reliable, scalable, cost-effective data centres and creating easily accessible services via APIs.

There is a substantial difference between creating a software infrastructure that both delivers a service AND solves hard technical challenges and a software that uses pre-existing technologies to take a business idea online. However, perhaps thanks to the "quantitative easing approach" taken by central banks and governments after the 2008 financial crisis and never left since, cheaper and cheaper capital kept flushing the market.

Investors and financiers have continued to jump on the Venture Capital bandwagon at all levels and with bigger and bigger funds raised for pure financial speculation. We are now at the point that me-too copycats startups in the same industry can raise money from different investors to build yet another "B2B SaaS platform to do X" or a "Digital Consumer Platform facilitating Y". Entire new "late stage" funds have now appeared, allowing companies to raise bigger rounds than IPOs and yet remain private.

The trending philosophy seems to be: "Why bother backing "risky" new technology makers, when you can take an existing business, deploy it online on the cloud with some re-edited open-source code, and ride the money train?

A Never Ending Party...?

For now, just as it happened in 1998-99, the system seems to be working. However, just as most ".com" businesses back then, the technology innovation generated by most of today's "digital platform startups" is incremental at best and nonexistent at worst. Most of today's startups, which happen to be the ones most investors currently chase, are riding the digital market train, rather than taking bold bets and building and commercialising new technologies to solve hard problems.

This fact, however, is driving up the cost of doing business for today's digital startups, as more and more money is poured into businesses with little to no technology differentiation amongst them and competition consequently increases.

Besides this capital inefficiency, proving that cheap does not always equal to efficient, there is also an increasing market risk. That is: when a new market crash comes along, with the consequent loss of consumer spending and investors shutting their wallets, how many of these platforms would have become a critical part of the world technology infrastructure and ensured their survival and future thriving?

In other words: how many of these low-tech startups will reveal themselves to be Amazons and Googles, rather than Webvans, Boo.coms and Pets.coms?

Finally, there is also a limit to how much exploitation the mere "digitisation" of existing businesses can bring. And the closer we get there, with more and more competition on top of this, the lower the value that this process can generate.

As columnist Dylan Tweney noted in 2011 in a piece ironically titled “Software is not eating the world":

If you’re concerned about the long-term shape of technology, software is only one dimension of many. (...)

You’ll pay Apple, RIM or Nokia for your phone. You’ll still be paying Intel for the chips, and Intel will still be paying Applied Materials for the million-dollar machines that make those chips.

Software is not eating the world. It’s merely riding on the back of an infrastructure leviathan, like a monkey on an elephant.

A (Not So) New Approach To Venture Capital

Of course, not all startup founders are out there to build yet another "platform to do X" using the very same tech stacks that are available to the rest of the world to potentially do the same. Also, not all VCs are out there looking to back "B2B SaaS" and "Digital Consumer" companies.

Sometimes hyped with fringe labels like "Deep Tech", these are actually the people that are needed today to build a brave new startup world. A new wave of startups commercialising entirely new technologies, which are both able to move us forward as a human race and help their creators survive an ever more likely new bubble crash. And just like in the 70s and 90s, they are mostly finance outsiders. Not the career VCs, MBAs, family office investors, or bankers who flock to where the money seems to be during any gold rush, but scientists escaping the academic or corporate world, technical exited founders, and engineers daring to both build and invest in what no one else dared to.

This is not just some kind of "moral" issue. As we've seen above, we can already see signs creeping in that a market downturn may be on the horizon. Moreover, all the investment mania into digital businesses with little tech differentiation amongst them has driven up competition and increased costs for these companies.

Without the competitive edge of superior technologies, modern startups won't be able to generate the value (and profits) that Fairchild Semiconductor, Intel, Apple, Cisco, Genentech, Juniper Networks, Amazon, or Google ultimately achieved. Hence a shift back to investing in high-risk science-based ventures may actually the only way for both entrepreneurs and VC investors to thrive as we approach the end of a cycle and the beginning of a new one. Plus: the market tells us that startups that actually build innovative new technologies, may not necessarily need insanely large amount of cash to succeed. Contrarily to some of the most funded startups of our age, who often end up fighting VC-funded marketing wars rather than investing in genuine R&D.

Ultimately, it goes back to what technology startups and venture capital were always meant to do: taking risky bets to build and then commercialise completely new technologies (at least partly) outside of the red tape of academic and state-led research.

When done properly, data shows that Venture Capital can magnify both business and R&D results.

Researchers from Harvard Business School showed that Venture Capital has been historically able to transform capital into new firms and innovations in a highly productive manner. The same pointed, for example, to a research from 2000 by Kortum and Lerner, that found that VC is 3 to 4 times more powerful than corporate R&D as a spur to innovation, and a 2009 one, by Kaplan and Lerner, which fond that roughly 50% of the “entrepreneurial” IPOs in the previous two decades were venture-backed, despite the fact that only 0.2% of all firms had received venture funding.

Conclusion: The New Frontier of Venture Capital

You have to be a technologist today, because there are so many different technologies. (...) So, you have to understand the technology. A person with a general business background would not make it in the venture capital business today.

