How AI is transforming the investment landscape.

      Founders' Takes is a new series that highlights expert opinions from tech leaders who are transforming industries through artificial intelligence. In this edition, Cem Ötkün, CEO and co-founder of the startup scouting platform Bounce Watch, shares his perspective on how AI is changing the investment landscape.

      Venture capital, which was previously reliant on networks and narratives, is experiencing a significant transformation. AI is emerging not just as a futuristic enhancement to the investment process but as a fundamental operating system. For those involved in the opaque domain of private markets, adapting to this change is not an option but a necessity.

      The flawed mechanisms behind the pitch process

      Despite the influx of capital into venture capital, many of the underlying processes remain antiquated. Deal flow still greatly depends on personal connections. Screening methods are inconsistent, and due diligence is often time-consuming and subjective. Frequently, the most conspicuous signals overshadow the most promising opportunities.

      This inefficiency gives rise to three primary risks:

      - Missed opportunities, especially in regions with limited networking.

      - Biased capital distribution, influenced more by pattern recognition than actual traction.

      - Time loss, with analysts dedicating more time to data collection than analysis.

      The heart of EU tech

      Updates from the EU tech landscape, a tale from our seasoned founder Boris, and some dubious AI-generated art are featured. It’s complimentary and delivered weekly to your inbox. Sign up now! AI is not merely addressing these issues; it is completely redefining the investment framework.

      A novel framework for decision-making

      The contemporary investment team increasingly resembles a blend of a research laboratory and a tech firm. Rather than asking “Who do we know?”, the focus shifts to “What emerging signals are others missing?”

      AI facilitates this transition in several ways:

      - Data orchestration: Tools now consolidate various sources—such as talent movement, product launches, and market activity—into coherent and queryable insights.

      - Micro-pattern identification: Models reveal subtle signals that precede significant movements, not just trends but also minor shifts.

      - Process acceleration: From drafting memos to competitive mapping, AI substantially streamlines workflows.

      At a deeper level, what’s occurring is a complete overhaul of the investment workflow. Large language models (LLMs) are being refined using deal memos and partner notes. Vector databases house historical pitch materials and internal scoring data. Embeddings enable semantic queries across raw PDFs, Notion documents, and CRM logs. Agents link these components—retrieving, analyzing, and acting autonomously based on established firm rules. This isn't about replacing analysts; it aims to empower them with enhanced capabilities.

      This evolution leads to a fundamental redesign of what “conviction” means in investing. It emphasizes the speed of insight over the number of meetings.

      Real-time insights instead of retrospective analysis

      The traditional rhythm of quarterly updates and founder calls is being supplanted by systems that track founders in action. Investors can now observe startups as they quietly initiate hiring, release code, register domains, or test market demand—all before a polished pitch is presented.

      This shift provides two key advantages:

      - Proactive sourcing: Startups can be identified ahead of their formal fundraising efforts.

      - Portfolio foresight: Investors can recognize risks and opportunities in real-time rather than several months later.

      Europe, in particular, stands to gain from this. Fragmented ecosystems and hidden opportunities across the continent can be better unveiled through models than via word-of-mouth.

      The next phase: agents and autonomy

      The future of investing will not revolve around dashboards but rather agents. We are already witnessing early iterations of AI "copilots" aiding in research, due diligence, and document preparation. However, the next step is achieving autonomy.

      Agents will start to:

      - Prioritize leads based on the strength of signals.

      - Draft investment memos aligned with internal thesis frameworks.

      - Recommend follow-ups, partnerships, or even exits.

      This concept isn't science fiction; it represents a logical progression where automation intersects with domain expertise. The most innovative funds are already exploring these capabilities behind the scenes.

      A cautionary note: systems lacking discernment are merely noise

      It’s important to recognize that AI is not infallible. Poorly calibrated systems can elevate noise levels, reinforce existing biases, or yield misleading insights.

      Hence, the successful model is not purely machine or human; it involves machine-assisted humans who apply strong internal logic. Teams must approach AI as a colleague—valuable but always open to scrutiny.

      Significantly, the quality of insight hinges on the quality of the data and the creativity of those asking the questions.

      What differentiates the leaders?

      In the current landscape, competitive advantage does not stem from building every system from the ground up. Most investment teams are not required to reinvent the wheel—they need to integrate smarter solutions.

      What distinguishes top-performing firms is not their engineering prowess but their ability to choose, combine, and embed the right tools within their daily workflows. Instead of spending months developing proprietary infrastructure, they focus on refining processes, enhancing analysis, and allowing more time