A few posts ago, I made the case that Vibe Coding eliminates the queue: the wait for an engineer to build the thing you need. Last post, Solo Scale showed what happens when you remove the ceiling on how much one expert can supervise. Neither one, by itself, changes how your business actually works. Loop Engineering is what does. It’s the bridge between “I can build this” and “this runs the business.” And it’s the part most business leaders haven’t discovered yet, even as it’s already quietly reshaping how businesses operationalize Agentic AI.

That quieter shift is starting, and the people closest to the technology have already made it. In June, Boris Cherny, who leads Claude Code at Anthropic, said he no longer prompts the model at all. He writes loops that do the prompting for him, and his job now is to write the loops. Engineers at Google and OpenAI have been saying versions of the same thing. The shorthand term is “loop engineering.”

Here is why it matters to you, even if you never write a line of code: the move from prompting to loops is not a coding story. It is an operating story. And it is the piece that turns everything agentic AI has promised into something a business can actually run on.

From Running the Tool to Designing a System

Start with the plainest version.

Prompting asks a model for an answer. You type a request, read what comes back, refine it, ask again. You are running the tool the entire time, one turn after another, and the quality of the work depends on your skill at the exchange and your patience for it. You are the bottleneck.

A loop is different. A loop is a short set of instructions that says what you are trying to accomplish, what the system may use to get there, what counts as evidence it succeeded, what to do when it fails, what to remember between runs, and when to stop. Then it runs: checking its own output against your standard and continuing until the result is good enough to use.

Prompting gets you one good answer. A loop builds the thing that produces good answers without you in the room.

What a loop really is

Strip the code away and a loop is something every good operator already carries in their head: a standard for what good looks like, and the judgment to know when work meets it. What you have never had is a way to enforce that standard at scale, on many fronts at once, without being present for every step.

That is what a loop gives you. You define the goal, the tools, the evidence, the failure response, the memory, and the stopping point once. The system carries your standard forward every time it runs.

The scarce thing just moved

For two years the value lived in the interaction: knowing how to prompt well, working the tool skillfully turn by turn. That skill is being commoditized. The new scarcity sits upstream of it: knowing what good looks like, and being able to specify it precisely enough that a system can hold to it without you.

The premium is moving from doing the work to knowing what good looks like, and being able to say so precisely.

That reallocation changes what each of you should do next. Here is where to start, depending on where you sit.

If you run the work: deploy

Pick one process you run over and over that leans on your judgment: a weekly competitive brief, lead qualification, a first-pass content review, campaign QA. Something with a clear standard for good that you currently apply by hand. Write that standard down: the goal, what the system may draw on, what a good result looks like, what to do when it isn’t. Then let a loop run it while you check the output instead of producing it. Start with one. Get it right before you add a second. The skill you are building is not technical. It is learning to state a standard clearly enough that a system can hold it.

If you run the company: reallocate

If value is moving from execution to judgment, your org chart is about to feel it. Ask yourself three questions. What are you staffing, promoting, and paying for today that is execution capacity about to get cheap? Who on your team can author standards, not just follow them, because that is the scarce talent now. And the pointed one: which part of the business would you most want running on your standard instead of your headcount, and what is keeping you from starting there?

The executives I watch most closely are not asking whether this is real. They are asking where to apply it first, and how fast they can move.

If you fund the work: evaluate

When execution is cheap and judgment is scarce, the shape of a good business changes. The moat is no longer how much a company can build, or how fast. It is the quality of the standard encoded in its systems: proprietary judgment about what good looks like in a specific domain, enforced by loops a competitor cannot easily copy. In diligence, the question moves from how big is the team to whose judgment is in the loop, and will it hold. And learn to tell a real loop from loops-as-lipstick: genuine encoded expertise versus a thin prompt in a nice interface.

