OpenAI has filed to go public. The IPO could value it between $730 billion and $1 trillion, one of the largest debuts in history. Not bad for a company that had no product and no revenue a decade ago.
But the number that should stop you isn't the valuation. It's a different one, and it landed the same year.
The Other Number
As of this week, 267 layoff events have hit tech in 2026. Total workers affected: 185,894. And here's the part that should get your attention: 56% of those events name AI, automation, or machine learning as a factor. That's not my spin. That's from layoff tracking data across public company announcements.
The company about to go public is the same company cited in pink slips across the industry it disrupted.
That's not a criticism. It's a market reality. But it creates a specific set of questions that every CTO, VP of Engineering, and hiring leader needs to answer right now — and most of them aren't.
What an IPO Changes
When a company like OpenAI goes public, the calculus shifts. Pre-IPO, it can operate as a mission-driven lab that occasionally makes commercial concessions. Post-IPO, it answers to quarterly earnings, revenue growth expectations, and shareholder returns.
That means the pricing, access, and roadmap of the tools your teams are depending on will increasingly be shaped by what's good for OpenAI's stock, not what's best for your workflow.
This isn't hypothetical. We've seen it before with every major platform transition. Reddit matured, then priced its API out of reach for the third-party developers who built on it. Broadcom acquired VMware and rewrote licensing into a subscription model that spiked customer costs overnight. The pattern repeats. Infrastructure becomes a cost center the moment it has to grow revenue.
You've built your engineering workflows, and possibly your headcount decisions, around a company that's about to have a very different set of incentives.
The Dependency Problem You're Not Solving
I talk to CTOs every week running production AI workflows on a single vendor's API. No fallback. No cost cap. No contractual ceiling on price changes. Just a credit card and a prayer that the model they're building on doesn't get deprecated or repriced into inviability.
Gartner has put a name to it. "Prioritizing open standards, open APIs and modular architectures in AI stack design, help enterprises avoid vendor lock-ins. In addition, CIOs must make interoperability a standard in generative AI pilots and assessments," says Arun Chandrasekaran, a Distinguished VP Analyst at the firm, which lists vendor lock-in among the critical GenAI blind spots companies need to address.
That was a manageable risk when OpenAI was a capped-profit lab burning investor money. It's a different kind of risk when the company answers to public shareholders who expect the numbers to grow every quarter.
The same logic applies to your talent strategy. If you cut headcount in 2025 and 2026 because AI was handling the output — great. But what's your staffing posture if the AI you're relying on gets 40% more expensive, or the model you fine-tuned gets sunsetted for a newer tier that costs more?
AI is replacing workers. I've written about it.
But a hollowed-out workforce isn't flexible. It can't absorb a vendor pricing shock. It can't reskill fast enough if the tool landscape shifts. It can't respond to a competitor who just hired the people you let go and is outpacing you.
What Smart Operators Are Doing
The leaders I'm watching right now are doing three things at once.
They're auditing AI vendor concentration: mapping which tools have quietly become load-bearing infrastructure, finding where they've got single-vendor dependency, and building at least a minimal fallback, whether that's a second provider like Anthropic or an open-weight model they can self-host. That's not paranoia. It's basic business continuity applied to a new class of vendor.
They're keeping hiring optionality. Not freezing every role — being surgical, cutting what's genuinely AI-replaceable while protecting the roles that need judgment, institutional knowledge, and the ability to adapt. The ratio of human-to-AI capability shifts slowly enough that a blanket freeze today is almost always a talent gap eighteen months from now.
And they're treating AI spend as a capital allocation question, not an IT budget line. Where's the money going? What return is it earning? What's the exposure if the price doubles? Those are CFO questions, and most CFOs aren't asking them yet.
The Real Takeaway
OpenAI going public is good news for the industry in many ways. More scrutiny, more transparency, more market discipline. But it's also a signal that the "wild west" phase of AI tooling is ending.
The companies that did well during the early internet didn't just use it. They understood the infrastructure they were building on, kept their strategic flexibility, and didn't let vendor lock-in make their decisions.
The same playbook applies. Your AI strategy is only as durable as your ability to adapt when the companies behind it change their terms.
And they will.
VC5 Consulting specializes in placing senior engineering and technical leadership talent at companies navigating AI-driven transformation.