Biggest AI Funding in Europe: Behind the Scenes of Aleph Alpha’s 500M USD Series B
We’re incredibly excited to double down and participate in the $ 500M Series B funding round of our portfolio company Aleph Alpha. The oversubscribed round was co-led by Innovation Park AI (IPAI), Robert Bosch Ventures, and Schwarz Group, followed by Hewlett Packard Enterprise (HPE), SAP, Christ&Company, Burda Principal Investments as well as existing investors.
It’s not only the biggest AI funding round ever in Europe, but also a very special setup, given the partners involved. In this post, I’d like to share the full story of how we identified the next generative AI champion with the help of AI, our initial belief and why we invested, what happened within the 2.5 years until today, and most importantly, why we doubled down in such a special funding set up, and what the future holds. Let’s dive in!
Leveraging AI to Find the Next AI Champion
Following my Ph.D. in “Machine Learning and the Value of Data in Venture Capital”, I conceptualized and productized the first version of what we call “Earlybird’s EagleEye”. Between then and early 2022, however, I was the only person using it.
Why? Because I understood early enough that blockers for the adoption of data-driven approaches in the investment world would not be technical but cultural in nature. You need top-down support from the General Partners and only get one chance to change the workflows of your investment team. If you fuck it up, you won’t get another try. Therefore, we developed EagleEye mostly behind the scenes and only rolled it out at a more mature stage than you would typically do. Contrary to the “zero-to-one” approach of fast iterations and short feedback cycles.
While I sourced an increasing number of opportunities during the beta test phase, none made it through our investment committee. None, except for Aleph Alpha. We sourced the company through their Handelsregister (=German public register) registration in 2019 but didn’t prioritize it until the end of 2020 when sufficient online data became available to improve their “likelihood of success” score and highlight it in our system.
Early Conviction: Why We Led the € 23M Series A
I vividly remember my first call with Jonas, the CEO and Co-Founder of Aleph Alpha, in spring 2021. We immediately connected on a deep content level and shared a lot of theses around the future of AI. The follow-up call with his co-founder Samuel perfectly complemented my first impression. From initially an hour scheduled, we ended up discussing and playing around with their models all afternoon.
I was deeply impressed by both of them, their profound expertise, entrepreneurial drive, and unique positioning in a new, potentially unlimited market. However, Generative AI was not a thing back then and the founders’ ask for € 20m+ without any commercial traction didn’t make the discussions within our partnership easier.
After some back and forth, a lot more research and references, tough negotiations, and syndication efforts on all sides, we eventually ended up leading Aleph Alpha’s € 23M Series A round in June 2021. Only 6 months after their first funding round end of 2020. I outlined our initial investment ratio here; some extracts:
Together they had the clear vision to establish a European alternative to OpenAI and BAAI, and establish a globally leading AI-research institution with European values at its core (..)
Aleph Alpha decided to build not only an independent and open multi-language alternative to the closed US and Chinese offerings but add functionality that makes the integration, (ethical) alignment and innovation based on large models easier, more transparent and robust.
Knowing that developers seek more than performance, Aleph Alpha decided to pick a select few light-house customers with a strong pull-dynamic and broadly relevant use cases to gradually educate the market, better understand customer needs and ultimately identify reusable components (..)
Moving closer towards large generalizable models/AGI requires significant computing power/resources, comprehensive training datasets and the ability to overcome unique engineering challenges (=talent).
“These models are so adaptable and flexible and their capabilities have been so correlated with scale we may actually see them providing several billions of dollars worth of value from a single model, so in the next five years, spending a billion in compute to train those could make sense” — Bryan Catanzaro, VP of Allied Deep Learning Research at NVIDIA
In a nutshell, we gained strong conviction that Aleph Alpha can stay competitive on performance by accessing compute, talent, and data, yet leaning more into B2B relevant aspects such as independence, trustworthiness, transparency, and explainability — all by incorporating values and norms of the Western World, and allowing their customers to decide what’s right or wrong. We assumed this to be their edge in the market.
Why this seemed so important? Well, as Jonas tends to say “we want to have a hand on the wheel”. Generative AI will soon be controlling every single product we use. Giving up sovereignty and becoming no more than a customer, we leave the directive to other players based in China or the US, similar to what Europeans did with social media platforms. We assumed that Aleph Alpha’s sovereign and independent positioning would help to gain trust with customers (and yes, by now we know it does).
Essentially, our initial thesis boils down to “becoming the critical infrastructure for customers who run our critical infrastructure”. Said differently, gaining trust and providing competitive generative AI technology to customers across governments and industries such as finance, insurance, legal, healthcare, and industry, among others.
Although all of our initial assumptions seem to still hold 2.5 years later, one of them was partially off: “Entry barriers will increase and generative AI will be centralized.”
- Partially off, because we did not expect open-source variants such as Llama to drive democratization, eventually leading to convergence of LLM performance. Today, we know that LLMs will become a commodity very soon. It’s comparably cheap and simple to train them.
