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AI's safety slowdown spooks the market, delays IPOs, and hands incumbents more room to run

Kyle Stanford, Dimitri Zabelin, Harrison Rolfes

Tue, September 15, 2026 at 1:00 PM GMT+3 3 min read

Dario Amodei, CEO and co-founder of Anthropic.

Michael M. Santiago/Getty Images

In recent days, CEOs of leading technology companies endorsed a call for slowing down the pace of AI development alongside a coordinated effort to safeguard their models against misuse. The announcements, made in response to rising security concerns, have introduced uncertainty into the capex spending cycle, leading to a sell-off in AI-related stocks on Monday.

Below, our analysts offer perspectives on how this could affect current private market trends.

A marketing tactic with no long-term bite

Dimitri Zabelin, Senior Research Analyst

Heading further into Q3, we could see investor optimism and appetite briefly wilt, but a fundraising freeze in Q4 is unlikely. More likely are elongated timelines between funding rounds as VCs and PEs re-evaluate the longer-term implications of these announcements. Longer term, the fundamental, secular story of the multi-trillion-dollar AI buildout has not been derailed.

The recent calls for AI safety from OpenAI, Anthropic and others are tactical market strategies, not genuine altruistic gestures. The calls for external auditors and a slowdown appear like a classic case of pulling up the ladder from beneath them. Adding regulatory complexities and costs can create a massive legal barrier to entry. Smaller, open-source developers looking to disrupt the software hegemons will have to stomach higher costs and divert more resources to compliance than R&D. Self-imposed regulation satiates the growing public appetite for government-led policies, while President Trump has made it clear he is uninterested in a legislative approach.

These announcements have the same look and feel as recent marketing tactics by leading LLM developers to publicly warn that their model is too powerful, effectively driving up more interest in it. Anthropic and OpenAI have repeatedly issued public warnings about the risks posed by their most advanced models. These tactics amplify the hype around frontier models and the atmosphere-piercing valuations they are assigned.

Pushing the IPO cycle out further

Kyle Stanford, Director of US VC Research

With the recent escalation in Iran driving up oil prices, inflation and interest-rate-hike expectations, hopes for a renewed exit cycle were already dimming. OpenAI has already stated that it wouldn't IPO this year, and news that AI leaders might self-impose growth restrictions is likely to reinforce expectations for longer exit timelines, putting more pressure on an already-strained LP base.

While the market reaction is likely temporary and does not change the longer-term AI investment thesis, investors will still need to focus on potential downstream risks. This includes portfolio companies exposed to the expanding AI ecosystem, where a slowdown in development—or in spending—might affect operating fundamentals.

Value shifts to scale operators

Harrison Rolfes, Senior Research Analyst

While details are not clear, a credible slowdown in development would lengthen model-release cycles, delay data-center commitments, and force investors to distinguish between revenue generated by current products and growth that depends on capable models. It could also shift competition away from raw performance toward distribution, reliability, safety, and cost, which are areas where scale matters as much as technical leadership.

For Anthropic and OpenAI, the near-term IPO impact is higher uncertainty, not necessarily lower value. Public investors would demand clearer disclosure around testing standards, release authority, compute obligations, and how slower capability gains affect revenue forecasts. Anthropic may benefit more because safety is already central to its positioning; accepting slower releases could reinforce its credibility without changing its strategy. OpenAI faces a harder balance because its valuation and infrastructure commitments require rapid commercialization across a broader product portfolio.

The deeper consequence is industry concentration. Both companies can absorb delayed launches and rising compliance costs that smaller labs cannot. A slowdown could therefore make frontier AI safer while simultaneously making it less open and competitive. The forward-looking question now shifts to who controls the pace, standards, and economics of progress.

This article originally appeared on PitchBook News