Board Evaluation Best Practices: A Three-Year Plan | Louis Lehot
A Three-Year Plan for Effective Board Evaluations By Louis Lehot and Kelly Boyd Intensifying scrutiny. Faster risk cycles. Rising stakeholder expectations. In this environment, a board that does not systematically evaluate itself cannot credibly claim to steward long-term value. When designed well, board evaluations are the boardroom’s most reliable instrument for self-correction and maintaining strategic alignment and cultural health. However, while virtually all large U.S. public companies disclose that an evaluation process exists — and a growing majority now assess individual directors — relatively few explain how insights from these assessments translate into action. The imperative is clear: boards should move beyond process compliance to a disciplined, outcome-oriented evaluation system that builds future-ready boards. Following a three-year road map is an effective best practice, though not a one-size-fits-all solution. Boards should calibrate the cadence to their business context. Such a plan uses year one to set priorities, year two to focus on disciplined execution, and year three to reinforce accountability, inform refreshment, and set the next evaluation cycle. This sequence builds shared information and priorities before asking for behavior change. It also conducts individualized feedback after the organization defines what “good” looks like for its board in its own specific strategy context. The road map is designed to slot into the annual strategy and risk calendar, minimizing disruption. Year One: Baseline Comprehensive Assessment In year one, the board can establish a candid baseline of governance behavior and performance standards through a comprehensive, three-tier assessment of the full board, each standing committee, and — as appropriate — individual members. The objective is to produce a clear and agreed-upon picture of how the board performs its work, where governance disciplines are strong, and where targeted improvements will have the greatest impact on decision quality and strategic oversight. To maximize candor, comparability, and specificity, the evaluation should blend three approaches: A structured questionnaire that establishes quantifiable baselines and trend lines One-on-one interviews that surface context, nuance, and divergent perspectives A facilitated discussion that enables the board to align on two to five priority actions and the measures of success that will be tracked through the three-year cycle When baseline metrics are established for a handful of core indicators, the board may then observe directional movement rather than rely on anecdotes alone. The scope of the year one assessment is intentionally broad but disciplined. It should cover: Strategy and risk oversight CEO and leadership succession planning Board composition and refreshment Culture and dynamics Governance infrastructure — including agenda design, information flow, and committee scope Particular attention should be paid to participation patterns: who speaks, who listens, and whether challenge and dissent are welcomed and integrated. The assessment should also review the clarity of boundaries between board and management responsibilities. In parallel, the assessment should incorporate selected management perspectives to test where the board adds the most strategic value, how effectively it sets expectations, and where it may inadvertently drift into operational detail. These inputs should be tightly scoped and confidential to protect constructive candor. External support and legal calibration are central to credibility and risk management. An external facilitator strengthens objectivity, protects anonymity in interviews, and benchmarks against practices and disclosures from peer companies and global markets. Where useful, third-party observation of board meetings supplements the evaluation with an objective view of dynamics and decision processes. Internal or external counsel involvement should be considered at the outset to determine privilege, recordkeeping, and discoverability risk of assessment data. Written surveys can establish quantitative trend lines while interview synthesis and oral debriefs limit unnecessary written footprints. Following the baseline assessment, typical year one actions include: Redesigning agendas to increase time spent on forward-looking strategy and enterprise risk Sharpening board materials to focus on decision-ready content Clarifying committee mandates and oversight boundaries to eliminate duplication and gaps Identifying target capabilities for future refreshment — including artificial intelligence, cybersecurity, data governance, supply chain resilience, and regulated markets Each action should be translated into a concrete work plan with interim checkpoints and a clear definition of what “better” will look like by the end of the cycle. Progress should be reviewed quarterly via a concise dashboard that ties each priority to milestones and observable outcomes. The year one output should not be a static report — it is a living action register that names owners, milestones, and success measures for the agreed priorities, and should be continuously updated. Year Two: Execution and Targeted Pulse Checks Year two is about accountability, disciplined execution, and evidence of behavioral and process change. The board should embed the year one action plan into its operating rhythms — committee plans, board agendas, leadership routines, and one-on-one meetings with members of the management team — and then use targeted pulse assessments to test whether the changes are taking hold. Committee chairs and management should implement agreed-upon changes with routine progress check-ins that emphasize observable outcomes, not activity. Examples include: Increasing the cadence and clarity of enterprise risk reporting so the board can see emerging risk migration and management response Commissioning committee-level deep dives on salient risks tied to the company’s strategy Aligning board leadership rotations and succession planning to the director skills and experience matrix so that oversight capability deepens over time Targeted pulse checks are compact instruments designed to minimize fatigue and disruption while providing real-time feedback on whether year one priorities are translating into better board work. They typically test core indicators across strategy focus, risk reporting quality, board culture and inclusion, onboarding effectiveness, and continuing director education. The cadence should be light but consistent — one pulse check at midyear and one at year-end, supplemented by micro-surveys after significant decisions — so that the board can spot behavioral drift early and reinforce positive changes before habits regress. Culture and the board’s inherent power dynamic receive sustained attention in year two because they determine whether process changes stick. The chair or lead independent director should model inclusive deliberation, actively draw in quieter voices, and normalize brief reviews following major decisions. Dominant voices