─ Arthur Rock, one of the "godfathers" of modern venture capital

Venture Capital "done right" got us the silicon semiconductor revolution of the 70s and 80s, the internet infrastructure of the 90s, the cloud stacks and new web algorithms of the 2000s and 2010s, and is desperately needed today. As a matter of fact, we need VC at its best to deliver us better energy and climate-saving solutions, next-generation computing, gene-based life-improving medicine, new means of space exploration and exploitation, or even "simply" new ways for technology to deliver us from mundane as well as dangerous or heavy labour tasks, and make us cross those frontiers that we are currently unable to reach.

Breakthroughs in computing, space, robotics, semiconductors, energy, prosthetic and human augmentation, as well as food production and genetics have already started to break into the commercial space. It goes without saying that for those in Venture Capital who have both the technical expertise and risk-taking mindset required to invest early in these frontier technologies, a brave new world may just be opening up. Potentially bringing with it a value windfall not seen in a long time in the industry.

The journey to rediscover the whole history of Venture Capital starts right here in Europe, right now.

It starts with today's current landscape: Europe climbing to a strong 3rd place in VC investing, led by the UK. The US and China lead the way for the amount of capital invested, but the Old Continent is closing the gap.

In 2019 alone the amount of capital invested in European VC alone was $34.3B: on track to surpass $110B of capital invested since 2015 by this year.

Before 2014, however, money flowing into new ventures was hard to come by. Europe struggled for decades to go from $1B to about $5B of yearly invested capital, and then got stuck there for years.

The US, on the other hand, were racing ahead both in innovation and technology-related employment creation thanks to a solid Venture Capital industry. In terms of modern history, they are effectively the fathers of today's VC game and remain clear leaders to this day.

This was not always the case.

The Official Beginningsof VC

Harvard Business School professor Georges Doriot is generally considered the "Father of Venture Capital". He started the American Research and Development Corporation (ARDC) in 1946 and raised a $3.5 million fund to invest in US companies that commercialized technologies developed during WWII.

Georges Doriot

ARDC's first investment was in a company that had ambitions to use x-ray technology for cancer treatment. The $200,000 that Doriot invested turned into $1.8 million when the company went public in 1955.

Venture Capital was sought by risky startups needing lots of up-front cash, whether for research and development or for essential leaps in scale. Such financing seemed especially suited to proprietary technology, which was expensive, hard to seed into the market, and yet, if things went right, extremely lucrative.

Official finance history regards Fairchild Semiconductor as the first technology company to receive VC funding. It was funded by east coast industrialist Sherman Fairchild of Fairchild Camera & Instrument Corp.

Fairchild Semiconductor (1957)

Arthur Rock, an investment banker at Hayden, Stone & Co. in New York City, helped facilitate that deal and subsequently started one of the first VC firms in Silicon Valley: Davis and Rock. Davis & Rock funded some of the most influential technology companies, including Intel and Apple.

The Unofficial Beginnings of VC

In V.C.: An American History, another Harvard Business School professor, Tom Nicholas, proposes instead that whaling was the first industry where what we now call venture capital appeared: collecting large pots of money and using it to invest in young ventures, in the hope of guiding growth and generating huge returns.

Dispatching a whaling voyage would cost between twenty and thirty thousand dollars, a small fortune in the mid-nineteenth century, and an industry emerged to get these expeditions off the dock. Specialised agents in whaling-industry towns invested their own money, pooled cash from rich investors, did due diligence, and worked with captains to develop winning strategies and to plot uncrowded routes.

Whaling Vessel

In most cases, their efforts were fruitless: data from a couple of whaling ports in Massachusetts in 1858 suggest that fully two-thirds of returning expeditions were unprofitable; another study found that a third of the whale ships in the New Bedford fleet never made it home. A lucky outing, though, could return with a hundred and fifty thousand dollars in goods, a fortune several times the outlay, and for many investors this was enough to justify the risk.

In the mid-nineteenth century, New Bedford, Massachusetts, was the "Silicon Valley" of the whaling world and the richest city per capita in the United States --- if not in the world, according to one 1854 American newspaper. The US whaling industry grew by a factor of fourteen between 1816 and 1850, driving wealth, innovation and worldwide prominence for a country, the USA, that was only born less than a century before.

In Pursuit of Leviathan, a classic text on whaling economics, uncovers countless examples of innovation that both empowered and were empowered by this "VC approach" to whaling. They chiefly revolved around 4 areas.

A whale hunt, Courtesy: Nantucket Historical Association

First and most broadly, Americans sailed bigger and better ships guided by smarter ocean cartography and more precise charts. Second, a series of tinkering with harpoon technology led to the invention of the iron toggle harpoon. Third, innovations in winch technology made it easier to pull in or let out large sails, reducing the number of skilled workers needed to man a vessel. Fourth, whale captains were innovators in employee compensation. In the lay system, "every member of the ship's company from captain to cabin boy signed on, not for a wage or piece rate, but for a predetermined percentage of the value of the product returned," the Leviathan authors write. Savvy captains of the whaling barques were keen to aligning company interests, not unlike startup entrepreneurs today using stock options.

