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Almost every investment firm says it uses AI.

Saying it is easy.

Using it is doable.

But building your firm around it is a different game.

I recently joined David Weisburd on the How I Invest podcast for an honest conversation about exactly this: what we learned over almost a decade of transforming Earlybird into an increasingly AI-native investment platform.

We discussed power laws, founder selection, portfolio construction, sourcing automation, proprietary data, change management and where VC alpha will actually come from when everyone has access to the same AI models.

For everyone who doesn’t want to spend the full 90 minutes, I extracted seven key takeaways.

Let’s dive in.

Venture capital has a strange error function.

If you invest €1M into a startup and it fails, you lose €1M.

If you don’t invest €1M into a company that would have returned 50x, you effectively miss €50M of potential upside.

That asymmetry matters because venture follows an extreme power law. A tiny percentage of companies generate the overwhelming majority of returns.

It means false negatives are incredibly expensive.

This also helps explain one of the industry's biggest contradictions. Venture capital was created to finance unconventional businesses that traditional capital providers wouldn't touch. Yet investors increasingly cluster around the same companies.

Why?

Everyone knows they cannot afford to miss the outlier.

So when Fund A looks at a company, Fund B suddenly wants to look as well. When a famous investor joins a round, everyone pays attention.

FOMO becomes rational.

But following consensus is also dangerous because some of the best investments initially look anything but obvious.

Earlybird invested in UiPath out of Romania in 2015. Our Romanian Partner Dan Lupu led the seed round when raising money for the company was still difficult. Six years later, UiPath went public at roughly $33B.

The opportunity was exceptional precisely because it wasn't yet consensus.

The challenge is therefore to do two things simultaneously: Don't miss the obvious winners. Find the non-obvious winners before everyone else does.

And this also means studying your anti-portfolio.

One example we discussed was Lovable. We saw the company at pre-seed and passed partly because the roughly 8% ownership available was below our fund target of around 15%.

In hindsight, that was incredibly expensive. At their recent $13bn valuation, the investment would’ve likely returned north of 50x and potentially become a fund-returning investment by itself.

The lesson isn't to ignore valuation or ownership. It's to continuously ask whether your investment rules are helping you find outliers; or systematically filtering them out.

VCs love lists of founder characteristics. Relentless. Intelligent. Ambitious. Resilient. Obsessive.

They're useful, but they miss something important: The ideal founder depends enormously on what they're building.

At Earlybird, we roughly think about the technology stack across three layers:

At the Deep Tech layer, technical credibility is critical. Can the founders bring research into production? Do they understand manufacturing, supply chains and process engineering?

At the frontier AI layer, something else becomes particularly important: talent gravity. If you're building a world model, foundation model or specialized AI infrastructure, there might only be a few dozen exceptional researchers globally for what you're doing. Can your CEO or CTO convince those people to join?

At the application layer, the weighting changes again. Building software has become dramatically cheaper. You can increasingly prototype products over a weekend. Capital therefore matters less initially, while product velocity, customer understanding and distribution matter more.

One framework I like is: The deeper you sit in the technology stack, the more important capital becomes. The higher you sit in the stack, the more important distribution becomes.

A rocket company needs significant capital before it can even prove the technology works. An AI application might reach its first million in ARR before raising meaningful external capital.

Same asset class. Completely different founder requirements.

One particularly interesting pattern from startup data is that companies raising early rounds at higher valuations and lower dilution often continue doing so in subsequent rounds.

One explanation is simple: Fundraising is a skill. And the underlying skill transfers.

Great CEOs sell a vision.

They sell it to investors to attract capital.

They sell it to exceptional employees to attract talent.

They sell it to customers.

They sell it to partners.

This is particularly important for capital-intensive companies where access to funding can itself become a competitive advantage.

But the same underlying capability increasingly shows up somewhere else: founder branding.

Five years ago, personal brand wasn't something we thought much about in our research. Today, look at many of the fastest-growing technology companies. Their founders often have strong public identities.

That's not necessarily vanity. I think of founder branding as scalable vision-selling. Instead of explaining your mission individually to every candidate, investor and customer, you package that vision and distribute it to thousands or millions of people.

And this becomes more important as building gets cheaper.

If 50 teams can build technically similar AI products, differentiation increasingly moves toward distribution, talent, speed and brand.

In that environment, founder brand becomes part of the company's infrastructure.

When I joined Earlybird in 2017, I encountered something that surprised me. Some of the smartest people I'd ever met were spending enormous amounts of time doing repetitive manual work.

One of our founding partners once gave me a conference attendee list with around 400 people and asked: Who should I meet?

I spent days searching Crunchbase, LinkedIn, Xing and our CRM before producing a prioritized list.

And I remember thinking:

Why is a human doing this?

The same question applied everywhere. Researching companies. Finding introductions. Updating CRM systems. Preparing meetings. Creating competitive landscapes. Writing investment memos.

So I started building tools for myself.

First simple automations. Then knowledge graphs representing our network. Then sourcing systems and recommendation engines.

Eventually we hired our first full-time engineer. At the peak, Earlybird had eight engineers, representing roughly 20% of our team, building the fundamental infrastructure. Today, three senior engineers continue developing on top of it.

But the important lesson is that we didn't start by asking: “Where can we use AI?”

We asked: “If we built an investment firm from scratch today, how should it work?”

That's a very different starting point.

Today, AI touches almost every part of our workflow.

A portfolio company receives a term sheet? Benchmark it against historical term sheets.

Founder meeting in 15 minutes? Automatically generate a briefing containing our previous interactions, company background, traction, competitive landscape, recent news and relevant questions.

