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I have spent close to three decades sitting across deal tables from private equity sponsors, and for most of that time the game looked the same. Buy a good business, fix the capital structure, drive operational discipline, dress it up, and sell it for a multiple of what you paid. Financial engineering on one side of the table, operational engineering on the other. That was the playbook, and it worked.

It still works, but it is not enough on its own anymore.

What I am seeing in deal rooms across Silicon Valley and the rest of the state is that sponsors have stopped opening with the old questions. The first question I get now is some version of, where does AI live inside this business, and what is it worth if we get it right?

Let me put some context around that, because it is not theoretical. It is a function of where the market actually sits right now.

The exit door is jammed

Coming into 2026, the industry is living with the consequences of three years of stretched hold periods. DPI, distributions to paid-in capital, has become the metric every LP wants to talk about and that very few GPs want to discuss. The prolonged government shutdown at the end of 2025 took what should have been a strong Q4 IPO window and turned it a lost opportunity. Companies didn’t disappear. They stacked up. Figma cleared the window. CoreWeave cleared the window. Cerebas cleared the window.  The rest are queued, and they are going to stay queued until interest rates come down and risk-on sentiment returns to support the multiples we need to clear the backlog.

When you cannot get out the front door, you find other ways. We saw a real wave of secondaries last year, with sponsors packaging assets into continuation vehicles, sometimes single asset, sometimes baskets, and selling them into other funds. I worked on more of those in 2025 than I had in any twelve-month period in my career. That tool works once or twice, but it does not solve the underlying problem, which is that you have to actually create value during the hold. And in a slower exit environment, you have to create more of it than the model said you needed when you signed the LOI.

That is what is really driving the AI conversation in PE right now. It is not about chasing a trend. It is about defending and expanding margin during a hold that is lasting longer than anyone underwrote.

AI has moved from experiment to operating budget

Across my portfolio company clients, AI has stopped being a slide in the strategic plan and started being a line item. Back office automation, forecasting, supply chain optimization, customer service deflection, code generation inside engineering. I think the lean manufacturing crowd from the 1990s would have a hard time recognizing what is happening to SG&A inside well-run portfolio companies right now.

I had a conversation recently with an operating partner at a mid-market sponsor who told me about their thesis that every new platform deal now starts with one question. What percentage of headcount is doing repetitive cognitive work that an agent could do tomorrow? Two years ago, that was a footnote. Today it is the model.

The numbers track with what I am seeing on the ground. AI and machine learning private equity deal value went from roughly $42 billion in 2023 to north of $140 billion in 2024, and the momentum kept building through 2025. Q1 of 2026 broke every record on the books, with AI taking roughly 80 percent of the venture dollars deployed. When capital moves that decisively, sponsors who are not running an AI-first value creation thesis are going to find themselves selling into a market that prices one in, whether they delivered it or not.

Robotics is no longer just industrial

I came up in the Valley watching robotics live in factories and warehouses. That is not where the next leg is. The advances in computer vision, autonomous systems, and sensor technology have opened up applications that just were not economically viable five years ago. Logistics yards, food service, construction, field services, healthcare delivery. Defense tech and space are particularly interesting right now, because when you sign a government contract you have visibility on five to ten years of revenue, and that kind of stability is hard to come by in AI infrastructure, where people in PE still cannot really bank on what the next five years look like.

For sponsors looking at industries with persistent labor shortages, wage inflation, or seasonal volatility, robotics is a credible operational lever, and it is the kind of investment that can change a business’s unit economics permanently. Permanent margin expansion is exactly what you need when you may be holding the asset another two or three years longer than your model contemplated.

A diligence war story

The diligence I am running on technology-enabled deals these days does not look anything like the diligence I was running in 2022.

We spend real time now on data rights. Whose data is the model trained on, do you have the license, was it scraped, are there indemnities, and are the outputs clean for commercial use? On a recent platform acquisition, we found the target had been training an internal model on a dataset that, when we peeled the onion, included licensed third-party content with field-of-use restrictions that flatly prohibited model training. The deal did not die, but it got repapered, and the seller took a haircut. Founders should assume that their data rights are now getting the same scrutiny in diligence as the cap table.

We spend real time on regulatory exposure. The EU AI Act is being implemented, and state laws on automated decisioning are proliferating in California, Colorado, and New York. If a portfolio company is using AI to make hiring, pricing, credit, or insurance decisions, there is a regulatory diligence problem there, whether the sponsor knows it or not. I am also seeing reps and warranties insurance carriers begin to underwrite AI-specific exclusions, which tells you the market is pricing this risk in real time.

We spend real time on cybersecurity, because the attack surface of an AI-enabled enterprise looks nothing like the attack surface of the same business pre-AI. Prompt injection, model extraction, training data poisoning. I am taking incident response calls on this stuff right now.

And we spend real time on workforce. Mass automation has labor consequences. WARN Act considerations, plant closing rules, severance practices, and in unionized environments, bargaining obligations. A sponsor who plans to take headcount out of a target needs a plan that survives legal scrutiny, not a slide deck.

A tale of two worlds in the talent market

At the TED AI conference last year, I described what I have been calling a tale of two worlds in AI. The hyperscalers and the large model labs are paying compensation packages that early-stage startups simply cannot match, and I have watched a Series A founder lose his lead engineer to a nine-figure package from one of the frontier labs. That is the market we are operating in.

For PE sponsors, this matters in two ways. When you buy a tech-enabled platform, your retention plan for technical talent has to be designed for a world where Google, Meta, and the AI labs are calling your engineers every Tuesday. Single-trigger acceleration, aggressive equity refresh grants, and meaningful cash retention, which used to be reserved for the founder and the CTO, are now what it takes to keep the top of the engineering org in place. And when you are diligencing an AI-native target, founder-engineer concentration risk is real in a way it has never been. Lose two people and you may not have a company.

Operational engineering has become technology engineering

The sponsors who are going to outperform in this cycle are the ones who function less like financial buyers and more like technology transformation platforms. They have operating partners with real AI implementation experience, they have relationships with the model providers and the systems integrators and the automation vendors, and they walk into a target and know within thirty days which workflows to automate, where to deploy agentic systems, and where to pull back.

Sponsors who treat AI as a cost-cutting tactic will get the cost-cutting return, which is a one-time bump that gets competed away inside eighteen months. Sponsors who treat it as a strategic platform investment will get something more durable. Margin expansion that sticks, scalability without proportional headcount, and an exit story that resonates with the next buyer in a market that, by then, will be pricing AI delivery into every model.

Where this leaves us

I tell my PE clients the same thing I told a roomful of them last quarter. Hope is not a strategy, preparation is not optional, and the best deals are won long before the term sheet arrives. That has always been true. What is new is the playing field. AI, automation, and robotics are not a side dish to value creation in this cycle. In a lot of cases, they are the value creation, and the sponsors who treat them that way are the ones I would put my money behind.

The convergence of private equity, artificial intelligence, and robotics in Silicon Valley and across the rest of the country is real, and it is accelerating. Sponsors who internalize that, who build their teams around it, structure deals around it, and put it at the center of diligence, are going to win this cycle. Those who do not are going to spend a lot of time explaining to their LPs why DPI never came back.