The individuals who organised these whaling voyages, the whaling agents, functioned much like venture capital firms of today. Although they raised most of their money from wealthy individuals, there are many records of blacksmiths and shopkeepers investing their savings with agents, in hopes of healthy returns. In pooling the capital of many members of the community and allocating it across multiple risky ventures, the agents filled an important role, not only for financing the voyages, but also for diversifying the risks that their investors faced.

Although there are some notable differences between the VC/startup and the agent/lay model, the latter was a clear preview of the stock option/venture capitalist structure of today.

The European Precursors

Venture Capital, so the story goes, was born and grew up as an "American thing" of the 20th Century. At first mainly funded by banks located in the Northeast, then moving to the West Coast after the growth of the tech ecosystem.

Tom Nicholas' book seem able to convincingly trace the industry roots before the beginnings of equity investments, all the way back to the 18th Century. Yet, it still looks like an all-American affair.

Was that the ultimate beginning?

Not quite.

Fra Angelico ─ St. Nicholas with the Emperor's Envoy and the Miraculous Rescue of a Sailing Vessel

Raymond De Roover, a Harvard historian, published in 1963 his classic The Rise and Decline of the Medici Bank (1397--1494), which opens with the statement that "modern capitalism based on private ownership" was invented by Italian merchants and bankers, by far the most active businessmen in the Middle Ages. Joint stock companies did not exist until the 1600s, "but the Medici Bank had foreshadowed the holding companies in certain respects."

The Medici Bank of Florence was the most important financial institution in 15th-century Europe. At its peak, the Medici Bank was the chief bank for the Roman Catholic curia, and it had branches in the major cities of Italy, as well as in London, Lyon, Geneva, Bruges, and Avignon.

It operated like a true investment bank of today's age, as well as like a merchant corporation with its own Venture arm.

The wool and cloth industries for example were the export mainspring of the Florentine economy in the 14th and 15th centuries. In 1402, the Medici Bank invested 3,000 florins, nearly one-third of its original capital, to finance a Medici family partnership to produce woollen cloth. In 1408, they "seed invested" a second and more successful shop of the same kind and some time later they invested to back a new Florentine silk workshop. The Medici further diversified their risk by engaging in the trade of a large number of commodities that included wool, cloth, alum, spices, olive oil, silk stuffs, brocades, jewellery, silver plate, and citrus fruit.

Marinus van Reymerswaele --- Money Changers

Their "merchant ventures" aboard galleys and ships were in many ways similar to the whaling expeditions of 18th and 19th Century America. Vessels had to be filled with goods, captains had to be paid and salesmen needed to reach international markets and fairs and try to sell their goods there. There was no guarantee this process would be successful, yet if it did it normally returned handsomely to both the salesman and the investor-merchant.

The Medici Bank was organised as a partnership, with the Medici family as the largest investor in the parent company. The parent company was the largest investor in the branch partnerships (often "limited liability partnerships") and functioned like a modern holding company. Managers' interests were kept aligned with those of the parent/investor by being paid a share of the profits as opposed to a fixed salary.

Bust of Giovanni di Bicci de' Medici by Romeo Pazzini

The founder of the Bank was Giovanni Di Bicci De' Medici, whose father was neither rich nor successful in business. His fortune begun by being admitted as a clerk in a distant cousin's bank, where he eventually rose to the position of branch manager in Rome. When his relative retired in 1393, Giovanni and his partner, Benedetto di Lippaccio de' Bardi, invested their own savings as well as externally raised capital into the business and took over the Roman branch. In 1397 they moved their business to Florence where Giovanni officially incorporated as the Medici Bank ("Banco de' Medici").

By the time of his death, Giovanni had accumulated enough wealth and power through his bank to be as important a figure as Italian royals, cardinals and politicians of his time. He was succeeded by his son Cosimo, who continued to grow the Medici Bank into the largest banking house of its time.

After Cosimo's death, his son, Piero, and his grandson, Lorenzo, had a much less steady hand on the branch managers and gradually lost their grip on the banking empire. The Bank eventually failed in 1492, when Lorenzo's son was ousted from Florence. By then, however, the Medici had contributed to the economic and artistic development of Florence into the craddle of Europe's Renaissance. Their championing of financial innovation, marchant and industrial venture endeavours, and artistic patronage brought about a time where humanity advanced significantly with a burst of new ideas, new inventions, and new art.

Financial Innovations, Arts and Venture Capitalism

In finance and business, the Medici perfected and popularised modern double-entry accounting. The method of double-entry bookkeeping works on the equation that 'Assets = Liabilities + Equity'. It meant recording both credits and debits, for an easier overview of what money the business has, and where. It helped bankers and merchants keep a more accurate account of their financial decisions and allowed them to control their portfolio of subsidiaries, merchant ventures and investments.