If you’re starting something: build

This is the part that should keep you up at night, in the good way. If one expert, armed with the right standards and a set of loops, can produce what used to take a department, then a single person with deep domain knowledge can start and run a business that used to require a team and a raise. Not by writing code. By encoding what they know about their field into systems that run. The barrier to starting has never been lower for the person whose real asset is expertise. So the question is simple: what would you build if execution were no longer the thing in your way?

One hard truth first

A loop running unattended is also a loop making mistakes unattended. The skill of loop engineering is not the loop. That part is simple. It is knowing where the system will be confidently wrong, feeding it inputs you can trust, and keeping a human on the standard. The people learning this the hard way keep arriving at the same lesson: the model rarely fails because it is dumb. It fails because it was handed bad inputs or a fuzzy standard, and it faithfully delivered exactly what it was told to.

Verification stays human. That is not a limitation to engineer away. It is the job.

Three posts, one argument

Now step back.

Vibe Coding removed the constraint on who can build. Anyone who can describe software can produce it. The queue disappeared.

Solo Scale named what becomes possible at the far end: an expert-led, agent-powered business running at software margins, one person, or a very small team, producing what once took many.

Loop engineering is the bridge between them. It is the mechanism that turns “anyone can build” into “one expert can run a whole business.” Without loops, Vibe Coding gives you faster one-off builds, impressive, but still one turn at a time, still bottlenecked by your bandwidth. With loops, those builds become systems that run: your judgment, encoded, working on many fronts at once. That is the machinery beneath Solo Scale. It is what makes the margins real.

And the same mechanism, pointed at an organization instead of a solo operator, opens a much larger door. When you stop automating single tasks and start encoding judgment into loops that run across a whole process, you are no longer improving a workflow. You are transforming it, rethinking how work moves through the business from end to end. That is the next frontier, and loop engineering is the way in.

So: three posts, one argument. Vibe Coding opened the door. Solo Scale showed the room on the other side. Loop engineering is how you walk through, and it is the same threshold every business will cross on the way to changing how work actually gets done.

The queue is gone. The ceiling is going. What is left is the standard, and whether you can name it clearly enough for a system to carry it.

That is the work now.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

“Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC: a strategic advisory practice for founders, CEOs, and investors in B2B information, marketing, and technology. The businesses that drive business.

There’s a conversation happening about what AI means for software creation. Elena Verna at Lovable calls it Mom-and-Pop SaaS. The thesis is smart: as the cost of building software collapses, domain experts — not just developers — become builders. She’s right. And it’s only half the story.

The bigger shift isn’t happening in software products. It’s happening in services.

Agentic AI and vibe coding aren’t just lowering the cost of building apps. They’re dismantling the core constraint of every services business ever built: the ratio of expert time to revenue. That constraint: one expert, one engagement, finite hours, is what kept professional services firms trapped at 10–20% EBITDA margins while SaaS companies ran 60–80%.

That constraint is ending.

The Old Model Was Always a Labor Problem

Services businesses sell expertise. But expertise doesn’t scale. You hire more experts, manage more overhead, and your margins compress. The best consulting firms in the world, running mature operations, optimized for utilization, still average under 10% EBITDA. The 2025 SPI Professional Services Maturity Benchmark, covering 509 firms managing $63 billion in revenue, put the industry average at 9.9%. Historic low.

The fundamental problem is structural. Services revenue scales linearly with headcount. Software revenue doesn’t. That gap is what the entire VC-backed SaaS industry was built to exploit.

Now the gap is available to everyone.

What Agentic AI Actually Changes

Gartner projects that 40% of enterprise applications will embed task-specific AI agents by end of 2026, up from under 5% in 2025. That’s an enterprise story. The more important story is what’s happening at the edges.

Agentic AI doesn’t just make individuals more productive. It allows one person to deploy systems that do execution work that previously required a team. Research. Analysis. Outreach. Reporting. Monitoring. Drafting. These aren’t tasks an AI does instead of you. They’re tasks an AI does alongside you, continuously, at scale, without adding headcount.