The Habits of Boards That Get It Right
The Habits of Boards That Get It Right Key Takeaways Bad board decisions rarely come from bad directors; they come from how the room is run. Send the board deck genuinely early and ask directors in advance what they want to discuss. Protect dedicated agenda time for strategy, and build trust between directors before a crisis forces it. The best chairs bring quiet directors in first so the loudest voice does not anchor the discussion. Treat board self-assessment as a living, ongoing practice rather than an annual formality. About Board Service Between us, we have spent more than 25 years as scriveners in boardrooms — taking minutes, advising chairs, logging somewhere in the zip code of 500 hours a year in those seats. You would think, after that many meetings, the patterns would stop surprising you. They do not. The thing nobody says at the dinner afterward is that the bad decisions almost never come from bad directors. The people in the room are accomplished and prepared. They did the reading. In our experience, what frequently goes wrong is the room itself — how it gets run, and what it quietly makes hard to say. NACD Northern California put a group of directors around a table last week to talk about why good boards still talk themselves into bad decisions. We were privileged to co-lead the discussion alongside our friends Tracey-Lee Brown and Matt DiGuiseppe, both Directors in the Governance Insights Center at PwC. What follows are some of the ideas we took away from that conversation. The Data That Sparked the Conversation PwC’s latest Annual Corporate Directors Survey had just landed, and the headline numbers are striking: More than half of directors now say at least one of their fellow board members should go — the highest in the twenty years of the survey. Most also say their own self-assessment does not tell them much. Only about a third of executives in the PwC and Conference Board effectiveness survey say their board is doing a good job. Read those numbers the wrong way and they sound like a confession. Read them right and you see directors raising the bar on themselves faster than their tools can keep up. When the directors and the CEOs are both asking for more, they are usually after the same thing. We do not read those numbers as a problem with the people. We read them as a sign the work is getting more serious faster than the habits around it. The best boards we sit on have already changed theirs — not through any grand overhaul, but through small, deliberate choices about how the room works. Here is what they actually do. What the Best Boards Actually Do 1. Win the Pre-Read It starts before anyone sits down. Send the deck early — genuinely early, not late-Friday-for-a-Monday-meeting early — and directors arrive ready to use their judgment. Send it the night before and the meeting turns into a book report read aloud, with management holding the pen on the framing because nobody else had time to question it. The good chairs go one step further: they ask, ahead of time, what the directors want to spend the meeting on. That single move flips the dynamic from a meeting the board sits through to one the board actually runs. It sounds obvious. In practice, surprisingly few chairs do it. 2. Protect Time for Strategy None of that matters if the agenda leaves no room for the conversation directors actually came for. Ask any director what they want more of, and the answer is never another operating update. It is the conversation about where the company is going and what it is missing. That is the whole reason these people are in the room — and it is also the only item on the agenda without a deadline, which means it is the first thing to get cut when something catches fire. Put it on the calendar and guard it, and the board keeps its bearings. Let it slide, and you learn about the strategic problem a quarter too late to do much about it. 3. Build Trust Before You Need It That conversation runs on trust — the thing everyone files under “soft” but which is in fact the most practical asset a board has. The candor to disagree well does not get built in the meeting where you need it. It gets built in the hallway, the dinner, the unscripted half hour, long before the hard call lands. The problem is sharpest at late-stage private and newly public boards, where the directors barely know each other yet and the company is moving faster than the relationships. When trust is there, the hard conversation stays about the problem. When it is not, a board of polite strangers facing its first real test goes quiet — which is the one thing you cannot afford right then. 4. Run the Room by Design Trust matters just as much in how the conversation itself is run, and here the chair is doing real design work, not just minding manners. The best chairs bring the quiet directors in first. They hold the loud ones — themselves included — for later, so people form their own view before the room settles on one. It takes discipline, because the instinct is always to let the person with the strongest opinion anchor the discussion. PwC’s data bears this out: directors have started prizing the colleague who says less and means more over the one who fills the air. Run it the other way — let the most confident voice set the frame in the first two minutes — and everyone who follows is reacting instead of thinking. Good governance is usually quiet by design. 5. Know Where You Add Value This carries over to how you show up as an individual director. The most effective ones know the two or three things where their experience actually changes the answer, and
The Sidewalk Is the Lab: Hard Things, Round Three
The Sidewalk Is the Lab: Hard Things, Round Three Notes from a conversation about Physical AI with Touraj Parang Last Thursday, May 28, a small room in Palo Alto stayed later than it should have — hopefully the signal that an evening worked. Mavka Capital and Foley convened the third installment of “Hard Things,” our invite-only series for the founders, investors, and builders working at the frontier of physical AI. The conversation, moderated by my partner in this series Vitaly Golomb of Mavka Capital, ran past the point where people normally start drifting toward the door. Nobody drifted. We built Hard Things around the shift from bits to atoms — from intelligence in the cloud to intelligence embodied in machines that have to survive contact with the real world. The bits are easy to write about. The atoms are where companies break, and where the honest lessons live. So we keep the format deliberate: no stage, no deck, just one operator in a room with people who build. Our most recent guest was Touraj Parang, COO of Serve Robotics and an advisor to Pear VC. If you wanted a résumé that proves a point about physical AI, you would build his. A liberal arts thoroughbred — JD from Yale, philosophy and economics at Stanford — who began as a white-shoe corporate attorney before crossing over to the operating side. He has since lived through roughly 300 