They also helped modern lending and banking develop across Europe through instruments such as the Letter of Credit and the Bill of Exchange. These agreements between banks (often different branches of the very same Medici Bank) involved the buyer's bank guaranteeing to pay the seller's bank at the time goods/services are delivered. They would be authorised to receive pounds in the London branch, for example, at 40 pence to the florin exactly 90 days after a deposit in Florence. The London branch of the bank would then turn around and find someone wanting to purchase florins in Florence, but at the rate of 36 pence to a florin (currencies traded in different rates home and away). This little difference of 4 pence per florin gave the cunning Medicis a 22% annual return. In the eyes of the contemporary theologian, this was a currency exchange rather than a sin, absolving them of the judgement of God, whilst making a tidy profit.

Lorenzo il Magnifico

In practice, they also invented the Middle Class. Before them, there was only distinction between royals and commoners; now, an extra class inserted itself into the economy. The Medici effectively became rulers of the state of Florence by leveraging economical and business power, even though they did not come from an aristocratic background.

The Medici also used patronage to foster talent. They patronised some of the most brilliant artists of the Renaissance, including Bernini, Michelangelo, Leonardo Da Vinci, and Botticelli. The Medici took calculated monetary risks to sponsor genius, which paid back handsomely when statesmen, popes and dignitaries exchanged favours and business opportunities to have one of these artists work for them.

Cosimo De' Medici

The Florentines (and especially the Medici) also looked to different cultures and the past for inspiration. They dispatched emissaries far and wide in search of prised ancient Greek and Roman manuscripts. In 1444, Cosimo de' Medici founded the first public library in Florence and gifted it a huge number of books and manuscripts. This too was an investment that helped his family raise to its privileged position of leadership in the city. He hand-selected those individuals who were given access to this laboratory of learning, and, through this social dynamic, he actively shaped the politics of the state.

The Medici were not the only merchant-bankers of their time to sponsor artists, seed new businesses, invest in entrepreneurial managers and take calculated bets in risky trade ventures or political sponsorship. They were the ones that took them the farthest, though.

Thanks to their business dealings and private sponsorship, they did not only succeeded at becoming one of the most powerful families of Renaissance Europe, they also empowered others (from artists to merchants and entrepreneurs) to innovate, trade and effectively take Europe out of the Dark Ages.

The Venture Capital Rebirth

The capitalist and the proto-VC traits that emerged in merchant-bankers like the Medici effectively turned into state-sponsored mercantilism and colonialism in the 16th Century.

Credit was then the chief means of financing business ventures and the state played a main role in most large-scale enterprises.

It would take over 200 years for Adam Smith's works and the industrial revolution to plant the seed for a new wave of venture-based capitalism, which would still, however, rely on credit way more than equity investing.

The only exception to this was the American whaling industry, which, as we saw, worked similarly to Florentine bankers' partnership ventures or to modern Venture Capital.

This was because the whole finance world had actually regressed to a state where it was difficult for external agents to track the internal dealings of a company due to lack of structured information systems. Also, the concept of limited liability had fallen out of fashion and most businesses carried unlimited liability for its stakeholders.

American office in 1900

This all changed when limited liability became the norm for corporations --- thanks to an 1811 law by the state of New York. It was soon adopted throughout the US, then Great Britain, and at a later stage by countries around the world. On the other hand, establishing a company's value and the monitoring of its operations and finances took the whole 19th century to improve to standards that could make equity investments meaningful. The cash register was not invented until 1879 and it was only at the beginning of the 20th century that Henry Goldman (the son of the 'Goldman' in Goldman Sachs) found a way to underwrite securities for companies that didn't own tangible assets of substantial value, such as retailers and manufacturers. His idea? Recording and evaluating their earning power.

Before World War II, therefore, banks and the public would begin to buy shares in or lend money to companies with tangible assets or a recurring revenue derived from a retailing business.

The problem is that technology-focused entrepreneurial ventures didn't fall into either category. They couldn't borrow from banks because their business model was unknown and they still had everything to prove. And they couldn't raise capital from the public because no financier could value them.

Lionel Pincus --- a precursor to Angel syndicate investing

This gap led to the emergence of private equity: because it was so risky, technology ventures were forced to rely on wealthy individuals. In some cases those were syndicated by a merchant banker, who co-invested with his clients. But those deals usually failed to generate enough money to finance very ambitious ventures --- except if they were carried out by exceptional financiers such as Warburg Pincus' Lionel Pincus.

By the late nineteen-twenties, one per cent of American families earned nearly a quarter of the United States' income and held half of its wealth. Many set up investment vehicles, some specialising in high-risk offerings. Laurance Rockefeller, a grandson of John D. Rockefeller, began putting "venture" money into untested aviation companies.

In V.C.: An American History, Tom Nicholas calculates that he could have made more in the stock market, but Rockefeller was adamant. "Venture capital endeavours are not for the impatient," he remarked. "Nor are they for widows and orphans or people who cannot afford to lose."

The US Government's Pivotal Role

In Secret History of Silicon Valley, author Steve Blank notes how the Roosevelt administration decided to tackle the issue of outsmarting German and Japanese military technologies with an unconventional approach: they gave up on enrolling researchers in the military in favour of allocating public funds to the best universities and technology companies in the country.

Claude Shannon at Bell's Secret Labs

At Harvard University and MIT in Cambridge, and at Columbia University in New York City and Bell Telephone Labs, secret research laboratories were inundated with public money and crowded with the best scientists in the country, all with one mission: to invent the new, cutting-edge technologies that would help the US regain the upper hand.

