As software becomes increasingly agentic, technical due diligence readiness has evolved from an event into an operating capability. As a result, investors, PE operating partners, boards, and corporate development teams are asking a different class of questions. They’re no longer just evaluating whether your architecture scales or your codebase is maintainable. Instead, they want to understand how autonomous systems make decisions, who owns them, what they cost, and how they’re governed. Those are fundamentally different questions, and most engineering organizations, even very good ones, aren’t yet prepared to answer them.

Technical due diligence has always been about reducing uncertainty. In the past, that uncertainty mostly lived inside the technology itself: could the platform scale, was the architecture sound, how much technical debt existed. Those questions still matter. But in the agentic era they’re no longer sufficient, because software increasingly depends on external models, autonomous workflows, AI-generated code, and providers that evolve independently of your release cycle. So the engineering challenge is no longer just building reliable systems. It’s building organizations that can explain, govern, and adapt those systems over time. The technology changed. The bigger change is organizational.

“Show Me Your Agent Logs”

No diligence team actually opens a review by literally asking for agent logs. The phrase is shorthand for something bigger: can you explain what your autonomous systems are doing? Can you trace an important decision back to a responsible human? Do you know every production agent, what it’s authorized to do, and where its authority ends? And can you demonstrate governance rather than simply describe it? Those are increasingly the questions underneath every technical due diligence readiness review, regardless of whether the company is two years old or twenty.

The organizations that answer them well aren’t necessarily the ones using the newest models or shipping the most AI features. Instead, they’re the organizations that have made their engineering systems, and their decision-making, legible. Technology rarely fails diligence because it’s imperfect. It fails because nobody outside the engineering team can understand it.

The Biggest Change Isn’t AI. It’s Continuous Diligence.

Technical due diligence used to happen at predictable moments: before a funding round, during an acquisition, ahead of an IPO. Today, the same companies increasingly revisit those questions long after the deal closes. Private equity operating partners are building teams that continuously evaluate portfolio companies using the same agentic tooling they used pre-acquisition. Boards are asking for deeper visibility into AI initiatives before approving additional investment, and internal audit functions are expanding their own reviews to cover AI governance and provider dependencies.

Diligence is becoming less of a milestone and more of a management practice, and that distinction matters. If your organization only becomes diligence-ready when a transaction appears on the horizon, you’re already behind. In practice, what makes a company easy to diligence is usually what makes it easier to run day to day, whether or not a deal is on the calendar. Good governance isn’t something you create for investors. It’s something investors recognize.

Your First Diligence Reviewer May Not Be Human

There’s another change getting far less attention. Historically, engineers wrote technical documentation for expert readers who could infer intent from incomplete documentation and fill gaps through conversation. Increasingly, the first reviewer of your technology isn’t a person at all. Agentic diligence tools can ingest thousands of documents, diagrams, contracts, and repositories in hours rather than weeks. They don’t remember hallway conversations, and they don’t know that “everyone understands how this service works.” They evaluate what’s documented, and nothing more.

That’s a real shift for technical due diligence readiness. For years, incomplete documentation slowed diligence down. Today, incomplete documentation reads as incomplete governance. A human reviewer might spend an hour talking with your team to understand an undocumented AI workflow. But an autonomous reviewer just logs the gap and moves on. Machine-readable governance is becoming just as important as machine-readable code.

Governance Is Becoming the New Technical Moat

This is why the conversation has shifted. The hard problems are no longer limited to distributed systems, cloud architecture, or software delivery. Who approved this autonomous workflow? Who reviews AI-assisted code before it reaches production? What happens if a model provider changes pricing, terms, or availability overnight? Who owns an agent whose decisions span multiple business functions? How quickly can the organization explain a critical AI decision six months later? These aren’t purely technical questions anymore. They’re governance questions, and governance is increasingly what separates organizations that inspire confidence from the ones that generate uncertainty.

That doesn’t mean engineering suddenly matters less. Instead, it means the job expanded. The modern CTO isn’t just responsible for building reliable systems. They’re responsible for building organizations that can explain those systems, at any company stage, to someone who wasn’t in the room when the team made the decisions.

What Modern Technical Due Diligence Readiness Measures

That shift changes what “ready” means. A clean codebase is no longer enough. Neither is a polished architecture deck or a well-organized data room. Modern technical due diligence readiness increasingly measures whether an organization has built the governance structures needed to operate autonomous technology responsibly over time: who reviews AI-generated code, who manages model dependencies, who quantifies technical debt, who monitors autonomous systems, and, most importantly, who remains accountable as machines make more of the operational decisions. The question isn’t whether your AI works. It’s whether your organization can explain why it works, how it’s governed, and what happens when it doesn’t. That’s where the most common governance gaps start to show, and they show up the same way at a Series B startup and a fifteen-year-old enterprise.