venture rejections, a bank account that once hit $6,000 against a six-figure monthly burn, a spinout from Uber, a Nasdaq listing, and a fleet of more than two thousand sidewalk robots scaling across major downtown metros. He also wrote the book on the subject most founders avoid: Exit Path: How to Win the Startup End Game (McGraw Hill, 2022). At Hard Things, Touraj did not show up with a thesis to sell. He arrived with scar tissue — the only credential I trust in this category. Here is what he told the room. Go Where You Have Insight, Not Where the Capital Is Going This is the lesson founders most need to hear in 2026 and are least equipped to act on, because the pull of capital is loud. When a sector gets hot, money floods in and founders rush to stand where it lands — but as Touraj put it, proximity to capital is not proximity to a business. The best physical AI companies are founded not by people who noticed physical AI was funded, but by people who possess proprietary knowledge of an industry, see the operational seam outsiders cannot, and then go looking for the technology to exploit it. His own seam was specific. Because Serve was born inside Postmates, the team could see the food-delivery data directly — and it told them something the market had not priced: roughly half of all U.S. deliveries cover a median distance of about two and a half miles, short enough for a sidewalk robot, in a market of millions of deliveries a day. Three trends were bending the right way at once: falling hardware cost, rising AI capability, and ubiquitous connectivity — against a rising cost of labor. The pitch reduced to a line he still uses: why move a two-pound burrito in a two-ton car. He showed that same picture to the VCs, and most found reasons to doubt it. The insight was not that the opportunity existed; it was that he could see, from inside the data, that the conditions had arrived. In Hardware, Strategic Capital Beats Venture Capital I have spent a career on the financing side of this question, and Touraj’s argument here is not against venture capital — it is about fit. Traditional venture is built for software economics: low marginal cost, fast iteration, a return profile that tolerates a portfolio of zeros. Hardware is capital-intensive, its cycles measured against the physical world, its timelines indifferent to a fund’s clock. He was direct about why the VCs balked: robotics needs a lot of money, and the fear that turned them off was the cram-down — fund this round, then watch the larger rounds the hardware demands wash you out. Strategic backers like Nvidia and Uber carried the day instead, and in his telling that validation is part of what made going public possible at all. A well-structured strategic deal, he argued, answers what venture cannot: patient capital that does not panic when the next milestone is a manufacturing problem, industry validation worth more than the money attached to it, and customer access — the hardest thing for a hardware startup to manufacture on its own. He also took on the old worry that one strategic investor taints you with every other partner and acquirer. Mostly overblown, he thinks: Uber sits on Serve’s board, and Serve still struck a delivery partnership with DoorDash. You can do that — but only if the deal is structured so you do not give away what would make those future moves impossible. The governance rights, change-of-control terms, and rights of first refusal that quietly decide who you may sell to in four years have to be negotiated with the eventual exit already in view. The Unconventional Road to Nasdaq Vitaly asked Touraj directly about the going-public story and initially framed it as a SPAC. Touraj’s correction is worth keeping. What Serve did was an alternative public offering, not a SPAC. In a SPAC, retail investors put money into a blind-pool shell that then hunts for a company to buy. An APO is the sober cousin: you merge into a clean shell — public-reporting, but with no operations and no public retail investors — bring accredited investors in alongside you, then uplist from the OTC market to Nasdaq through an underwritten offering. Serve traded over the counter while it built the operating history an exchange demands, then uplisted, where it trades today as SERV. One detail surprised the room: Serve was essentially pre-revenue when it listed — you can qualify on an enterprise-value
How to make IPOs great again
How to make IPOs great again By Louis Lehot and Patrick Daugherty, Foley & Lardner LLP | June 10, 2026 Louis Lehot and Patrick Daugherty of Foley & Lardner LLP discuss the SEC’s May 2026 proposals to make IPOs more attractive, and outline additional reforms needed to bring companies back to the public markets. In May, the SEC made its most ambitious proposals in a generation to bring companies back to the public markets. That is the right goal and a strong start. We endorse it, but more is needed. The Argument in Brief The public market has been shrinking for thirty years. The number of U.S.-listed companies has fallen by roughly half since the mid-1990s, while trillions in growth capital now stays private — out of public view and inaccessible to retail investors. The SEC’s May 2026 proposals are the right response. The four rulemaking proceedings promise to lower the cost of being public and return disclosure to what matters to investors. More is needed. To bring companies back into the public markets, the SEC must also fix the rules that keep them out: the gun-jumping regime, the research and trading deserts for smaller companies, and a litigation system that punishes newly-public companies. Why the Public Market Is Shrinking For most of the last century, “going public” was the goal. A company that reached a certain size raised capital from the public, and ordinary investors shared in its growth. That bargain is breaking down. The number of U.S.-listed public companies has fallen from roughly 8,000 in the mid-1990s to about 4,000 today — a decline of around 40 percent. The capital did not disappear. It moved to the private markets, which now hold roughly $8.5 trillion in assets under management and ask for almost none of the disclosure, accountability, or investor protection that the public markets require. A generation of growth has happened while most Americans cannot invest in it. This is not only a problem for companies and their bankers. It is a problem for ordinary Americans. When a company stays private through its highest-growth years and lists only after the best gains are behind it, those gains accrue to a narrow group of venture funds, private-equity sponsors, and institutional insiders. The teacher, the firefighter, and the small-business owner saving for retirement through a 401(k) or an IRA are left to buy in late, if at all. Public markets are supposed to be the one place where anyone can own a piece of the country’s growth — where the discipline of transparency, audited financials, independent boards, and real accountability protects the people who invest. Every company that chooses to stay private is, in effect, a door