The economics flip. A solo practitioner running AI agents across ten client engagements simultaneously isn’t running a consulting business anymore. They’re running something new.

Vibe coding accelerates this further. The market for AI coding tools hit $4.7 billion in 2026 and is projected to reach $12.3 billion by 2027. Sixty-three percent of vibe coding users identify as non-developers. The cost of building a functional product has dropped from roughly $200,000 to about $5,000. Build timelines compressed from six months to six weeks.

The combination matters. Agentic AI handles execution at scale. Vibe coding eliminates the technical barrier to building the systems and tools that deliver it. A domain expert with deep practitioner knowledge and a willingness to learn these tools now has access to a business architecture that didn’t exist two years ago.

Introducing Solo Scale

I want to name what’s emerging, because it’s distinct from anything we’ve described before.

Solo Scale is a new category of AI-enabled business: expert-led, agent-powered, running at software margins.

The entity at the center of a Solo Scale business is what I call an Expert Scale operator. A practitioner who combines deep domain knowledge with agentic AI systems to deliver services at a leverage ratio that traditional firms can’t match.

This isn’t freelancing. Freelancing trades time for money at a fixed rate. Solo Scale trades expertise for outcomes, with AI handling the execution volume. The margin structure is fundamentally different.

This isn’t SaaS. There’s no product to sell at scale without the practitioner. The expertise is the product. But the delivery infrastructure is AI.

Three things define a Solo Scale business:

1. Domain depth. The operator has years, often decades, of practitioner knowledge in a specific field. Not generalist knowledge. Vertical expertise that took time to accumulate and can’t be easily replicated by a model alone.

2. Agentic infrastructure. AI agents handle the execution layer: research, analysis, drafting, monitoring, outreach, reporting. The operator sets strategy, reviews output, exercises judgment. The agents do the volume.

3. Software-like margins. Because the execution scales without proportional headcount, the margin structure approaches software economics. Not 10–20%. Closer to 40–70%. Territory that professional services firms have never accessed before.

Elena Verna’s data from Lovable points in this direction: 80% of builders intend to monetize. 35% are already generating revenue. But the real money won’t be in software products built by domain experts. It will be in services businesses run by domain experts, using software as the delivery infrastructure, not as the product.

The Five Categories That Emerge First

Not every services domain is equally positioned for Solo Scale. The highest-value early categories share three characteristics: high complexity (which protects against commoditization), high existing fee levels (which means the margin expansion is large in absolute dollars), and repeatable workflow structures (which AI agents can execute reliably).

Here are the five categories where I expect Solo Scale businesses to emerge first: including two that may surprise you.

1. Specialized B2B Advisory

Management consulting, strategic advisory, and fractional executive services are the clearest near-term Solo Scale opportunity. The practitioner provides judgment, relationships, and pattern recognition accumulated over a career. AI agents handle the research, analysis, competitive monitoring, and report generation that junior associates and analysts currently do.

A senior advisor running five engagements simultaneously, with AI handling the execution layer across all five, is running a business with fundamentally different economics than a traditional consulting firm. No associate overhead. No utilization management. No bench time. The margin structure changes completely.

This is not hypothetical. It’s happening now, across advisory practices of every kind.

2. Vertical Content and Intelligence

B2B information and intelligence businesses have always been constrained by the cost of producing high-quality, specialized content at volume. The editorial team is the cost structure. AI changes that equation.

Consider a practitioner with genuine vertical expertise: supply chain, procurement, healthcare operations, industrial technology. That practitioner can now produce research reports, market analysis, buyer intelligence, and curated content at a volume and quality that previously required a full editorial operation. The practitioner provides the framing, the sourcing judgment, and the editorial voice. AI handles the production volume.

The Vertical Intelligence Company I’ve written about in this series fits squarely here. Solo Scale is the business model underneath it.