After the end of World War II, a new challenge was to convert the US industry from manufacturing weapon systems to manufacturing consumer goods.

A few wealthy families created their own investment firms to seize the opportunities brought about by the end of the wartime economy. The Whitneys founded J.H. Whitney & Co., the Rockefellers founded Rockefeller Brothers Inc. (later Venrock) and the Phippses founded Bessemer Securities. All those took investing in technology companies to the next level.

With those new family-funded firms, private equity and Venture Capital became more professional. Instead of acting as co-investors as did the old-fashioned merchant bankers, professional management teams took charge of sourcing opportunities, evaluating risks, and negotiating investment deals on behalf of their shareholders.

Modern Venture Capital

Our journey through history has finally circled back to the "official beginning" of Venture Capital: the post-war world of Georges Doriot's ARD (1946) and of Davis and Rock (1961).

Despite promising financial results, ARD ultimately ceased to exist due to its conflicting goals: it aimed both at sustaining financial performance and rebuilding the American economy to create jobs for the veterans.

Meanwhile, the West Coast was still managing to make it without venture capital. Frederick Terman, who headed the Harvard Radio Research Laboratory during the war, was back on the West Coast as the provost of Stanford University, which he intended to turn into a leading university in the sciences. To achieve that goal, Terman made it a priority to attract the budgets that the Department of Defence now allocated to advanced research in US universities.

Vannevar Bus, the father of advanced military research in US universities

In all practical terms, it was military procurement, both by the government and weapons manufacturers, that enabled the founding of the first entrepreneurial ventures in what was to become Silicon Valley.

To stimulate private investment into innovative new ventures, in 1958, the US Congress passed an act designed to encourage small-business investments and loans. If a small-business investment company could raise a hundred and fifty thousand dollars, the government would match those funds and lend more at a low rate, bringing the fund to at least four hundred and fifty thousand dollars --- nearly four million in current dollars.

In V.C.: An American History, we find quotes of early venture capitalists saying that they wouldn't have got into the game if it hadn't been for federal incentives. Venture Capital thus transformed from the pursuit of a few ultra-wealthy scions into a true profession.

In 1978, the Revenue Act was amended to reduce the capital gains tax from 49.5% to 28%. Then, in 1979, a change in the Employee Retirement Income Security Act (ERISA) allowed pension funds to invest up to 10% of their total funds in the industry.

These changes, plus firms' embrace of limited liability partnerships, brought financial growth to the community that the incentives had founded. By this time, professional venture-capital portfolios began to repeatedly outperform the public markets.

As personal computing reached consumers for the first time, notably with the release of the Apple II, Venture Capital grew tenfold in the following years.

With growing returns from a now established VC sector and technology spreading into everyone's home, US investors became overtly bullish. Towards the end of the 90s, a craze around technology companies turned a growth period into a true and sudden boom. It was the now infamous 'dot com' bubble.

The Macintosh Team, 1984 (Left to Right) George Crow, Joanna Hoffman, Burrell Smith, Andy Hertzfeld, Bill Atkinson, Jerry Manock. Credit: © Norman Seeff

According to some estimates, funding levels during that period peaked at $119.6 billion. But the promised returns did not materialise as several publicly-listed Internet companies with high valuations crashed and burned their way to bankruptcy.

Despite the burst, VC had by then become a truly established and mature vehicle. The industry recovered and in the following decade it would contribute to the rise of household names like Stripe, Facebook and Netflix as much as of less know billion dollar innovators such as Palantir, Bloom Energy, and Okta. Meanwhile, 48% of dot-com companies survived through 2004, albeit at lower valuations.

As growth in the technology sector stabilised, companies consolidated; some, such as Amazon.com, eBay, and Google, gained market share and came to dominate their respective fields. The most valuable companies are now in the technology sector.

Total US Venture Capital Investments

Venture Capital continued to gain pace in the US and to produce both new technologies and even entire new economies. In the aftermath of the 2007--8 financial crisis, for example, startups like Airbnb, Bolt, Careem, Gojek, Grab, Lyft, and Uber grew out of bold VC financing to create multi-billion dollar value in a worldwide economy still shocked by the fall of the public markets. Their rise marked the beginning of what's become known as the "sharing economy" phenomenon.

Number of Unicorns Founded by Year (2005--2018) (CrunchBase)

Since its post-war inception, modern US Venture Capital investors seeked profits by backing innovators in evolving industries, often at the ground level, hedging the risks associated with mature companies ripe for disruption. Hence, the overwhelming majority of deals financed by venture capitalists today are in the technology industry. But other industries have also benefited from VC funding. Notable examples are Staples and Starbucks, which both received venture money. Venture Capital is also no longer the preserve of elite firms. Institutional investors and established companies have also entered the fray. For example, tech behemoths Google and Intel have separate venture funds to invest in emerging technology. Starbucks also recently announced a $100 million venture fund to invest in food startups.

With an increase in average deal sizes and the presence of more institutional players in the mix, venture capital has matured over time. The industry now comprises an assortment of players and investor types who invest in different stages of a startup's evolution, depending on their appetite for risk.