Five Governance Gaps Modern Diligence Exposes

Every engineering organization has technical debt and legacy decisions it would make differently today. Still, that’s normal, and it isn’t what sinks a review. Instead, the companies that struggle in technical due diligence readiness reviews are the ones that can’t explain what they have, why it exists, or who’s responsible for it. The same five gaps show up again and again across startups, PE-backed companies, and mature enterprises, a pattern that comes up in Hoola Hoop’s CTO Roundtable series, under Chatham House rules.

The Pattern Behind Every Finding

At first glance, ownership, technical debt, code review, provider dependencies, and unit economics look like five unrelated problems. They’re actually symptoms of the same one: governance. Every one of these findings comes back to four questions. Can your organization explain what exists, and why? Does it know who’s accountable? And can it explain what happens when something changes? Those aren’t engineering questions. They’re organizational questions, and they’re becoming some of the most important technical questions a CTO answers, whatever stage the company is at.

What Technical Due Diligence Readiness Looks Like in Practice

Closing these gaps rarely requires rewriting your platform. Most organizations already have the knowledge. What’s missing are the artifacts: the documentation, the named ownership, the operational discipline that turns what your team knows into something a stranger can verify in two weeks. So organizations that handle technical due diligence readiness well tend to share six governance artifacts, and they didn’t build them for investors. They built them to run the business better.

The Underlying Principle

Governance isn’t documentation, and it never has been. Technical due diligence readiness has always rewarded organizations that can demonstrate preparedness, whether the reviewer is a partner at a PE firm, a board member, or an agent reading your data room at two in the morning. Still, none of this replaces the fundamentals. If you haven’t documented your architecture decisions or priced your technical debt yet, my original technical due diligence guide is still the right starting point. What’s changed since I wrote it is the layer on top: agent governance, model provenance, and AI unit economics are now part of the same conversation, not a separate one.

Questions to Sit With

Whether your next diligence event is a term sheet, an annual portfolio review, or a board asking hard questions ahead of an acquisition you’re making, these are worth working through honestly now, before someone else asks:

• Can you name a single accountable owner for every autonomous system in production right now?
• Is your technical debt register priced in business terms, or still written in engineering language nobody outside the team can act on?
• If a diligence team, human or agentic, asked for your agent inventory tomorrow, does that document already exist?
• What happens to your core AI features if your primary model provider changes pricing or shuts off access next quarter, and is that answer written down anywhere?
• Has your team actually rehearsed your operational playbook for an AI failure, or does it only exist on paper?

A Final Thought

Technical due diligence readiness in 2026 isn’t a favor you do for a future buyer. It’s the same operating discipline that makes a CTO’s own job easier: knowing what your systems cost, who’s accountable for them, and what happens when something breaks. A deal or a portfolio review doesn’t create that need. Instead, it just puts a deadline on work that was already worth doing, whether you’re three weeks from a term sheet or three years past your last one.

I’ve sat on both sides of this table, as the CTO walking a company through an acquisition and as the person building the governance case from the inside. In fact, most of what a 2026 technical review flags isn’t a surprise to the engineering team. It’s a fact everyone already knew, and had never written down where anyone else could find it.

Leigh Newsome

Partner, Hoola Hoop · CTO Coach

Leigh Newsome is a Partner at Hoola Hoop and a CTO coach with 25 years of experience scaling product and engineering teams. He has worked with a wide range of startups and global enterprises, including Avid, Digidesign, WPP, and Kantar/Millward Brown, and successfully led TargetSpot (backed by Union Square Ventures, Bain Capital Ventures, and CBS) through its acquisition to Radionomy Group (Vivendi). When he’s not coaching CTOs, you’ll find him teaching digital audio to graduate students at NYU, building audio and signal processing applications, or flying fixed-wing aircraft, but never all three at once.

The phrase “AI-native trust paradox” is doing a lot of rhetorical work this month, and much of it is misleading. Output is up. Trust is down. The headline numbers are easy to dramatize, and the conclusion offered up across LinkedIn and Slack communities is usually some version of “the industry needs more time. Let’s see where things are in a few months.” I have coached enough CTOs through the version unfolding right now to recognize what is actually going on. The AI-native trust paradox is not a paradox. It is a lag between a production process that changed twelve months ago and an operating model that has not changed at all or very little.

Why the AI-Native Trust Paradox Is the Wrong Frame

Most of what I read about the AI-native trust paradox starts in the same place. A statistic about velocity. A statistic about declining trust. A hand-wringing conclusion that the industry is figuring it out. That reading is too kind to the leaders running the orgs, because it implies the gap is mysterious. The gap is not mysterious. The reality is the team’s job changed, yet the structure around the job did not. The more useful question is not why the gap exists but who is responsible for closing it.