3. AI-Augmented Professional Services

Law, accounting, financial advisory, and compliance-adjacent services are early-stage Solo Scale territory. Regulatory complexity and liability requirements mean pure automation can’t displace the practitioner. But AI dramatically compresses the time required for the research, drafting, and analysis that underlies professional work.

A specialist attorney who handles a narrow, high-value area of commercial law, and deploys AI agents to handle the research, precedent review, and initial drafting, operates at a leverage ratio that a traditional associate-dependent firm cannot match. Same expertise. Dramatically lower cost structure.

4. Supply Chain and Logistics Intelligence (The Surprising One)

Here’s a category most people aren’t talking about yet. Supply chain consulting has historically required large teams: data analysts, logistics modelers, procurement specialists, demand forecasters. The work is extraordinarily complex. That complexity is also the moat.

A veteran supply chain practitioner: someone who spent twenty years running logistics operations, sourcing organizations, or distribution networks, can now deploy AI agents that monitor supplier risk, model tariff scenarios, track demand signals across markets, and generate procurement recommendations. BCG estimates agentic systems already account for 17% of total AI value in supply chain and are projected to reach 29% by 2028. Supply chain leaders report that 78% anticipate disruptions to intensify over the next two years, but only 25% feel prepared.

That gap is a market. And the practitioner who combines deep operational experience with agentic infrastructure to serve mid-market manufacturers, distributors, and retailers, companies too small to staff an internal supply chain intelligence function, is sitting on a genuine Solo Scale opportunity. The client can be enterprise-scale. The operator doesn’t have to be.

5. Trades and Skilled Work Operations (The Very Surprising One)

This one will raise eyebrows. Solo Scale isn’t just a knowledge economy phenomenon. It extends into the physical economy. Particularly into the operations layer that sits above skilled trades.

Consider the experienced HVAC contractor, the seasoned electrical contractor, the veteran construction project manager. The actual hands-on work requires licensed, skilled tradespeople on-site. But the operations layer: estimating, scheduling, compliance documentation, supplier negotiations, customer communications, change order management, warranty tracking, is all information work. And information work is exactly what agentic AI transforms.

A solo operator with deep trades experience can now run the back office, customer acquisition, and operations management for a services business that employs 10–20 tradespeople in the field, without a team of administrators, estimators, and project coordinators. The leverage point isn’t the work itself. It’s the operations intelligence layer that organizes and monetizes the work.

The trades are facing a documented expertise gap as veteran operators retire. The Solo Scale model lets that expertise be preserved, leveraged, and deployed at scale. This is the physical economy version of the Vertical Intelligence Company.

The Prediction: What Solo Scale Does to the Economy

The internet was supposed to create the creator economy. It did, but only partially. The promise was that anyone with expertise and a laptop could build a scalable business. The reality was messier.

The numbers tell the story. The creator economy was valued at roughly $250 billion in 2024. More than 300 million people worldwide identify as creators. But only 4% earn more than $100,000 annually. More than 50% earn less than $15,000 per year. The top 10% of creators received 62% of ad payments in 2025. The long tail of the creator economy doesn’t produce economic independence. It produces economic fragility.

The bottleneck was always the same: the internet gave everyone distribution, but it didn’t give everyone leverage. Attention was the scarce resource. And attention, unlike expertise, doesn’t compound. It chases novelty. The creator who went viral in 2022 has to go viral again in 2023. The game never ends.

Solo Scale changes the bottleneck. Expertise compounds. Client relationships deepen. Reputation concentrates in narrow domains. A Solo Scale practitioner isn’t competing for attention. They’re deploying knowledge that took decades to accumulate, through infrastructure that didn’t exist until now.

Here’s the prediction: we are about to see the largest wave of high-value small business formation in American history.

The data is already signaling it. Business applications in the US hit 5.62 million in 2025: up 8.2% from 2024, and nearly double the pre-pandemic annual average. In the first four months of 2026, applications are running 17.4% ahead of the same period last year. Solo-founded startups surged from 23.7% of all new ventures in 2019 to 36.3% by mid-2025: a shift that tracks almost precisely with mainstream AI tool adoption. And 47% of respondents in a 2026 Entrepreneur survey said AI availability makes them more likely to start a business.