What about the rest of the world?

Despite overseeing the development of ancestral form of Venture Capital and Private Equity, Europe never developed a proper VC industry in modern times. As we have seen, Venture Capital in its contemporary form originated in the United States. Since then, American firms have been the largest participants in venture deals with the bulk of venture capital being deployed in American companies.

However, spurred by the American example, other economies decided to promote a professional enterprise-backing industry in their own territories. Therefore, over the last two decades, the number and size of non-US venture capitalists have been expanding.

2017 GLOBAL VC FUNDING --- Data from CB Insights and PWC

China

As an emerging economy, China started to develop its VC industry in the 1980s, though at a very slow pace. In The rise of venture capital centres in China: a spatial and network analysis, authors Pan and Zhao report that it was not until the late 2000s when the industry experienced a fast hyper-growth that catapulted it to being the 2nd largest VC market in the world.

Not surprisingly, governments were highly involved in setting up early VC firms in China. For instance, the National Research Centre of Science and Technology for Development was established in 1984 aimed at developing high-tech industries. More venture capital firms backed by state and local government were set up in the 1990s, most of them in national high-tech industry parks across the country. However, the development of domestic VC was stifled due to the lack of divestment opportunities, with no Nasdaq-like segment on the Chinese stock market.

Andy Yan Yan, chief managing partner of SAIF Partners, a Beijing fund created by Cisco Systems and Softbank Group

In contrast to domestic VC firms, foreign VC firms in China experienced rapid growth in the late 1990s and early 2000s, highly related to the strong stock market performance in the US. Many foreign VC firms that invested in Internet-related portfolio companies successfully exited via IPOs on overseas stock exchanges.

However, domestic VC firms had no access to IPO channels on either foreign or domestic stock exchanges. Many successful listings on overseas stock exchanges motivated China's policy makers to launch its own Nasdaq-like stock market segment. During this period, active foreign VC activities were extremely concentrated in several metropolitan areas in east China.

China planned to establish Nasdaq-like stock markets from the late 1990s. It was expected that the second board would be set up in Shenzhen in 2002. However, the bust of the dot.com bubble in the US's capital market delayed this development. In 2004, the SMEB was finally launched, providing an opportunity for domestic VC firms to divest in their portfolio firms through an IPO.

State-owned VC firms have become dominant in the rapid growth of the domestic VC industry in China since about 2004. In particular, China has evolved a tradition in setting up government-financed VC firms to support tech startups. In the meantime, privately-owned VC firms were also emerging, including spin-offs and spin-outs from established VC firms, stock-exchange listed firms and those set up by former government officials.

VC investments increased in 2006 and reached the first peak in 2007. After one year's stagnation, they started to grow again from 2009 at an unprecedented pace. Meanwhile, the number of VC-backed IPOs grew from 2005 and peaked in 2011. By the end of 2013, there were 516 publicly listed firms that were backed by VC firms. In China, VC-backed firms are highly concentrated in coastal regions, while less presence in the central and western part of the country.

Shenzen, prominent Hi-Tech Hub in China

Given the composition of the VC-backed listing firms, the institutions influences are more important than technology capacity in shaping the spatial and network patterns of VC activities related to domestic IPOs. VC in China (and in general in Asian economies) have been thought to be different from that in western economies due to th e different regulatory environment. In particular, the regulation of IPOs and the active involvement of state in capital markets and VC investments probably constitute the key institutional context to understanding domestic VC activities in China. The ongoing interests of governments of different levels in providing funds for VC firms might continue to shape the geography of VC activities in the country, just like it continues to shape the growth of the whole Chinese economy in general.

After 4 years of hyper-growth, the Chinese VC industry corrected in in 2019 and looks set for a period of slower growth and increased maturity

India

The first professinal venture capital firm known to be started in India was ICICI, wholly owned by the ICICI bank. They started giving funds to companies in the late 2000s as risk capital by modelling themselves in the style of US VCs. Their success attracted other players, both domestic and international.

2019 was a milestone year for the Indian VC industry, with $10 billion in capital deployed, the highest ever and about 55% higher than 2018.

Foreign VCs with an office in India still account for most of the capital raised and deployed in the Indian ecosystem

Accourding to the India Venture Capital Report 2020 by Bain & Company, the Indian VC industry passed through three distinct phases over the past decade:

• Between 2011 and 2015, the industry experienced rapid activity growth (albeit off a small base) to support an evolving start-up environment. During this phase, multiple VCs entered and became active participants in India's economy for the first time.
• This initial, almost euphoric, phase was then followed by moderation between 2015 and 2017. The lack of clarity regarding exits made investors more cautious, and that shifted the focus to fewer and higher-quality investments.
• Over the past two years, however, the VC industry in India has been in a renewed growth phase, buoyed by marquee exits for investors, such as Flipkart, MakeMyTrip and Oyo; strong start-up activity in new sectors, such as fintech and software as a service (SaaS); and market depth in e-commerce.

Europe and Israel

While North America has historically dominated the modern Venture Capital industry, and despite Asia accounting for the greatest proportion of growth capital secured over the years, Europe's overall shares of venture and growth capital have increased recently in line with investors' growing interest in the region.