closed to the public investor. Reversing that is not just sound capital-markets policy; it is a matter of who gets to participate in American prosperity. SEC Chairman Paul Atkins has made reversing this trend the center of his agenda, under the banner “Make IPOs Great Again.” We have spent a combined seventy years advising technology, life sciences, and clean-energy companies — the companies the public markets were built to finance — and we think he has the diagnosis right. What the SEC Proposed in May 2026 Across four rulemaking proceedings and one invitation, the Commission addressed nearly every stage of public-company life. It proposed to let companies report twice a year instead of four times, on the theory that the cadence of disclosure should be a business judgment rather than a federal mandate. It proposed to open the fast, flexible “shelf” registration system — long reserved for the biggest companies — to nearly all public companies, including the newest and smallest. It proposed to collapse a tangle of overlapping “filer” categories into a simpler framework, to exempt roughly four-fifths of public companies from the most expensive recurring audit requirement, and to guarantee newly public companies a multi-year on-ramp before the heaviest obligations apply. It also proposed to withdraw the 2024 climate-disclosure rules — which a federal court had paused — on the ground that they compelled vast disclosure untethered to what a reasonable investor needs to know. Then the Chairman went to Stanford and opened a public comment file inviting other “bold and creative” ideas to modernize the way companies go public. That invitation matters as much as the proposed rules because it acknowledges that more work needs to be done. Why This Is the Right Direction Each May proposal points the same way: toward a public market that is cheaper to join, less burdensome to occupy, and focused on information that actually informs an investment decision. For a mid-sized company weighing an IPO, relief from a single multi-million-dollar annual compliance ritual can be the difference between listing and staying private. Returning disclosure to the standard of economic materiality — the touchstone the Supreme Court set decades ago — would spare companies from providing information that few investors read, while ensuring they still provide the information that investors need. None of this weakens investor protection. The antifraud laws remain in force. What changes is that the cost of being public stops being a tax that only the largest companies can comfortably pay. For more on how the capital markets landscape is evolving in 2026, see: 2026 IPO Market Outlook: Momentum, Deregulation, and the Path to Liquidity. Why the SEC’s May Package Is Not Yet Enough Here is the uncomfortable truth the May package does not reach: companies do not avoid the public markets mainly because reporting is expensive. They avoid them because the private markets now offer everything a growing company needs, with no friction. You cannot draw companies into the public market simply by discounting the rent charged to public companies when the alternative is unlimited and unregulated. Three barriers, in particular, do more to keep companies private than any periodic report. The first is the gun-jumping rules that govern what a company can say while going public. Written in 1933 and last meaningfully updated in 2005, they effectively silence
Software Valuation Multiples 2026: A Guide for Founders | Louis Lehot
Software valuation multiples 2026 By Louis Lehot. From garage to global, since Y2K. A read on Rob Bartlett’s June 2026 Jefferies Software Valuation Update, for the founders, CEOs, and directors who have to make a decision about it. Friends, I am not a banker, and I do not call markets. But I read Rob Bartlett’s monthly Jefferies note the way I read an indemnity — closely — because it tells me what the other side already knows. The June 2026 update landed last week. It quietly rewrites the comp your company gets measured against the next time you raise or sell, and you should know about it before your next board meeting, not after. Here is the number that should reset the conversation. As of late May, exactly four public software companies are growing revenue north of 30 percent next year: Broadcom, Oracle, Palantir, and DigitalOcean. Four. They trade near 19.7 times forward revenue. Everybody else lives well below that. The 10 to 20 percent growth crowd trades around 4.7 times. Roughly 85 percent of public software now grows under 20 percent, against about half the universe at the 2021 peak. The high-growth club did not get re-rated. It got depopulated. What Is Actually Driving the Shift in Software Valuation Multiples This is not a rate story. It is the SaaSpocalypse — the name trading desks gave the February selloff that wiped out roughly $285 billion of software value in forty-eight hours. No earnings miss. No rate hike. No recession to blame. The trigger was a batch of AI agent plugins for legal, financial, and sales workflows, and the market asked the one question that now hangs over every per-seat business: Why pay for ten licenses when one agent does the work? I have watched a lot of technology shifts in this valley over thirty years. This one is different. Coding agents have collapsed the cost of building software. Think about how a project used to be shaped: Three weeks on design and architecture Eight weeks of coding and engineering Three weeks on deployment The engineering in the middle was the expensive part — exactly what SaaS sold against. Don’t hire eight engineers for eight weeks. Just buy the seat. That shape has flipped into a barbell. Design still takes a couple of weeks, because deciding what to build is still human judgment. Deployment, integration, and security review still take one to three weeks. But the coding in the middle has gone from eight weeks to somewhere between a day and a week. The bottlenecks moved to the two ends. The middle — the part SaaS got paid for — has nearly disappeared. That is the whole ballgame for software valuation multiples in 2026. When the expensive parts are judgment at the front and trust at the back, the old build-versus-buy math tips toward build. A customer who does not like a renewal quote can credibly threaten to stand up its own internal tool in days. Even when it does not, the threat alone drags down what a vendor can charge. Klarna walking away from its CRM in 2024 was not a stunt. It was the leading edge. AI is not eating startups. It is eating SaaS. No serious buyer or sponsor wants to underwrite recurring revenue that a competitor — or the customer itself — could rebuild in a weekend. When you cannot tell whether a target’s ARR is durable or a melting ice cube, you do not pay a premium. You do not pay at all. That uncertainty, more than any move in rates, is what has frozen the software M&A market. Seat-based pricing was beautiful because revenue grew automatically as headcount grew. Agents break that link. Net revenue retention — the metric that justified premium multiples