How AI is fundamentally changing the operational needs of startups

From Abu Dhabi to Zurich, artificial intelligence (AI) is transforming industries and redefining how new businesses are created. AI-native startups are companies whose core products are built from the ground up on AI technologies. By doing this, they accelerate the time to market and revenue, reduce the need for scaling teams and pose questions to venture capitalists and government leaders alike. At Startup Genome, we are committed to crystallizing the opportunities and challenges AI presents to startups and regions in a rapidly evolving digital economy.

In-depth conversations with the founders of some of the fastest-growing AI-native startups — including Coframe and Didero — reveal first-hand insights into how AI is actively reshaping the global startup ecosystem.

Four key themes stand out:

Traditionally, scaling a startup meant hiring more employees, but AI is rewriting this narrative. AI-native startups achieve product-market fit with smaller teams and higher levels of automation. As Kevin Terrell, founder of BirchAI, now a Sagility company, explains: “We’ve seen incredible efficiencies with how we run our business. We’ve highly productized our solution. Even with Fortune 500 healthcare clients, the workload per engineer is minimal.”

This points to a broader economic shift: the classic correlation between startup success and job creation is weakening. Policy-makers need to rethink how they define and measure entrepreneurial impact and approach job creation from a different perspective.

Naturally, this new way of startup creation is also changing their investment requirements. AI-native startups are beginning to rewrite the rules of venture capital — particularly when it comes to early-stage fundraising. Terrell notes: “As a new startup, if I already have a few hundred thousand in revenue with a mix of customers, why would I give away 20% of my company for a $3 to $5 million investment?”

Startups that can bootstrap longer and reach significant traction before seeking external funding are in a position to demand better terms, fundamentally shifting the balance of power in investor-founder relationships. This may also lower the barriers to startup creation, opening the door for more founders to launch capital-efficient ventures.

Despite AI’s potential to democratize innovation, access to top AI talent remains concentrated in a few global hubs. According to Josh Payne, founder of Coframe, San Francisco remains the epicentre: “Most of the top AI talent is concentrated in the Bay Area. There are exceptions — Mistral in Paris, Lovable in Sweden — but the highest density of talent is still in San Francisco.”

Given the speed at which AI is evolving, being close to where the innovation is happening matters more than ever. Much of the core knowledge hasn't had time to spread beyond the core innovation hubs.

AI-native startups are proving that they can scale with leaner teams, but the demand for top-tier engineers remains intense, particularly for those working on foundational models or complex applications.

Tom Petit, founder of Didero, underscores this challenge: “The biggest challenge? Hiring. How do you convince an AI engineer to leave a $500,000 salary at a big tech firm to join a seed-stage startup? Candidates need to understand the opportunities behind early-stage companies.”

With a thin global talent pool gravitating towards major tech hubs and big-tech salaries, startups in emerging ecosystems find themselves at a disadvantage — competing not just on innovation, but on access to the people who can build it.

This hints at one critical challenge governments must address as they race to become competitive: AI innovation hubs. Policy-makers need to design and execute an integrated set of policies and programmes to position themselves for success. They must invest in computing infrastructure, agile regulation, research translation and more.

Terrell highlights an underutilized asset that policy-makers must leverage: data. “In healthcare, for example, a critical moat, a country or a region can offer startups access to rich, longitudinal datasets. If a startup has access to millions of structured records, that’s a competitive advantage that’s hard to replicate.”

Countries that can facilitate access to structured, normalized data across domains — while ensuring privacy safeguards — are one step ahead in nurturing AI innovation ecosystems.

AI-native startups are redefining the nature of entrepreneurship through accelerated scaling. With leaner teams, evolving funding dynamics, and intense competition for talent, the global startup ecosystem has reached an inflexion point. Policy-makers, investors and corporate leaders must rapidly adapt to this new reality.

Key takeaways for stakeholders:

Build AI-ready ecosystems by investing in AI education, research translation, structured datasets and computing infrastructure, while fostering agile regulations that balance innovation with governance.

Evaluate your investment approach with an understanding that AI-native startups are more capital-efficient and reach revenue milestones faster.

AI startups will continue reshaping enterprise solutions, requiring a shift in partnership strategies, procurement models and AI adoption roadmaps.

AI-native startups are fundamentally altering how businesses are built, scaled and supported. Stakeholders must reassess whether their models are built for this new era — and adapt now — or risk being left behind in an AI-native world.