Attendees at SuperVenture 2020, source Sifted.eu

However, despite being similar to the US in terms of GDP, possessing a larger population, and pioneering a proto-VC industry already in the 15th Century, Europe still sits very far behind the US in terms of venture financing.

So, why has a comparable VC industry not grown in such a strong economy?

By analysing data from the late 2000s, it is possible to notice certain differences between the US and Europe. In particular, securing growth financing has historically been significantly harder in Europe compared to the US.

Despite being the cradle of capitalism and having witnessed a certain level of early stage private investment into private firms for centuries, Europe never developed a professional VC industry that could support long term company growth.

Due to limited late-stage funding, European companies have been historically forced to cut growth, reduce expense, and become profitable. Meanwhile, well-funded US counterparts continued to invest their sizeable funds into product and sales, allowing them to dominate their markets. Consequently European startup firms never reached their potential, whilst VC-backed companies in the US matured in the tech giants we are all familiar with, such as Apple, Amazon, Google, Facebook or Airbnb.

In short, the lack of mature VC players able to raise large funds and fund companies' growth-stage expansion prevented Europe from developing a mature VC industry in modern times. This lack of Venture Capitalists may in turn be explained by the lack of a central government in Europe that could mirror the funding and tax incentives for R&D and VC, which the US government implemented after World War II. Besides, trade barriers in the form of national frontiers as well as currency, language and cultural disconnect, made it harder for local startup companies to address a large user-base than it might have been for American startups.

With the creation of the European Union in 1992, the situation began to change. Non-English speaking children across Europe began to learn and practice English as a second language and trade barriers effectively disappeared. In 1994 the EU established the European Investment Fund (EIF), a European Union agency for the provision of finance to small and medium-sized enterprises through the investment in venture funds. In 2002, most of Europe adopted the Euro as a single currency. Over time, the EIF encouraged the development of national funds throughout the EU, which in turn joined the EIF in spurring the development of new VC firms.

As these new entities developed in the early 2000s and their track record became established, they were able to raise increasingly large funds to back private companies with larger cheques and a more patient growth capital. This allowed tech entrepreneurs in the late 2010s to "dare more" and focus on growth as opposed to rush for a quick acquisition or early profitability.

In 2008--2015, Europe-focused venture capital and growth funds collectively failed to raise more than €10bn in any year; however, 2017 marked the second consecutive year in which this figure was surpassed, and the industry has since grown not only in amount of money invested but also in the number of billion-dollar companies produced (so called "unicorns"). Over the years, venture capital investment activity in Europe has increased, and twice as many transactions were completed in 2016 than in 2009.

Just like in the US, not all the European states withnessed the same rate of VC growth and results. Mirroring Silicon Valley, London became the epicentre of the European Venture Capital growth, whilst other smaller centres developed across the continent and continue to accelerate their growth.

Number of VC-backed & non-VC-backed $1B+ European tech companies per year (cumulative) --- Source: Dealroom

In the years 2014--2019, capital invested in Europe has increased by 124%. Since 2018, this number has grown over 39%. There are now at least 174 European tech companies that have scaled to a valuation of more than $1 billion. Before entering this decade, that number stood at just 13, meaning Europe has seen over 13x increase in the number of companies scaling to this milestone.

The European VC growth also attracted oversea investors, which fuelled a further increase in capital available to startups and VC funds. Meanwhile, the number of exits and their size continued to increase. This meant that many former entrepreneurs could inject their new wealth back into the system, as well as raising further funds from private investors. A prime example of this is the London firm Atomico, founded by Skype's cofounder Niklas Zennstrom, which announced a new $820m fund just before 2020.

Overall, Europe seems on track to enter the 2020s with a stronger ecosystem with both room to grow and fundamentals to fuel such expansion. According to Crunchbase's 2019 "Key Trends" forecast, the European ecosystem could easily grow 4 to 5 times before reaching the levels developed in the US over the last century.

Geographically positioned near Europe, on the shores of the Mediterranean sea, Israel too deserves a note when discussing the development of Venture Capital. Israel's venture capital industry was born in the mid-1980s and has rapidly developed since. The first Israeli Venture Capital fund, Athena Venture Partners, was founded by Major-General Dan Tolkowsky, the past Chief of Staff of the Israel Air Force.

The success of the Venture Capital industry in Israel continued with Yozma (Hebrew for "initiative"), a government initiative in 1993 offering attractive tax incentives to foreign Venture Capital investments in Israel and promising to double any investment with funds from the government.

CyberTech in Tel-Aviv has become one of the world's most famous cybersecurity and hi-tech conferences of our age due to the strong local ecosystem

As a result of their efforts, Israel's annual venture-capital outlays rose nearly 60-fold, from $58 million to $3.3 billion, between 1991 and 2000. The number of companies launched using Israeli venture funds rose from 100 to 800. Israel's information-technology revenues rose from $1.6 billion to $12.5 billion.

By 1999, Israel ranked second only to the United States in invested private-equity capital as a share of GDP. It also led the world in the share of its growth attributable to high-tech ventures: 70 percent. According to the OECD, Israel is also ranked 1st in the world in expenditure on Research and Development (R&D) as a percentage of GDP.