for a decade — softens the moment expansion stops happening on its own. For the first time I can remember, software as a group trades at a discount to the S&P 500. Here is the honest part, and the part I tell every board: nobody actually knows yet whether AI eats SaaS or feeds it. The bear case is structural: seats compress, features commoditize, the agent becomes the interface and the app becomes plumbing. The bull case is that this is a snake shedding its skin. Shiny AI-native tools get adopted fast and churn out just as fast. The incumbents with real data, regulated-industry trust, and deep workflow integration are the ones that survive and re-rate. Both cases are credible. Workflow stickiness — the most-cited moat in every Series C deck since 2018 — now has to be re-earned from scratch. Why GRAF Changes the Framework, Not Just the Scoreboard Against all of that, Bartlett offers GRAF — a Growth-Adjusted Rule of 40. The premise is one I flag for any founder still putting a tidy Rule-of-40 score on a slide: growth and margin are not interchangeable. His two-factor regression puts revenue growth at roughly 2.4 times the valuation weight of free-cash-flow margin. A company that hit 40 by grinding to a fat margin on thin growth is not the same asset as one that hit 40 on real growth. On that weighted basis, software trades around 0.23 times today, down from 0.52 times in November 2021. For a broader view on how AI is reshaping value creation across the portfolio, see my earlier piece: AI, Automation, and Robotics Are Reshaping Value Creation for Private Equity. Where the Comps Meet the Deal Terms This is where the software valuation story runs into the deal-points studies I keep on the shelf. The SRS Acquiom M&A Deal Terms Study has shown earnouts climbing as the tool of choice for bridging a valuation gap — and there is no wider gap than the one between a seller who believes its growth is durable and a buyer quietly modeling seat erosion out to 2030. The earnout is the bridge. Its structure — which milestones gate the payout, who controls the roadmap after close —
May 2026 Technology Market Outlook: AI, Valuations & M&A Trends
May 2026 Technology Market Outlook Written for founders, CEOs, CFOs, and directors who actually have to decide something.By Louis Lehot, Silicon Valley M&A lawyer KEY TAKEAWAYS Three public software companies are growing 30%+: Broadcom, Oracle, and Palantir. Their average valuation multiple is 21.0x revenue, while companies growing below 10% are trading at just 3.1x revenue. Nearly 90% of public software companies are now growing below 20%, compared with 56% in November 2021. The high-growth category wasn’t repriced. It simply became much smaller. Application software currently trades at 3.4x revenue compared with a five-year average of 7.0x. Vertical software trades at 4.4x. Companies relying on valuation benchmarks from 2024 may be using outdated assumptions. The IPO window remains effectively closed for businesses that are not clearly tied to AI. M&A activity remains active but highly disciplined, and secondary transactions have become the most popular mechanism for providing partial liquidity. If you run a software company or sit on a board, the question for the next meeting is no longer, “What is the market multiple?” Instead, it is, “Which market are we actually in, and what are we doing about it?” According to Rob Bartlett’s May 2026 update from Jefferies’ technology investment banking team, the divide in the market is difficult to ignore. Only three public software companies are expected to grow more than 30% over the next twelve months, and they command a 21.0x revenue multiple. Companies growing below 10% trade at 3.1x. The middle ground has largely disappeared. This is not one market experiencing compression. It is two entirely different markets sharing the same Bloomberg terminal. The Hard Question Since ChatGPT launched in November 2022, the companies in Bartlett’s AI Darlings group—Broadcom, Google, Meta, Microsoft, NVIDIA, Oracle, Palantir, and Amazon—have risen by 565%. AI Beneficiaries are up 160%. Cybersecurity companies have gained 94%. Vertical software is up 5%, while horizontal application software has declined 16%. The shift is far from theoretical. Harvey has reached an $11 billion valuation while targeting Thomson Reuters and LexisNexis. Sierra and Decagon are AI-native customer service platforms challenging Salesforce and ServiceNow. Glean, valued at $7.2 billion, is attacking markets where Microsoft 365 and ServiceNow once commanded premium pricing. Jake Saper of Emergence recently described the Cursor versus Claude Code dynamic as a warning sign for the broader industry. One product controls the developer workflow while the other merely executes tasks. Developers are increasingly choosing the latter. As agents begin performing the work, the traditional advantage created by having humans deeply embedded within software starts to weaken. Workflow stickiness, which has been a central argument in growth-stage software investing for years, now requires fresh justification. Boards and leadership teams should be asking a difficult question: Is our growth story still credible in an AI-native world, or are we managing a melting ice cube? A company growing 12% annually with a 22% free cash flow margin may appear healthy. However, if investors believe AI-native competitors could erode customer renewals within two years, enterprise value is already being discounted, whether management acknowledges it or not. Founders are often the last people to recognize these shifts. Boards exist precisely for moments like these. What Your Suitors Are Seeing Two important facts frame the current environment. First, diversified technology giants remain financially strong. Microsoft, Alphabet, Oracle, Broadcom, Salesforce, and their peers collectively trade around 5.5x revenue, slightly above their long-term averages and significantly above pre-pandemic levels. These companies possess strong acquisition currency and are deploying it selectively to acquire AI capabilities, distribution channels, or proprietary data that cannot easily be built internally. Second, private equity firms continue to invest aggressively, though with greater discipline. Thoma Bravo recently closed a $24.3 billion software fund. Orlando Bravo has argued that markets are overestimating AI disruption while undervaluing domain-specific software businesses. Holden Spaht has been even more direct, suggesting that public markets are failing to distinguish between software that large language models can replace and software they cannot. Recent take-private transactions involving Dayforce and Verint demonstrate that Thoma Bravo is backing those views with capital. However, cautionary examples have also emerged. On April 22, Thoma Bravo handed Medallia back to its lenders. The $6.4 billion acquisition completed in 2021 resulted in roughly $5.1 billion of equity losses. It marked the second major SaaS equity wipeout in eighteen months following Vista’s transfer of Pluralsight in 2024. The take-private strategy