How AI is rewriting the playbook for investing

Founders’ takes is a new series featuring expert insights from tech leaders transforming industries with artificial intelligence. In this edition, Cem Ötkün, CEO and co-founder of startup scouting platform Bounce Watch, shares his views on how AI is reshaping investing.

Venture capital, once built on networks and narratives, is now undergoing a structural shift. AI is no longer a futuristic add-on to the investment process — it’s becoming an operating system. And for those investing in the opaque world of private markets, it’s not optional. It’s existential.

The broken machinery behind the pitch

Despite all the capital flowing through venture, much of the machinery remains outdated. Deal flow still relies heavily on intros. Screening is inconsistent. Diligence is time-consuming and subjective. Too often, the loudest signals win — not the most promising ones.

This inefficiency creates three core risks:

Missed opportunities, especially in under-networked geographies.
Biased capital allocation, driven by pattern-matching rather than real traction.
Time dilution, with analysts spending more time gathering data than interpreting it.

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AI isn’t just solving these problems. It’s redefining the investment stack entirely.

A new architecture for decision-making

The modern investment team increasingly resembles a hybrid between a research lab and a software company. Instead of asking “Who do we know?”, the question is “What signals are emerging that others haven’t seen yet?”

AI enables this shift in several ways:

Data orchestration: Tools now unify disparate sources — talent movement, product launches, market activity — into coherent, queryable insight.
Micro-pattern detection: Models surface weak signals that precede big movements. Not just trends, but subtle tremors.
Process acceleration: From drafting memos to mapping competitors, AI dramatically compresses workflows.

Under the hood, what’s actually happening is a full rewire of the investment workflow. LLMs are being fine-tuned on deal memos and partner notes. Vector databases store historical pitch content and internal scoring data. Embeddings allow semantic queries across raw PDFs, Notion docs, and CRM logs. Agents chain these components together — retrieving, interpreting, and acting autonomously based on firm-level rules. This isn’t about replacing analysts; it’s about giving them superpowers they didn’t know they needed.

This is leading to a fundamental redesign of what “conviction” looks like in investing. It’s less about volume of meetings, and more about velocity of insight.

Real-time instead of retrospective

The old cadence of quarterly updates and founder calls is being overtaken by systems that observe founders in motion. Investors are now able to monitor startups as they quietly begin hiring, ship code, register domains, or test demand — all before a polished pitch emerges.

This creates two distinct advantages:

Proactive sourcing: Startups can be identified before they formally fundraise.
Portfolio foresight: Investors can spot risk and opportunity in real-time — not months later.

Europe, in particular, stands to benefit here. Fragmented ecosystems and hidden gems across the continent are better surfaced through models than word-of-mouth.

The next layer: agents and autonomy

The future of investing won’t be dashboards — it will be agents. Already, we’re seeing early versions of AI “copilots” assisting with research, due diligence, and document creation. But the next leap is autonomy.

Agents will begin to act by:

Prioritising leads based on signal strength.
Drafting investment memos tailored to internal thesis frameworks.
Recommending follow-ups, partnerships, or even exits.

This isn’t science fiction. It’s a logical evolution of where automation meets domain knowledge. And the most forward-thinking funds are already testing these capabilities behind the scenes.

A word of caution: systems without thinking are just noise

Of course, AI isn’t infallible. Poorly tuned systems can amplify noise, reinforce existing biases, or produce convincing but inaccurate insights.

That’s why the winning model isn’t machine or human. It’s machine-assisted humans with strong internal logic. Teams must treat AI like a colleague: useful, but always subject to challenge.

Crucially, the quality of insight still depends on the quality of the data — and the creativity of the people asking the questions.

What distinguishes the leaders?

In today’s landscape, advantage no longer lies in building every system from scratch. Most investment teams don’t need to reinvent the wheel — they need to integrate smarter.

What sets top-performing firms apart is not in-house engineering muscle, but the ability to select, combine, and embed the right tools into their daily routines. Instead of spending months building proprietary infrastructure, they focus on refining workflows, enhancing interpretation, and freeing up time for strategic thinking.

It’s not about owning every layer — it’s about orchestrating what matters.

The firms that excel are those that:

Seamlessly blend external intelligence into internal processes.
Adapt quickly to evolving signals and technologies.
Focus on decision quality rather than tooling pride.

They don’t try to be tech companies. They simply operate like intelligent investors in a tech-enabled world.

The nature of investing hasn’t changed. It’s still about taking smart bets on uncertain futures. But the inputs — and the speed at which we interpret them — have changed beyond recognition.

In this new era, edge doesn’t come from intuition alone. It comes from infrastructure.

And the firms that build it, adopt it, and refine it daily?

They won’t just win deals. They’ll redefine what it means to be an investor.