Artificial intelligence (AI) is reshaping the full lifecycle of capital, from where it flows and how companies access it, to how investors deploy it. AI-native companies are attracting institutional capital at increasing scale, while new AI-enabled infrastructure is emerging to allow investors to deploy that capital faster.

As these shifts accelerate, dealmakers are racing to keep pace on execution, disclosure and oversight.

Institutional capital surges into AI-native companies

Issuers reaching public markets no longer rely on traditional IPOs alone. They are weighing direct listings, dual-track processes, alternative trading systems and the re-emerging SPAC market.

This multi-track activity is fueling a major resurgence in public issuance. According to EY's Q2 2026 Global IPO Trends report , twelve deals in the U.S. each raised more than $1 billion in the first half of 2026, up from four in the same period last year. EY identified AI as a dominant driver with the strongest momentum in semiconductors, power and data center infrastructure, noting that companies are already translating AI demand into revenue.

“The capital markets are moving from the incubation phase of AI to a more disciplined industrial scale-up,” said Jerry Serowik, Head of Cohen & Company Capital Markets. “While innovation remains a key driver of interest, investors are increasingly focused on operational readiness, scalability and sustainable business models. As companies evaluate public market opportunities, the ability to connect technology innovation with a clear and executable business strategy is becoming increasingly important.”

Cohen & Company Capital Markets has observed similar trends across transactions within the AI sector. The boutique investment bank completed more than $43 billion in transaction volume in 2025 and is ranked first in SPAC IPO underwriting by left bookrunner deal count and first in de-SPAC advisory activity for the period January 2025 to present, according to figures released by SPAC Research. In its Q1 2026 Physical AI Report , the firm highlighted growing investor interest in industrial and physical AI applications. The report noted that physical AI companies, including robotics-focused businesses, raised more than $25 billion during the first quarter of 2026.

Institutional demand is being reinforced by how quickly AI has moved mainstream. According to a recent survey from my company, Prosper Insights & Analytics , 40.6% of U.S. adults already use generative AI, reinforcing that AI-native companies are being built for a market that is already adopting the technology.

As demand brings more AI companies into capital markets, the focus shifts from capital formation to capital deployment. Investors need infrastructure capable of moving money and executing decisions at the speed and scale AI makes possible.

Rewriting the capital markets operating model

The surge in capital is also flowing into the infrastructure that enables modern markets. Morgan Stanley’s May 2026 IPO analysis notes that global equity capital markets issuance rose 43% year over year in Q1 to $256.8 billion, with digital infrastructure being identified as a primary driver.

Alpaca is a clear example of where that institutional capital is landing. The API-first brokerage platform, which powers more than 10 million brokerage accounts in 40+ countries, secured $135 million in new funding (part of $435 million in total capital and debt financing) to scale its agent-first brokerage infrastructure. That institutional backing follows rapid adoption, with Alpaca seeing monthly active API users grow nearly 4x from December 2025 to June 2026.

But infrastructure that moves capital faster also leaves less room for failures in disclosure and oversight. As AI becomes embedded in market participation and the transaction process, governance is no longer separate from capital markets infrastructure. It is the control layer that allows the system to scale.

Building governance into the capital lifecycle

For issuers and boards, that control layer begins with how companies use AI systems, what information those systems process and how those practices are disclosed. According to a recent Prosper Insights & Analytics survey, 32.5% of U.S. adults want more disclosure and transparency on the data AI systems use, signaling that retail investors are asking the same disclosure questions that institutions are currently navigating with counsel.

That pressure is intersecting with a market that can analyze and react to disclosures almost immediately, giving companies very little room to correct course after a filing becomes public.

At the same time, legal and operational risks surrounding AI use during transaction review are intensifying. In a recent client alert on AI use in SEC filings and deal documents, Reed Smith attorneys warned that uploading sensitive disclosures into unapproved AI platforms creates severe legal exposure. They highlighted that casual AI use can trigger Regulation FD violations from leaking material nonpublic information (MNPI), lead to a waiver of attorney-client privilege and expose gaps in Sarbanes-Oxley disclosure controls.

“Public companies today are leveraging advances in AI capability to increase their responsiveness and flexibility across all areas of their business, and legal functions are not and should not be immune to that paradigm,” said Lynwood E. Reinhardt, U.S. Chair of Reed Smith’s Capital Markets and Corporate Governance Group and member of the firm’s On Chain: Crypto & Digital Assets Group. “From SEC reporting to fiduciary risk, oversight can no longer be handled reactively. The companies that hold their value after going public will be the ones that are AI-native from the start of their public listing, embedding AI governance, disclosure controls and regulatory risk directly into their public company architecture. We are seeing huge demand to assist with that process.”

Disclosure: The consumer sentiment study referenced above was conducted by my company, Prosper Insights & Analytics . This is the same dataset used by the National Retail Federation, and available from Amazon Web Services, Databricks, and the London Stock Exchange Group for economic benchmarking.