AI Has Reached Its Inflection Point

By Tal Elyashiv, managing partner of SPiCE VC, general partner of True Global Partners

Every technological revolution reaches an inflection point.

It’s the moment when technology stops being defined primarily by what it can do and starts being shaped by everything around it: economics, regulation, geopolitics, infrastructure and social acceptance. AI has reached that point.

The numbers are extraordinary. Generative AI reached 53% adoption within three years, faster than either the personal computer or the internet. Global corporate AI investment reached $581.7 billion in 2025, up 130% in a single year. In 2025, 88% of surveyed organizations reported using AI.

But adoption curves alone do not determine how a technological revolution unfolds.
In Investing in Revolutions, I call the external forces that accelerate, constrain and redirect emerging technologies force field factors. They include macroeconomics, regulation, geopolitics, infrastructure and sociopolitical pressures. They are often underestimated during the exuberant early stages of a technology because everyone is focused on capability.

Then the technology becomes important enough that the force field takes over and that is where AI is now.

Technological Revolutions Get Messy

We tend to remember successful technologies as inevitable. Their actual histories are much less orderly.

The automobile brought mobility and economic growth along with accidents, congestion and new forms of public risk. Aviation transformed transportation while forcing governments and industry to develop safety systems around a technology where failure could be catastrophic. The internet democratized information and commerce, then produced cybercrime, fraud, privacy problems and enormous concentrations of economic power.

The pattern repeats: a technology solves one set of problems and creates another. Society responds, rules emerge, industries professionalize, some companies disappear, while others become more valuable because they solve the problems created by the technology’s success.

The difference between AI and all the technological revolutions before it is that it’s moving through this cycle at remarkable speed. Stanford’s 2026 AI Index found that frontier models improved by 30 percentage points in a single year on Humanity’s Last Exam, a benchmark deliberately designed to remain difficult for advanced AI. We are compressing changes that once unfolded over decades into years. That gives our institutions, businesses and society much less time to adjust.

Regulation Becomes Part of the Revolution

Regulation is one of the most powerful force field factors because it can accelerate adoption by creating trust or impede it through cost and complexity. Every mature transformational technology eventually develops rules. Aviation needs safety standards. Financial markets need disclosure requirements. Pharmaceuticals need clinical trials. AI will need its own institutional architecture, getting there will be difficult.

Legislators are trying to govern systems whose capabilities can change materially while legislation is still being written. Compliance also favors scale. Rules intended to make AI safer can inadvertently strengthen the largest companies because they are best equipped to absorb the cost.

AI’s rapid development creates challenges for policymakers working to establish clear and effective rules. Developing informed standards takes time, particularly as capabilities and risks continue to change. While regulatory frameworks evolve, companies should strengthen their own evaluation, accountability and deployment practices alongside meeting applicable legal requirements.

Independent evaluation, common testing methodologies, auditable safety commitments and credible deployment standards will have to become part of AI’s infrastructure. Mature industries eventually discover that trust is an economic asset and AI will too.

Social Acceptance May Be the Hardest Force to Predict

Technologists naturally focus on what a system can do, but society eventually asks what that system is doing to it. We are beginning to see that tension around employment. Stanford reports that employment among software developers ages 22 to 25 has fallen nearly 20% since 2024. One third of surveyed organizations expect AI to reduce their workforce in the coming year. At the same time, the estimated value Americans receive from generative AI reached an estimated $172 billion annually by early 2026.

People can find a technology enormously useful and still fear what it means for their livelihood.
The perception gap is striking. The Stanford report also revealed that 73% of AI experts expect the technology to have a positive impact on how people work. Only 23% of the U.S. public agrees.

If AI creates enormous productivity gains while the benefits appear concentrated among a relatively small number of companies and investors, social resistance will grow. That can affect labor policy, taxation, antitrust enforcement, copyright, data regulation and the speed at which AI is allowed into sensitive parts of the economy.

Social acceptance is not separate from the economics of a technological revolution. It helps determine them.

China Changes the Equation

Then there is perhaps the most consequential force field factor of all: geopolitics.

China views AI as strategic infrastructure. Increasingly, so does the United States. The competitive gap is narrower than many Americans assume. U.S. and Chinese models have traded places near the top of performance rankings since early 2025. As of March 2026, Stanford’s AI Index reported a 2.7% gap between the leading U.S. and Chinese models in the model-ranking comparison it cited. This reflects performance on that evaluation, rather than a comprehensive measure of national AI capability, and does not establish equivalent reliability in real-world applications.

While the capability differences between U.S. and Chinese models are small, the capital flow differences are immense. U.S. private AI investment reached nearly $286 billion in 2025 compared with $12.4 billion in China. But that comparison does not capture China’s use of state capital. Stanford estimates Chinese government guidance funds deployed roughly $184 billion into AI companies between 2000 and 2023.

This makes AI governance unusually difficult. The same governments contemplating restrictions to manage risk also believe leadership in AI will influence economic competitiveness and national security. Safety is therefore competing with strategic urgency and no serious investor can analyze AI without accounting for both.

Investing Through the Inflection Point

Inflection points do not mean technological progress is ending. Rather, the variables determining who wins are multiplying.

AI capabilities can improve while regulation tightens. Adoption can accelerate while public resistance grows. Enormous economic value can be created while entire categories of companies lose value. The United States can maintain a huge capital advantage while China remains technologically competitive.

For investors, this is where understanding the force field matters.
● Frontier model companies will face enormous capital requirements along with greater political scrutiny and safety obligations.
● Application companies will need more than access to the same models everyone else can use.
● Proprietary data, deep workflow integration, distribution and industry expertise become increasingly important.
● The entire infrastructure layer must develop around AI: security, governance, identity, compliance, evaluation, monitoring and verification.

These factors make up what will allow AI to move from extraordinary technological capability to dependable economic infrastructure.

The early stage of a revolution rewards those who recognize what has suddenly become possible, while the next stage rewards those who understand the forces that determine what becomes durable. AI has reached that inflection point.

Disclaimer: This article reflects the author’s views and is provided for general information only. It does not constitute financial or investment advice, or a recommendation to buy or sell any investment. The author holds senior roles at SPiCE VC and True Global Ventures. Investments involve risk, including the possible loss of capital.