What engineering leaders have not absorbed is the harder, second-order question: what work does the organization now exist to do at all? If a team is no longer primarily authoring code, then “engineering” has quietly become a different job. The AI-native trust paradox is what surfaces when leaders run the new job using the operating manual for the old one. Frame it that way and the answer stops looking like a research problem and starts looking like a familiar operator problem, the kind any seasoned CTO has run a version of before.

None of this means tooling quality is irrelevant. Better models absolutely reduce review burden and improve local correctness. But even perfect local correctness would not solve the organizational problem underneath this shift. An engineering organization still has to define accountability, evaluation criteria, architectural stewardship, and system-level judgment. Those are management design problems, not inference problems.

Four Reframes Underneath the AI-Native Trust Paradox

If I had ten minutes with a CTO who wanted to get unstuck, I would start with four reframes. Each is a sentence. Each carries a large structural consequence underneath, and each is the kind of thing senior engineers know is true but have not heard a leader name out loud yet.

What I’d Do First In The CTO Chair

I get some version of “where do I start” almost every week from CTOs trying to get out from under the AI-native trust paradox. The honest answer is not glamorous and does not involve picking a tool. Three moves, in order.

What this looks like in practice: a CTO I work with leads a 150+ person engineering org at a late-stage infrastructure company. When we rewrote the senior and staff engineer JDs together, the hardest conversation had nothing to do with AI tooling. It surfaced a recently promoted engineer whose output was almost entirely code volume: pull requests with no written rationale, no explanation of tradeoffs, nothing a teammate could use to understand or push back on a decision six months later. Under the old definition he was a strong performer. Under the new one he was operating invisibly. The rewrite forced a calibration conversation the leadership team had been quietly avoiding, and within a few weeks the broader team had noticed the change in how reviews were being run.

What the Data on the AI-Native Trust Paradox Actually Says

I am suspicious of articles that bury the evidence, so here is the data worth holding in mind while running the three moves above. I am putting it late on purpose: if I led with it, this piece would read as a summary of someone else’s research instead of the operator argument I came to make.

19 of 219 is not a tooling story. It is a leadership story about which documents got rewritten and which did not. Model quality will improve. But organizations waiting for model improvement alone to resolve the trust gap are waiting for a tooling fix to solve a management-design problem. The resolution comes from the redesign that follows the production change, which is the same work that has closed every prior productivity-shift gap in engineering.

A Final Thought

The AI-native trust paradox will not be resolved by a vendor demo, an offsite, or a smarter review agent. It will be resolved by CTOs running the org-design playbook they already know, applied to a job that changed under them while they were focused on the tooling. The mechanics are not new. The harder part is the honesty required to admit the work changed, and to say so out loud before the documents catch up.

Two earlier pieces sit directly next to this one. The continuous product roadmap article covers the cadence side of the same operator problem. The AI-native team topology piece covers the structural side. Read together, they describe the same shift from three angles, and the AI-native trust paradox is what shows up when none of them are being attended to.

Leigh Newsome

Partner, Hoola Hoop · CTO Coach

Leigh Newsome is a Partner at Hoola Hoop and a CTO & CPO coach with 25 years of experience scaling product and engineering teams. He has worked with a wide range of startups and global enterprises, including Avid, Digidesign, WPP, and Kantar/Millward Brown, and successfully led TargetSpot (backed by Union Square Ventures, Bain Capital Ventures, and CBS) through its acquisition to Radionomy Group (Vivendi). When he’s not coaching CTOs, you’ll find him teaching digital audio to graduate students at NYU, building audio and signal processing applications, or flying fixed-wing aircraft, but never all three at once.

For CTOs and CPOs, managing up to the CEO is often the leadership skill they were least prepared for. The problem rarely looks like a problem at first. You’re delivering. Your team is shipping. The CEO seems satisfied. And then, often suddenly, something shifts. You find yourself out of sync. Decisions begin to happen without your input, with your CEO working around or beneath your role to move things forward. Your strategic proposals don’t land the way you expected. The CEO seems to be operating from assumptions about your team that aren’t accurate, and you’re not sure how that happened.

What I’ve observed, across a lot of these relationships, is that the breakdown rarely starts with a single incident. It accumulates through a pattern of missed expectations, ones the CTO or CPO didn’t even know existed.

Why Managing Up to the CEO Is Hard for CTOs and CPOs

The CTO and CPO roles sit in a uniquely difficult position when it comes to managing up. Unlike a CFO or CMO, whose domains are reasonably legible to most CEOs, your world, whether that’s engineering architecture, technical debt, or product strategy, is opaque in ways that matter. The CEO can’t easily assess whether your team is performing well, whether the risks you’re describing are serious, or whether the timeline you’ve committed to is realistic.