still works for the right companies purchased at the right price. It does not work universally. For sellers, this means that there may be more potential acquirers in 2026 than there were in 2023. However, buyers are more disciplined, better informed, and operating with playbooks redesigned for the AI era. Deals that once closed in ninety days may now require one hundred fifty days. Strategic buyers who previously paid for synergies are demanding evidence of AI defensibility before offering premiums above the value of recurring revenue. This is not a bad market. It is simply a different one. How Secondaries Are Becoming a Strategic Tool The biggest change visible in deal activity during 2026 is not traditional M&A or take-private transactions. It is structured secondary liquidity. Carta reported that tender offer volume increased roughly 60% during 2025, and 2026 is expected to surpass that pace. Large company-led tender programs are becoming increasingly common. OpenAI completed a $6.6 billion tender offer at a $500 billion valuation. Stripe is conducting another transaction at a valuation exceeding $140 billion. Anthropic is reportedly preparing one at $350 billion, while SpaceX continues its semiannual programs. These are not fundraising rounds. They are liquidity events designed to allow employees and early investors to sell shares while companies remain private. Pricing and oversubscription have become critically important. Notion’s $270 million tender earlier this year experienced demand that exceeded available capacity, forcing prorated allocations and prompting a public apology from the CEO. General partner-led secondaries and continuation vehicles have also accelerated. Funds raised between 2017 and 2019 are reaching stages where investors expect distributions, yet many portfolio companies remain strong despite a
When the Ground Moves Beneath Your Feet: Notes from a PE-Backed CFO Forum
When the Ground Moves Beneath Your Feet: Notes from a PE-Backed CFO Forum In 20-plus years of papering venture and private equity deals in Silicon Valley, I’ve sat across the table from a lot of CFOs. The good ones tell you what’s actually moving the business in the first ninety seconds. The great ones tell you, in the same breath, what they’d change about the last cycle. On Thursday evening at the Wells Fargo Innovation Center in Menlo Park, I had the privilege of moderating a closed-door conversation with four of the great ones, and it was the most useful ninety minutes I’ve spent on the state of value creation in 2026. The credit for getting that room in the room belongs to the organizers. Christina Bui at Robert Half built the panel, ran the logistics, and kept the program on rails. Armello Rodriguez and the Wells Fargo Innovation Center team hosted us and made the venue feel like a working room rather than a stage. You can see it in the photo above—panelists on stools, no podium, the program board behind us setting the week’s tempo. And Murray Newlands, who knows everyone worth knowing on the early-stage capital side, helped pull the right people into the seats and then sharpened the questions in the lightning round. The panel itself was deliberately tight. Celine Dinh of Brain-Life and Vietsol. Drew Hamer of Arcserve. Ted Marks of Applitools. Viraj Patel of Signeasy. Four PE-backed CFOs who had pulled real levers in the last twelve months. No theory, no slideware, no AI-washing tolerated. Here’s what came out of it. The macro: AI is on fire, SaaS is in a squeeze I opened the night with a framing the room didn’t push back on. AI is in a boom cycle: budgets are flooding in, board attention is everywhere, infrastructure spend is accelerating. Meanwhile, traditional SaaS is in a tougher cycle than it’s been in a decade. Growth multiples have compressed. Expansion is harder. Churn is scrutinized more than ever. The math of the Rule of 40 has become the Rule of 100. For a PE-backed CFO, that creates a very specific mandate: create value now (in EBITDA and in cash), protect the downside, and keep optionality for an exit that may take longer than anyone wants. Or, as I kept saying throughout the night: value creation must be manufactured, not assumed. The ground is moving on valuation The most uncomfortable truth in the room (and the one every CFO nodded at) is that the ground has shifted beneath our feet on how growth gets valued. Thirty percent growth used to clear the bar without a second look. In 2026, the same number provokes a different question: at what margin, with what retention, and at what efficiency? Murray pushed it in the lightning round: is 30% growth respectable, or irrelevant? The answer was clear—it depends entirely on whether the growth is efficient and durable. A growth number without a margin profile and a retention story is no longer a complete sentence. Buyers and sponsors are no longer paying for top-line optics. They are paying for predictable, repeatable margin expansion at scale. This shift reframes everything a CFO does. Pricing discipline moves higher up the priority list. Churn analysis becomes a weekly focus instead of a quarterly review. And there is a strong emphasis on what Drew described as “margin that sticks”—structural improvements that don’t reverse over time. EBITDA from precision, not pain Every CFO in the room had already cut what was easy to cut. The next phase is about precision: pricing, product mix, and operating discipline. Viraj outlined how stronger pricing discipline works in practice: fewer discounts, clearer packaging, and strict enforcement through approval workflows. The result is sustainable margin expansion without significantly slowing growth. Ted also raised an important warning. Chasing “EBITDA at all costs” can backfire. Cutting too aggressively can hurt retention and product quality, leading to churn that shows up quarters later. Sponsors value repeatable margin, not short-term cost cutting. The cultural shift: finance has moved into the operating core The role of finance has fundamentally changed. Celine described a shift from monthly reviews to weekly operating reviews driven by key performance indicators. Finance now owns the scoreboard and decision gates, including pricing approvals, hiring, and spend governance. Drew clarified the boundary: finance should lead when decisions are economic and support when they are technical or customer-facing. This distinction helps CFOs act as strategic operators rather than just financial overseers. AI, AI-washing, and the pricing question AI is delivering real value, but primarily in focused workflows. Automating high-volume processes with clear ROI works. Superficial AI features that do not impact workflow or outcomes do not. Ted provided a disciplined framework: every AI initiative needs a clear owner, defined KPI, measurable payback period, and strict governance. If it does not impact margin, retention, or cash flow, it is deprioritized. Pricing AI services is emerging as a major challenge. Traditional SaaS pricing models do not fit well with AI products, where costs scale with usage and value scales with