But this wave will be different from prior entrepreneurial surges in one critical way. It won’t be driven by people selling to consumers. It will be driven by domain experts, veterans of industries, functions, and markets, selling to enterprises. Small businesses at software margins, serving Fortune 500 clients. That combination has never existed at scale before.

The economic implications compound. When a solo practitioner can serve enterprise clients at software margins, several things happen simultaneously. More experienced operators leave large organizations to compete against them. The addressable market for small businesses expands upmarket. Enterprise buyers gain access to specialized expertise they couldn’t previously afford. And the income distribution of entrepreneurship shifts: away from the winner-take-all dynamics of the creator economy, toward the more defensible economics of expertise.

78% of solo businesses currently make under $50,000 annually. The Solo Scale model, executed well, moves that number dramatically. Twenty percent of solopreneurs already earn between $100,000 and $300,000 annually without any employees. AI is early. The ceiling is rising fast.

The long-promised democratization of the economy is happening. It’s just not coming through social media. It’s coming through agentic infrastructure.

A Personal Note

Lest you think I’m only researching and analyzing this trend, I’m living it.

Uphoff Advisory, LLC is a Solo Scale business. In 90 days, as a solo operator, I’ve built a thriving multi-client advisory practice serving organizations that range from fast-growing entrepreneurial ventures to established B2B information brands. The client engagements are substantive. The margin structure looks nothing like a traditional consulting practice. And the AI infrastructure running underneath: research, analysis, content production, web site design and build, business development, is doing work that would have required a team of three to five people a few years ago.

I’m not describing a theory. I’m describing what happened when I built exactly this. Solo Scale indeed.

Why This Is Different From What Came Before

The creator economy taught us that one person could become a media company. Substack. YouTube. Podcasting. The economic unit of production changed. But the economics of creator businesses were still constrained: audience attention was the bottleneck, and monetization was tied to distribution scale.

Solo Scale is different. The bottleneck isn’t audience. It’s expertise. And expertise, unlike audience, is defensible. It compounds. It gets more valuable with specificity, not less.

What Shopify did for merchants, eliminating the infrastructure barrier to selling, agentic AI and vibe coding are doing for expert practitioners. The infrastructure barrier to running a high-leverage services business is collapsing.

The firms that built defensible practices on the old model, that hired in order to scale, that conflated headcount with capability, are not positioned for this shift. The practitioners who built deep domain knowledge and are willing to redesign the delivery model are.

What to Watch

The signal isn’t in the tools. Tools are proliferating everywhere, and most of the noise about AI is tool noise.

The signal is in the business models that start showing up with unusual margin structures. Solo practitioners handling client loads that would have required teams. Boutique advisory firms with economics that don’t match their headcount. Intelligence products delivered by two-person operations that used to require twenty. A trades contractor running a 15-person field operation from a home office.

That’s Solo Scale. And it’s only beginning.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

“Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC: a strategic advisory practice for founders, CEOs, and investors in B2B information, marketing, and technology. The businesses that drive business.

A couple of weeks ago I gave a talk to the Southern California chapter of the Counselors of Real Estate. Seasoned operators. People who have financed, built, and managed commercial real estate through multiple cycles. Smart, experienced, skeptical.

When I shared a slide showing that five technology companies plan to spend nearly $700 billion on AI infrastructure in 2026 alone, the room went quiet.

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Not skeptical quiet. Stunned quiet.

During the Q&A session, someone said what everyone was thinking: “Those numbers can’t be real.”

They are real. And understanding why they’re real, and what they mean, matters for every business leader working in B2B markets right now.

We Have Seen This Before. Just Not at This Scale.

The human mind has trouble processing numbers beyond a certain size. $700 billion is difficult to put into context. It doesn’t connect to lived experience the way a house price or a company budget does.