That opacity creates a relationship that depends almost entirely on trust. And trust, in this context, relies on a very specific kind of communication: the ability to translate what’s happening in your world into language that connects to what the CEO cares about most.

Most CTOs and CPOs are never taught how to do that translation well. So they default to what they know: status updates, technical briefings, roadmap reviews. These feel thorough, but they often miss the point entirely.

Five Patterns That Erode the Relationship

These are the patterns I see most consistently when CTOs and CPOs struggle with managing up to their CEO:

The Common Thread

None of these patterns stem from incompetence or bad intent. They develop because most CTOs and CPOs are never given a map for managing up. The encouraging thing is that all five are addressable, not through dramatic behaviour change, but through more deliberate communication habits applied consistently.

What Strong Managing Up Looks Like for CTOs and CPOs

The CTOs and CPOs who excel at managing up to their CEO aren’t necessarily the ones doing the best technical work. They’re the ones who’ve developed a specific set of communication habits that make their work legible, their risks visible, and their judgment trustworthy.

The Underlying Principle

Across all five habits, there’s a consistent thread. The CTO or CPO who manages up well isn’t trying to impress their CEO. They’re trying to make the CEO’s job easier. That shift in intent changes almost everything about how you communicate upward.

A Note on Timelines and Commitments

The timeline question deserves its own attention because it’s where managing up most often breaks down for CTOs and CPOs. “When will it be done?” is one of the most loaded questions a CEO can ask, and almost nobody answers it well.

The wrong answer is false precision: a specific date given under pressure that becomes an anchor in the CEO’s mind, surfaces in board conversations, and lingers long after you’ve forgotten you ever said it. Evasion is no better: “it depends,” “we’re still scoping it,” or an answer so hedged it communicates nothing.

The right answer is confident uncertainty. Something like: “Based on what we know today, we’re targeting the end of Q2. The biggest risk to that is X, and here’s how we’re managing it. I’ll give you an updated view in three weeks when we know more.” This gives the CEO what they actually need: a planning horizon, the key risk, and a date when they’ll have better information. It also models the kind of thinking that builds credibility over time.

The same principle applies to scope. When a CEO asks “can we add this feature?” the answer almost always involves a trade-off. The CTO or CPO who says “yes, and here’s what moves” is far more useful than the one who says “it’s complicated” or, worse, says yes and quietly absorbs the cost into the team.

Questions to Sit With

If you’re a CTO or CPO thinking honestly about managing up to your CEO, these are worth working through:

• If your CEO had to describe to the board what your team accomplished last quarter, what would they say? Is that what you would say? If there’s a gap, that’s a communication gap, not a performance gap.
• When did you last raise a meaningful risk to your CEO before you had a plan to address it? If you struggle to find an example, consider what that pattern is costing you in terms of trust.
• Do you have a consistent, self-initiated cadence for the CEO relationship, or does most of your upward communication happen reactively, when something is needed or something has gone wrong?
• Does your CEO understand the key constraints your team is operating under, such as the architectural decisions, the accumulated technical debt, and the hiring gaps, or would those feel like surprises if they came up in a board conversation?
• What does your CEO actually care most about right now? Not what they say in 1:1s, but the things driving their decisions. How explicitly does your work connect to those things in the way you communicate it?

A Final Thought

Managing up well doesn’t mean being political. It doesn’t mean softening hard truths or packaging bad news attractively. It means developing the discipline to communicate your world in terms that are useful to the person you’re communicating with. And it takes real courage to do it consistently: to raise the uncomfortable thing early, to push back on the unrealistic expectation, to say “I don’t know yet” to someone you want to inspire confidence in. If that’s a dimension you want to explore further, our Courage to Lead series goes deep on exactly that.

Your CEO is making decisions about the whole business, often with incomplete information and real time pressure. The more you can make your piece of the business legible to them, the better those decisions get. That’s good for them, good for your team, and ultimately good for you.

The CTOs and CPOs who build strong upward relationships aren’t the ones who have the smoothest delivery or the most polished presentations. They’re the ones who understand that their job doesn’t stop at the boundary of their own organization, and who invest in the relationship with the same seriousness they bring to everything else.

Leigh Newsome

Partner, Hoola Hoop · CTO & CPO Coach

Leigh Newsome is a Partner at Hoola Hoop and a CTO & CPO coach with 25 years of experience scaling product and engineering teams. He has worked with a wide range of startups and global enterprises, including Avid, Digidesign, WPP, and Kantar/Millward Brown, and successfully led TargetSpot (backed by Union Square Ventures, Bain Capital Ventures, and CBS) through its acquisition to Radionomy Group (Vivendi). When he’s not coaching CTOs, you’ll find him teaching digital audio to graduate students at NYU, building audio and signal processing applications, or flying fixed-wing aircraft, but never all three at once.