2027 government budgets as inflection point: build local AI exporters or forever import U.S. AI

The time required to build a startup ecosystem into a powerful economic engine has compressed from 20 years down to five, as discussed in our prior article. This window, however, is at risk of being compressed even further as the AI era concentrates technology winners faster than ever. 

The impacts of this concentration on future economic activity will be profound. Cities that start late will not just become weaker producers, they will become customers, importing expensive technology simply to hold corporate competitiveness, with no offsetting asset to build on. Their best people become employees of foreign producers, trained at local taxpayers' expense.

Conversely, cities that enter early and invest aggressively will reap the rewards of a ravenous global market for AI tools and applications. A new technology era opens an effectively unlimited number of business models, each one a category with room for an early entrant to go global. Because tech product categories are winner-takes-all on a global basis, the potential return on entering early is 100 times higher.

Some ecosystems didn't wait to find out; they built first. Korea, Taiwan, and Singapore did not import chips. They built an industry of fast-growing businesses with the capacity to design and manufacture them, at a moment when competing with the U.S. looked far-fetched at best. Israel did not import American web software. Within about two years of the first commercial browsers, on an economy that could barely afford the bet, it invested aggressively in startup policies to build the capacity to create web businesses. Both bets looked risky and expensive at the time. Within a decade, both looked obvious.

More clearly than ever, the prized asset that will produce economic prosperity in the AI Era is startup founders, not developers, research, large corporations, or local compute infrastructure.

Yet in a year of very tight budgets, governments are spending tens of millions of dollars to support AI adoption by corporations that can afford it on their own, and hundreds of millions to buy U.S. chips and services to build massive compute infrastructure, all while cutting or holding flat the startup ecosystem budget, the engine of future prosperity that produces economic growth, jobs, exports, and FDI. This is not good policy. 

The trillions of dollars in value the U.S. has captured from AI came from tech companies built 10-20 years ago and AI companies built in the last five – not from companies like General Motors or General Electric adopting AI. Worse, as they adopt AI to increase efficiency, job counts go down.

Buying U.S. chips and services to build sovereign compute infrastructure is importing someone else's tech exports. Does it accelerate the creation of an AI-Native startup ecosystem in the neighboring city?

Top U.S. & Europe Data and Compute Centers

(and GSER ranking of nearest startup ecosystem)

It does not. If it did, the AI-Native startup ecosystems of Washington, D.C., Atlanta, Frankfurt, and Dublin would be booming, and they are not. This is not surprising given what 30 years of startup ecosystem evidence has taught us: more startups are created by larger startup communities and by targeted startup policy, not by internet assets.

Compute infrastructure needs to sit close to markets to reduce latency for millions of users. And model builders want access to the latest and most powerful compute at a reasonable cost to build models in weeks rather than months, but latency does not matter for batch workload so it can sit far away, in Virginia or Dublin.

This does not mean there is no reason to build AI compute infrastructure in a country. Data privacy and technology sovereignty are real issues, but may be satisfied by smaller budgets in compute infrastructure, while policy follows the evidence: AI-Native startup creation is a more valuable investment. 

Toronto-Waterloo and Montréal were global AI research leaders for decades. Nobel Prize winner Geoffrey Hinton’s arrival in 1987 and Turing Award winner Yoshua Bengio’s in 1993 led to the development of three of the world’s top AI research institutes created 10-20 years ago.

But do large government investments in AI research produce faster creation of AI-Native startups, above the rate the Global Startup Ecosystem Report (GSER) ranking would predict?

Impact of Decade-Long AI Research on AI-Native Startup Creation

Toronto and Montréal vs. peers of adjacent GSER ranks

Clearly not. Toronto-Waterloo and Montréal produce the same number and rate of AI-Native startups as peers of similar GSER rank, despite the long AI research headstart both regions had. Investing in AI research in 2027 will not increase AI-Native startup creation in 2028, in 2029, or even in 2035.

AI research certainly produces patents and talent. But consider that 93% of the patents from Canada's national AI strategy are owned by foreign entities, and many, if not most, of the country's best AI researchers work for U.S. tech companies.

Unless governments first invest in creating a thousand AI-Native startups that can later draw on the patents and talent produced locally, investment in research will mostly produce economic growth, exports, and FDI in other countries – chiefly the U.S.

We now have overwhelming evidence that startup policies generate massive and self-compounding returns in economic impact. But four years into the AI Era of Tech, most government innovation budgets are still misaligned.

To rectify this misalignment, 2027 budgets must radically increase the allocation to AI-Native startup ecosystem development, whether by growing the total budget or by cutting older and less-productive policies. They must fund a sharp increase in AI-Native startup creation, community development and talent access, updated support program portfolios, and best-practice angel and seed funding policies, rather than a massive fund of funds directed to Series A and beyond.

They may fund research, but at a level sized to the existing AI-Native startup ecosystem. They should limit compute investment to the minimum required to support non-innovation policies so they can fund startup policies including funding the use of third-party compute infrastructure and models to support the development of solutions by local AI-Native startups.

Finally, policies must take root in the fact that the definition of talent is changing, as competitive advantage in tech migrates from innovation and development speed rooted in large teams of developers toward customer, and even job, intimacy. As Agentic AI startups outnumber AI Model startups 35:1 and build on job expertise and corporate engagement, startup creation and support programs must be reinvented to attract corporate executives and employees as founders, mentors, and lead customers.

There is no neutral allocation - your 2027 budget either builds domestic exporters or funds imports.

Planning your budget for 2027? Join Stephan Kuester, Managing Partner at Startup Genome, for our Strategic Budgeting Roundtable.

Startup Genome breaks down how the fastest growing ecosystems are allocating budget, not by spending more, but by spending where growth is actually accelerating. This session gives senior leaders and their teams a clear framework for making high-impact, budget-neutral decisions.

Register now to secure your spot.

September 8 at 8:00 AM CEST
Register: https://luma.com/y8h2zfss