Investments volume in Israeli startups grew by 140% during 2014--2018 and M&A transactions from US buyers provided for plenty of exit opportunities for Israeli VC investors. In particular, Artificial Intelligence (AI) is the field that raises most investments (17%) among all high technologies in Israel, which has become famous for producing hi-tech startups in fields such as Cybersecurity and applied Machine Learning.

With over 60 companies listed on the NASDAQ stock exchange as of 2020 and the highest "VC investment per capita" in the World, Israel managed to develop a significant Venture Capital system that continues to show strong returns for investors and to promote the development of innovative tech companies.

Israeli and European VC ecosystems keep growing and are increasingly connected

Conclusions

Whether it be cloud computing, machine learning, or Artificial Intelligence, emerging technologies are transforming many industries.

As we delve deeper into the 21st Century, we are all faced with challenges such as climate change and the adapting to a society where AI, genetic editing, remote working, self-driving cars and super-fast quantum computers will create both threats and opportunities.

Venture Capital investing has been for centuries the way wealthy families, corporations, institutions and even nations took charge of the "innovation and disruption game" rather than being a passive receiver.

In 15th Century Italy, Merchant-Bankers rising to powerful economical and political positions, like the Medici, developed the habit of serially investing in new businesses and in high-risk / high-reward trade ventures. They did so to both strengthen the economical power of their states (as well as their own families) and to diversify their asset allocation in a world that was changing fast. Wars, the Black Death epidemic, and a constantly changing economical and political landscape meant that hedging one's position from threats as well as capturing the opportunity offered by social, economical and technological disruption were one and the same thing.

In 19th Century America, investing in whaling ventures and their enterprising crews allowed an entire industry to develop and to reach a significant position in the global economy of the time.

After WWII, thanks to government incentives and the desire of wealthy industrialists to capture post-war opportunities, the US developed a systematic approach to Venture Capital investment for innovative enterprises. This helped the modern VC system to grow and was instrumental in the creation of many of today's highest valued companies, such as Apple, Alphabet, Intel, Amazon, NVIDIA and Facebook.

Silicon Valley --- the most advanced ecosystem of our time for startup enterprise and tech innovation is fuelled by Venture Capitalists' investments

When the emerging Asian economies of India or China grew their way into the aspiring superpowers they are today, they too decided to develop Venture Capital and spur innovation and private investment into new technologies and businesses. The results of such policies allowed the rise of tech giants in China, such as Alibaba and Tencent, or India's new unicorns such as Ola and One97.

Modern Europe did not managed to follow suit until the EU was formed and a centralised approach to promoting Venture Capital encouraged regulative changes, tax incentives and national programmes similar to what the US did post-WWII. Today Europe, including the now EU-exiting Britain, is raising fast to a position where a more mature VC ecosystem drives both high returns for investors and the development of new technologies and innovative businesses.

Due to its proximity to Israel, which developed a significant VC industry of its own, the two systems developed close ties and together account for a significant and growing slice of the worldwide VC market.

Where is this, now global, industry heading to next?

Venture Gapital has generated compelling returns relative to public markets, both in recent years and over long-term time periods.

As of June 30, 2019 - Global Venture Capital Periodic Rates of Return (%) (Cambridge Associates)

Meanwhile, the return and risk profiles of VC investing have changed, as today's market is not the same as 20 years ago. According to data collected by Cambridge Associates, broad-based value creation across sectors, geographies and funds means success is no longer limited to a handful of (often inaccessible) fund managers. Besides, top returns are not confined to a few dozen companies in Silicon Valley. In today's VC ecosystem, new and developing fund managers consistently rank as some of the best performers, whilst an increasing numbers of startup ecosystems throughout the world have the capacity to sustain mature and profitable Venture Capital investments.

Ranking, as of June 30, 2019 - US VC Funds by Vintage Year - Based on Net TVPI (Cambridge Associates)

Yet, when put into context, the amount of money raised in VC still represents a tiny fraction of the market value of the industries being disrupted by many venture-backed companies, and a fraction of the total addressable markets of emerging business categories being created by VC.

In 2018 Global VC stood at $340 billion net asset value (NAV), which was less than 0.5% of the $85 trillion in global equity valuation.

London's Old Street Roundabout ---One of the new global VC hubs that aim to rival the US Silicon Valley in the 21st Century

Even from our UK perspective, at Silicon Roundabout Ventures in London, we cannot but notice how an increasingly globalised VC and startup ecosystem is key to unlock opportunities out of 21st century challenges...

Just like the Renaissance merchant-bankers, the American whaling communities, and the post-war US venture capitalists, today's VCs are called to scout and back inventive entrepreneurs that can turn the global challenges of our time into new opportunities.

Originally published on SiliconRoundabout.tech on March 16th 2020 [edited 02/01/2022]

An embedded link has its content appear as part of a post and supplies a visual element that encourages increased click-through and engagement.

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<iframe width="560" height="315" src="https://www.youtube.com/embed/oavMtUWDBTM" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>

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Conclusion

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