Need an investment memo? Generate a first version based on our existing knowledge and the structure of more than 1,000 historical memos.

Need a presentation? Take a transcript and generate a deck in Earlybird's CI.

The interface itself is changing. For years, data-driven VC meant building dashboards. I increasingly believe the dashboard is dying. The future interface is conversational.

Instead of: Human → Software → Database

we increasingly move toward:

Human → AI → Everything.

This leads to perhaps the most important question David asked me:

If every VC has access to the same AI models, where does alpha come from

Everyone can use ChatGPT, Claude or Gemini. Everyone can buy PitchBook, Dealroom or Harmonic. Public information and foundation models are increasingly commoditized.

So differentiation moves somewhere else: proprietary data.

And by proprietary data, I don't necessarily mean some secret external dataset.

I mean your own institutional exhaust. Meeting transcripts. Investment memos. IC decisions. Partner ratings. Rejection reasons. Founder interactions. Portfolio performance.

At Earlybird, for example, we introduced a structured post-investment-committee survey in 2018. Participants rate dimensions such as market, product and team and record whether they would make the investment.

Over time, you can connect those judgments to actual outcomes.

Suddenly you can ask:

Eventually, you can begin to codify the firm's taste.

Imagine scoring every startup on two dimensions: Probability of success × Fit with Earlybird. Now add a feedback loop.

An investor rejects a company and labels why. A meeting gets transcribed. New information enters the system. Portfolio performance provides another signal.

The recommendation engine continuously improves.

The end state could look surprisingly simple: I wake up and the most relevant founder meetings are already in my calendar. Fifteen minutes before each one, I receive the briefing. The meeting is automatically captured. My feedback flows back into the system. Tomorrow's recommendations improve again.

That's where I believe AI can generate real investment alpha. Not because an LLM magically becomes the world's best investor. Because the investment system starts compounding institutional knowledge.

One of my biggest misconceptions was assuming data would be the hardest problem.

Startup data is messy. Sources contradict each other. Qualitative information needs to be quantified. Entities need to be matched and deduplicated. These are difficult problems.

But they're solvable.

The harder problem turned out to be change management.

We experienced this directly with our sourcing platform EagleEye.

Before building it, we tried to quantify our actual coverage. We defined a universe based on investments made by roughly 200 relevant funds and checked how many opportunities Earlybird had previously seen.

We thought our European coverage was comprehensive back then. The data showed roughly 70–72%. Said differently, we were missing about one in four relevant opportunities.

After building EagleEye, we increased this to roughly 95%+, meaning we now see approximately 19 out of every 20 relevant opportunities, typically at least six weeks before their financing round closes.

Surely everyone would immediately use it? Majority of the team initially didn't.

Existing habits were stronger than the objectively better tool.

So we changed the incentive. Instead of telling people to use EagleEye, we started measuring the outcome: coverage.

If you covered France, your hit rate in France became part of how performance was assessed. How you achieved it was your choice.

But when EagleEye contained 95% of relevant opportunities, the easiest path toward the desired outcome became obvious.

This taught me an important lesson:

I shared a granular deep dive on “Measure what matters to improve coverage and performance” before.

The same applies to AI adoption.

Start with quick wins. Give someone something they can learn in 30 minutes that saves them three hours. Create the Eureka moment.

Then build more complex workflows on top.

This might be the most counterintuitive conclusion.

If AI makes investors 10x more productive, should we evaluate 10x more companies?

No.

Earlybird receives 10k+ inbound opportunities per year.

Our early-stage fund might ultimately make only 10–12 investments per year.

Historically, human investors spent enormous amounts of time filtering those 10k companies. Wrong geography. Wrong stage. Wrong sector. Wrong ticket size. Already too late.

AI can increasingly handle this top-of-funnel work.

The objective shouldn't be to replace it with more work. It should be to move the investor further down the funnel.

Instead of four relatively shallow founder meetings, maybe you have five deeply prepared conversations.

Instead of spending an hour collecting information, spend that hour thinking about the information.

Instead of evaluating thousands of obviously irrelevant companies, spend more time with the handful that could actually become outliers.

This is also where experience changes the equation.

Early in your career, quantity matters enormously. You need reps. Thousands of companies. Thousands of decisions. Thousands of feedback loops. That's how you learn what good looks like.

But once you have accumulated those reps, more information can become noise. Then the scarce resource becomes cognitive capacity. You need the prepared mind to recognize something exceptional when it appears.

I've structured parts of my own week around this principle. Monday through Thursday are generally meeting-heavy. Parts of Friday remain blocked for reading papers, newsletters and research.

Friday provides input.

The weekend provides space to process.

Monday returns to execution.

Execute → Learn → Think → Execute.

AI should create more room for this cycle, not fill every newly available minute with another task.

After almost a decade of working on data-driven venture capital, I've become increasingly convinced of something that initially sounds contradictory: The more technology we introduce into venture capital, the more valuable the human parts of investing become.

AI can screen companies. Prepare meetings. Analyze markets. Build competitive landscapes. Generate investment memos. Benchmark term sheets. Retrieve institutional knowledge. Learn from historical decisions.

And eventually automate almost everything surrounding an investment decision.

But venture returns still come from a tiny number of outliers.

The ultimate job therefore remains remarkably human: Recognize the exceptional company. Make the right decision. Win the deal. Help the founder.

The AI-native investment firm isn't one without investors. It's one where investors finally spend most of their time doing the things that actually matter.

And if we get this right, the venture firm of the future might look much less like a traditional financial institution...

…and much more like a technology company.

Stay driven,
Andre

PS: Join the DDVC Investor Summit at Bits & Pretzels virtually or physically in Munich during Oktoberfest Sep 28-30