outcomes. CFOs are experimenting with hybrid pricing models, including usage-based tiers and outcome-linked pricing. This also highlights the issue of AI-washing. Superficial AI claims do not hold up under scrutiny. What matters are measurable outcomes—adoption rates, efficiency gains, and retention improvements. Exit readiness when the exit is further away In a slower exit environment, companies must remain decision-ready. Ted highlighted key fundamentals: reliable forecasts, clean KPIs, fast close processes, and strong cash management. Drew added that investor-grade operations require consistency, transparency, and controls that prevent surprises. Celine emphasized a simple principle: invest in what compounds and eliminate what does not. The takeaway Three themes stood out. First, EBITDA growth now comes from precision, not cost-cutting. Second, CFOs have become central to operational decision-making. Third, optionality must be built early, not at the moment of exit. The key message: be decision-ready before the market demands it. The networking after the session was equally
AI, Automation, and Robotics Are Reshaping Value Creation for Private Equity
Louis Lehot: AI, Automation, and Robotics Are Reshaping Value Creation for Private Equity Louis Lehot, a Silicon Valley private equity lawyer with close to three decades of experience advising sponsors, shares his perspective on how AI and robotics are changing deal-making. 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 into a lost opportunity. Companies didn’t disappear. They stacked up. Figma cleared the window. CoreWeave cleared the window. Cerebras 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 needed 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, and code generation inside engineering are now part of everyday operations. 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. Advances in computer vision, autonomous systems, and sensor technology have opened up applications that were not economically viable five years ago. Logistics yards, food service, construction, field services, healthcare delivery, defense tech, and space are all emerging areas. Government contracts in particular provide five to ten years of revenue visibility, offering stability that is hard to find in AI infrastructure markets. For sponsors looking at industries with persistent labor shortages, wage inflation, or seasonal volatility, robotics is a credible operational lever. It is the kind of investment that can permanently change a business’s unit economics—exactly what is needed when hold periods extend beyond initial expectations. A diligence war story The diligence I am running on technology-enabled deals today looks very different from 2022. We now spend real time on data rights. Whose data is the model trained on? Do you have the license? Was it scraped? Are there indemnities? Are the outputs clean for commercial use? In one recent deal, a target company had trained its model on restricted third-party data. The deal survived, but it was renegotiated and the seller took a financial hit. We also spend time on regulatory exposure. The EU AI Act and state-level laws in California, Colorado, and New York are reshaping compliance requirements. If AI is used in hiring, pricing, credit, or insurance decisions, regulatory diligence becomes unavoidable — an area I work through in detail with clients on my AI legal counsel practice. Insurance carriers are also beginning to introduce AI-specific exclusions, reflecting real-time risk pricing. Cybersecurity is another major focus. AI expands the attack surface through prompt injection, model extraction, and training data poisoning. These are not theoretical risks—they are active concerns. Finally, workforce impact matters. Automation has
Securing the Agentic Future: A GC Lunch Series Discussion
Securing the Agentic Future: A GC Lunch Series Discussion AI adoption across industries is entering a new chapter. As generative AI becomes embedded in business operations, a more powerful and more complex wave is emerging: agentic AI. These systems do not merely assist with discrete tasks, but they can autonomously execute multi-step processes, make decisions, and interact with enterprise tools across functions and sectors. For organizations navigating the shift, this raises a fundamentally different set of questions around governance, security, accountability, and operational resilience. The latest installment of our GC lunch series brought together legal and cybersecurity leaders from Sophos, Securonix, and RingCentral to examine how companies are preparing for this agentic era and the cross-sector challenges it presents. Here are the key takeaways. What Are Agents, and Why Should GCs Care? Agentic AI is qualitatively different from a chatbot or a document review tool. Agents can take action by reasoning, planning, executing multi-step processes, accessing enterprise systems, and making decisions with varying degrees of autonomy. That distinction should get every GC’s attention fast. A conventional enterprise AI system might draft a contract clause when prompted. An agentic system might identify the need for a clause, draft it, route it for approval, and update a contract management system without a human initiating each step. The governance implications of that leap are profound. From a GC’s perspective, the defining characteristic of agents is autonomy. Autonomy, in a legal context, is inseparable from questions of authority, liability, and control. The law of agency may get tested in new and unexpected ways. Where Agents Are in the Ecosystem The push to build, develop, and deploy agents is accelerating across multiple verticals, business units, and sectors. Organizations represented at the lunch series—spanning cybersecurity platforms and cloud communications—are actively building and deploying agents within their product suites and internal operations. The maturity curve for agents is still early, but the trajectory is steep. The biggest technical developments driving the move toward agentic systems are happening now, and panelists expect agents to show up most prominently across the enterprise in the next twelve to twenty-four months. The ecosystem is not settling on a single model. The conversation is moving between single-purpose agents, multi-agent systems, and orchestration layers. GCs need to understand these architectural choices because each carries different risk profiles. Cybersecurity companies like Sophos and Securonix are playing a dual role: both shaping the agentic ecosystem as builders and serving as frontline defenders against the risks agents introduce. The message from the panel was clear—separate the hype from what is durable, and make sure your legal team is tracking the developments that most companies probably aren’t. Agents Are Creating a New Governance Conversation The global legal landscape around AI is fluid, and agents are creating governance challenges that are materially different than those posed by standalone generative AI systems. GCs cannot simply extend existing AI policies