So let’s anchor it.

The U.S. Interstate Highway System remains the benchmark for transformative American infrastructure. Eisenhower signed it into law in 1956. It took more than 35 years to complete. Its final cost, in 1991 dollars, was $128.9 billion. That investment is credited with generating $1.80 in economic output for every dollar spent. The foundation of American commerce for half a century.

The AI infrastructure build will exceed that total in a single year.

The Hoover Dam, one of the engineering wonders of the 20th century, cost $49 million in 1931 dollars, roughly $811 million today. It powered the American Southwest and enabled the growth of cities like Las Vegas and Phoenix. Its construction required an act of Congress, years of planning, and thousands of workers.

Amazon alone is projecting $200 billion in capital expenditure this year. That’s roughly 250 Hoover Dams. In one year. By one company.

The Manhattan Project, which produced the atomic bomb and reshaped the post-war world order, cost approximately $24 billion in today’s dollars.

The five largest AI infrastructure investors: Microsoft, Alphabet, Amazon, Meta, and Oracle, will spend that amount every few weeks.

This Is Not a Tech Story. It’s an Infrastructure Story.

The numbers become comprehensible when you stop thinking about them as technology spending and start thinking about them as infrastructure investment.

Every transformative infrastructure build in history produced numbers that seemed disconnected from reality at the time. Because they were. They were civilization-scale investments, not household-scale ones. The people financing the transcontinental railroad or rural electrification weren’t spending money in the same category as a business investment. They were building the foundation an entire future economy would run on top of.

That is exactly what is happening now.

Goldman Sachs projects total hyperscaler capital expenditure from 2025 through 2027 will reach $1.15 trillion. More than double the $477 billion spent from 2022 through 2024. Global AI spending is projected to reach nearly $1.5 trillion this year and exceed $2 trillion in 2026.

This is not a bubble narrative or a FOMO story. It is a recognition that compute, in the machine age, is what roads and rails and electrical grids were in prior eras. It is the foundation everything else gets built on. And building that foundation at civilizational scale costs civilizational money.

The Nearest Comp: The Fiber Buildout of the Late 1990s

There is one prior episode that maps to this moment more closely than any government project. Not the dot-com stock bubble. The actual physical buildout underneath it.

Between 1996 and 2001, telecom companies spent more than $500 billion laying fiber optic cable across the United States, with global totals estimated as high as $1 trillion to $2 trillion once equipment, acquisitions, and debt-financed expansion are included. Telecom companies issued more than $500 billion in new bonds in that window alone, betting that internet traffic would grow fast enough to fill the capacity they were building.

It didn’t. Not on that timeline. By 2001, an estimated 95 percent of the fiber laid during the boom was dark, built, buried, and unused. The companies that built it mostly went bankrupt. Capital markets had financed a buildout years ahead of the demand curve.

Here is the part that matters most for this conversation: the infrastructure was not the mistake. The financing structure and the timeline were the mistake. And the dark fiber didn’t just eventually get used, it changed what was economically possible. Bandwidth costs collapsed so far, so fast, that companies could build business models that made no sense at 1999 prices. Netflix could not have pivoted from DVDs-by-mail to streaming in 2007 if it had been paying 1999 bandwidth rates. Amazon Web Services launched in 2006 by renting out infrastructure capacity that existed only because so much had been overbuilt. YouTube’s 2005 bet that ordinary people would upload and stream video at scale only worked because bandwidth was nearly free. None of this was the plan in 1999. It was the byproduct of a trillion-dollar overbuild that bankrupted the companies that financed it and then handed the next generation of entrepreneurs capacity at a fraction of its true cost.

It would be an overstatement to say none of this happens without the fiber bust. Someone, eventually, would have built the bandwidth the modern internet needed. What the overbuild actually bought was time and price. Cloud computing, SaaS, and streaming video arrived years earlier, and at a dramatically lower cost basis, because a previous generation of investors had already paid for capacity the market wasn't ready to use.