- Partially right, because independent of the democratization aspect, the value created seems to stay centralized with a “few take all logic”, just as for the hyper scalers. Looking at the actual value captured in $$, OpenAI is indisputably the giant in the room and it becomes increasingly more difficult to compete with them.
Slowly But Gradually: Picking Up Momentum
Following our initial investment, the founders hired a range of more world-class researchers and doubled down on their cutting-edge technology. Among other breakthroughs, the Aleph Alpha team invented multi-modality (early 2022, see video below) and explainability (late 2022). All of this was triggered by conversations with domain experts in their ICPs: Enterprises and governments who deal with sensitive data and run mission-critical applications.
This was all before the big bang, the ChatGPT moment in November 2022. Even though it was clear that this moment would eventually come, nobody knew about the timing. Probably not even OpenAI CEO Sam Altman as he said at a fireside chat at TU Munich in May 2023: “We didn’t expect that ChatGPT would’ve had such an impact. We were completely overwhelmed”.
AI Cambrian Explosion: “ChatGPT Moment”
The race was on. ChatGPT was the wave that lifted all boats with “AI” on their cover. Suddenly, everyone and his dog became an AI founder, investor, expert, or advisor. It’s funny because finally, people started listening to the nerdy AI guys. Two such nerdy guys 👋🏻 discussing the AI Cambrian Explosion, capabilities of LLMs, and the future of AI (and Aleph Alpha) in a fireside chat at Noah Conference in December 2022, a few weeks following the launch of ChatGPT. Video below.
Facing an exploding interest, material productivity gains, and value created by AI, it quickly became a “non-option” to not be an “AI Investor” anymore. If you didn’t have a thesis on AI as a venture firm, you seemed to be missing out heavily in 2023. At least this is what most people thought.
On the contrary, for me having developed, researched, and invested in AI for the past decade, 2023 was the year where I became very cautious with “AI investments”. It has become way too noisy with tons of dump money chasing hot “ex [insert crazy AI company name]” founders while at the same time, OpenAI continues to kill cohorts of startups with every new release.
Zooming out, my humble perspective is that if you started investing in early-stage AI ventures in 2023 … in short, I’m just sorry for you — too late. You are probably — what the academics call — the “Laggard”.
Validating “Enterprise-grade AI” And Pushing Into the Market
Back to Aleph Alpha. With the rising tide and increasing noise among AI startups, founders needed to ask themselves how to gain an edge long-term. Different from consumer/prosumer-focused OpenAI, Jonas and his team doubled down on its initial B2B and B2G positioning, and continued to extend their middle layer (if you don’t know what this is and want to understand the AI stack in less than 3mins, click here) through reusable components that simplify LLM integrations across use cases and industries.
On the flip side, recognizing the explosive momentum and extreme pressure on public companies to frame their AI strategy, we quickly realized that the window of opportunity to get a foot in the door as a Gen AI vendor will likely be limited to months, not years. With a validated value proposition of “enterprise-grade AI”, competitive LLMs, the ability to deploy across clouds and on-prem environments, and unique features such as explainability and traceability, the focus shifted to scale their go-to-market motion. Aleph Alpha was required to shift gears.
Cash Is Necessary, But Not Sufficient — Aleph Alpha’s Series B
Early 2023, the company opened the gates and started conversations for their Series B funding round. Initially planned at $ 100M, Aleph Alpha quickly received a variety of term sheets for significantly more money than anticipated. Besides the fact that we were required to revise the round size and structure, we also started to discuss our preferred investor type as interested parties ranged from purely financial to strategic investors. While historically we (as most other VCs) had a preference for TIER1 growth investors to lead follow-on funding rounds, we decided that this time we need a strategic set of investors to secure Aleph Alpha’s competitive positioning.
Cash is necessary, but not sufficient. OpenAI, Antrophic, and others have shown that strategic partnerships with Microsoft or Amazon, respectively, can help to gain an unfair advantage. Although most Gen AI companies focus solely on the “access to infrastructure” component when thinking about such partnerships, we extended this thought to “access to talent/researcher”, “access to domain expertise/data”, and “access to distribution”.
The result eventually became a $ 500M Series B funding round with a unique set of new investors that we announced earlier this week, see FT article here. At Earlybird, we decided to double down on this unique opportunity and participate as the largest existing shareholder in this oversubscribed round. With the funding and strategic partnerships across infrastructure (HPE, Schwarz Group), research (IPAI), domain expertise, distribution, and deep integrations (Bosch, SAP, Christ&Company, Burda), Aleph Alpha will double down to become the preferred Gen AI option for customers across enterprises and governments.
At Earlybird, we’re incredibly excited to continue working with Jonas, Samuel, and their exceptional team across the board and beyond. The future is bright and we’re humbled to be part of this journey!
>> Make sure to check out Aleph Alpha’s job openings here.