to cover agentic deployments because the risk profile is different in kind, not just degree. Autonomy and Accountability. Organizations must define the scope of authority granted to an agent with the same rigor they apply to human delegation. “Least privilege” takes on new meaning in the context of agents, and decision rights need to be documented when agents are used in critical workflows. The harder question of how much discretion an agent should have before mandatory human escalation is required has no one-size-fits-all answer, but it demands a deliberate framework. Security. Agents introduce security risks that are unique or heightened compared to non-agentic systems. Prompt injection, tool abuse, privilege escalation, and memory poisoning all take on new dimensions in an agentic environment. Agents are, in effect, creating a new attack surface inside the enterprise, and CISOs are thinking carefully about how to monitor and contain these risks. General counsel should be part of that conversation, not waiting for an incident to force it. Data Privacy and Additional Risks. Agents complicate existing data governance and privacy frameworks in ways that many organizations have not yet anticipated. For companies navigating the complicated global patchwork of data privacy laws, deploying agents without a clear understanding of how they interact with personal data, cross-border transfers, and regulatory obligations can present significant challenges. Third-Party Risk. Contracting with agentic vendors requires a different lens. Indemnity, limitation of liability, and security provisions all need to evolve for agentic AI. What GCs Should Take Back to Their Teams The panelists were candid about what excites them about the agentic future but equally candid about the governance it demands. The right balance between innovation and governance must remain dynamic, requiring ongoing engagement from legal leadership. GCs need to be positioned to answer a key question—if you had to explain an agent’s conduct to a regulator, a court, or your board, what documentation would you need, and how much of it does your organization actually have today? The one thing every legal leader in the room was urged to take back to their teams and their boards was that securing the agentic future is not a technology challenge alone. It is a governance, risk management, and leadership challenge as well. Agentic AI is moving from concept to deployment faster than most legal departments are prepared for. The GCs who pair early engagement with deliberate governance frameworks—and who define authority, demand auditability, and insist on security by design—will be the ones who lead their organizations through this transition rather than react to it. Click here for a timely Wall Street Journal read echoing many of the points we addressed. Onwards!
Private Equity’s AI Bet: Strategic Hedge or Structural Conflict?
Private Equity’s AI Bet: Strategic Hedge or Structural Conflict? Three Key Takeaways AI joint ventures are creating internal portfolio conflicts for private equity firms by accelerating disruption of legacy SaaS investments they still own. Delaware’s SB 21 gives sponsors more procedural certainty for affiliated transactions, but only if firms implement strong governance safeguards, independent approvals, and documented conflict-management processes. Antitrust scrutiny of AI investments is intensifying, with regulators increasingly focused on overlapping board seats, cross-holdings, information-sharing rights, and HSR disclosures tied to AI-related deals. This week’s headlines are remarkable. OpenAI closed a roughly $10 billion joint venture, called DeployCo, with TPG, Brookfield, Bain, and others to push its tools across sponsor portfolios. Within minutes of that news, Anthropic announced a $1.5 billion venture with Blackstone, Hellman & Friedman, and Goldman Sachs to embed Claude into mid-market businesses, starting with their own portfolio companies. These are not passive bets. They are JVs with real governance rights that give the largest pools of buyout capital first-look operational access to frontier AI, and give the labs a distribution channel into thousands of businesses in return. The commercial logic makes sense. The legal complications are bigger than most sponsors are admitting. The Strategy Eats Its Own Portfolio Many of the same sponsors funding DeployCo and the Anthropic venture still own legacy enterprise software businesses. Those companies were underwritten on assumptions about sticky seats, durable pricing, and net retention above 110% that AI-native services are now eroding. The tools being deployed through the new JVs will compress pricing power and shrink the addressable market of the same sponsors’ SaaS holdings. The conflict lives inside one fund family, and sometimes inside one fund. Fiduciary Conflicts After Delaware SB 21 When a GP-affiliated AI vehicle gains value at the expense of a SaaS portfolio company held in another fund, directors of the affected entity still owe Delaware fiduciary duties to its stockholders, including LP co-investors. The legal landscape changed in March 2025, when Governor Meyer signed Senate Bill 21, amending DGCL § 144. For controlling-stockholder transactions other than going-private deals, SB 21 now provides a statutory safe harbor against both equitable relief and damages if the transaction is approved by an independent committee of at least two disinterested directors, or by an uncoerced majority-of-the-minority vote of disinterested stockholders. Section 8 of the Clayton Act Already Sees It The Department of Justice Antitrust Division and the FTC have unwound or prevented more than two dozen interlocks since 2022. Reporting in April 2024 indicated DOJ is specifically examining shared board service among rival AI companies. HSR Filings Surface the Cross-Holdings The revised HSR rules took effect February 10, 2025, and they were drafted with private equity in mind. Limited Partners with management rights now have to be named, and minority holdings in overlapping NAICS codes get specifically flagged. Getting the JV Documents Right Most of the legal architecture for these AI joint ventures lives inside the JV agreements themselves. Governance, information rights, valuation methodologies, liquidity rights, and transfer restrictions must all be carefully drafted to avoid future disputes and unintended control triggers. M&A Documents Are Catching Up AI-specific representations and warranties are rapidly becoming standard in acquisition agreements, including provisions addressing training-data provenance, open-source compliance, model dependencies, and AI risk management frameworks. The Bottom Line Private equity firms are racing to secure an operational edge through AI partnerships, but regulators and courts are moving just as quickly to examine the governance, antitrust, and fiduciary implications. Firms that proactively address conflicts, cross-holdings, and AI-specific legal risks will be better positioned to capitalize on the opportunity while avoiding enforcement exposure.