The infrastructure outlived the investors who built it. The value accrued downstream, to companies and business models that didn’t exist yet when the fiber went into the ground.

That is the precedent that matters here. Not as proof the AI buildout is a bubble. Not as proof it isn’t. As a reminder that physical buildout and financial outcome are two different questions, and history answers them on different timelines. The fiber was real. The capacity was real. The mistake was assuming the company that laid the cable would be the company that profited from it.

The Valuation Numbers Follow the Same Logic

The IPO and valuation numbers circulating right now are equally disorienting.

OpenAI closed a funding round in March 2026 at a post-money valuation of $852 billion, with a public listing targeting above $1 trillion. SpaceX has confidentially filed for an IPO at a reported target valuation of $1.75 trillion to above $2 trillion.

For context: the largest IPO in history remains Saudi Aramco’s 2019 listing at a valuation of roughly $1.7 trillion. Aramco sits on the largest proven oil reserves on the planet. It was built over decades with the backing of a sovereign government.

AI companies are approaching those numbers in years, not decades, with private capital.

That either means the market is pricing the AI infrastructure build as one of comparable civilizational importance to the petroleum economy. Or it means the market is wrong

Both are possible. History will settle it. But the framing matters: these are not tech stock valuations. They are bets on which companies will own the infrastructure layer of the next economic era.

What B2B Markets Need to Understand

I’ve made two arguments here on Uphoff on Media that this build puts to the test. The first: The Vertical Intelligence Company, not the traditional media company, is the sustainable model, one that lets business information companies scale at software margins. The second: brand is the one thing AI cannot manufacture, the case I made in Your B2B Marketing Career in the Age of Agentic AI.

The infrastructure build makes both arguments more urgent.

For B2B Marketing, this build is what makes agentic AI execution at scale possible in the first place. Content generation, campaign orchestration, and personalization down to the individual account are becoming nearly free. That should terrify anyone whose value proposition was “we can produce more content than you can.” It should not terrify anyone whose value proposition was trust, relationships, and brand. Execution is being industrialized. Judgment and brand are what’s left standing.

Here is the pattern history keeps repeating: the companies that built the great infrastructure of each era rarely captured the most value from it. The railroad barons were real. So was the destruction of railroad economics once the infrastructure became commoditized. The fiber companies of the late 1990s built the network and mostly didn’t survive to use it. The electricity utilities built the grid. The consumer products companies, the manufacturers, the retailers — they built the businesses that ran on the grid.

AI infrastructure is being built at a scale and speed that will make it a commodity faster than most people expect. The economics will shift to the companies that know how to use it — not the ones that own the pipes.

For investors: it means the infrastructure bet is largely made. The application layer is where the next generation of value will be created.

The Numbers Are Real. The Question Is What You Do With Them.

The CRE professionals in that room were right to be stunned. These numbers are stunning. What they shouldn’t be is paralyzing.

Eisenhower’s highway system seemed incomprehensibly expensive in 1956. It became incomprehensibly valuable. The question for every business leader in that moment was not “are these numbers real?” It was “what do I build on these roads?”

That is the right question now.

The infrastructure is going in. The roads are being paved. The machine age is not arriving. It has arrived.

The only remaining question is what you build on it.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

“Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC: a strategic advisory practice for founders, CEOs, and investors in B2B media, marketing, and technology. The businesses that drive business.

Thanks for reading Uphoff on Media! Subscribe for free to receive new posts and support my work.

Something has gone wrong with digital networking.

It was already broken in 2020. I wrote about it then and the response was significant. Thousands of people read it, shared it, and the engagement was immediate. Hundreds responded with their own stories. Worst LinkedIn outreach messages. Cringe-worthy cold emails. Tales of digital networking gone horribly wrong.

Six years later, the problem hasn’t been solved. It’s been industrialized.

The clumsy, tone-deaf outreach messages that were already flooding LinkedIn and email in 2020 are now being generated, personalized, and sent at machine scale. The “I just stumbled across your profile” message didn’t go away. It got cloned. Your inbox isn’t receiving one bad pitch from one untrained sales rep. It’s receiving hundreds of AI-generated messages that sound like someone did their homework, reference the right things, use your name correctly, and are indistinguishable from the thousand other messages sent to a thousand other people that same morning.

We have a new problem. And it requires a new response.

WHAT AI CHANGED

The original sin of digital networking was low friction. Before social media and email automation, you had to stop and think before reaching out. You left a voicemail. You got through to an assistant. You ran into someone at an event. Every touchpoint required intention.

Digital tools eliminated that friction. Bad habits followed.

AI hasn’t fixed the bad habits. It has scaled them.

There is now an entire category of sales and marketing technology built around AI outreach. The pitch is seductive. Set it up, define your audience, and let the machine handle the rest. The machine writes the message, personalizes it with data from your LinkedIn profile, and sends it at volume. No human judgment required.

The result is an inbox experience that has become one of the defining frustrations of professional life in 2026. Every B2B executive I know describes their LinkedIn DMs and email inbox the same way: exhausting. Most of it AI-generated. Almost none of it relevant. Here is the irony. AI has made outreach technically better and meaningfully worse at the same time. The messages are more grammatically correct. The personalization tokens are populated. The subject lines are optimized. And none of it matters, because the signal that something is real and human and worth paying attention to has been buried under a flood of content that merely impersonates those qualities.

WHAT’S ACTUALLY IN YOUR INBOX RIGHT NOW

What’s Actually in Your Inbox Right Now

The examples from 2020 feel almost quaint now. Here’s the 2026 update.

But first, let’s start with the title of this post. “I just stumbled across your profile and thought we should connect.” Literally stumbled. As if they tripped over me in a hallway. Would you walk up to someone at a conference, extend your hand, and say “I just stumbled across you and thought we should meet”? No. You would not. Because it would be bizarre. Yet this line, or some AI-generated variation of it, lands in my inbox multiple times a week.

Here are a few more from the last 30 days. I am not making these up.

* “Hi Tony, I came across your work at Uphoff Advisory and was really impressed by your perspective on B2B media transformation. I think there’s a real opportunity to connect.” The tell: “B2B media transformation” is lifted verbatim from my LinkedIn headline. No one who had actually read my work would describe it that way. The AI pulled a string of text and dropped it in. Ten seconds of work, zero seconds of thought.

* “Tony, given your background leading companies through disruption, I think you’d have a strong take on what we’re building. Worth a 15-minute call?” The tell: “leading companies through disruption” is executive LinkedIn filler. It means nothing and applies to everyone. This message was sent to hundreds of people with the word “Tony” swapped in at the top.

* “I noticed we’re both thinking about the future of B2B marketing. Would love to add you to my network.” The tell: No human noticed anything. A prompt noticed it. And “add you to my network” is the language of a database operation, not a relationship.

The tell isn’t bad grammar anymore. It’s frictionless competence. Everything is correctly spelled. The personalization tokens are populated. Nothing is real.

THE HUMAN SIGNAL HAS NEVER BEEN MORE VALUABLE

Here is what the AI networking deluge has actually created: a massive opportunity for people who show up as human.

When every inbox is full of AI doing a passable impression of a person, a message that is clearly, demonstrably human stands out completely. Not because it’s more clever. Because it’s real.

I’ve spent over 35 years building business relationships. The relationships that have mattered, that have produced partnerships, deals, opportunities, and friendships, have all started with something specific. A shared experience. A direct reference to something I actually wrote or said. A question that only someone paying attention would think to ask. Proof that the other person saw me as a specific human being, not a job title in a database.

That specificity is now the rarest thing in professional communication. And scarcity creates value.

Look, I get it.

After 35 years of leading companies and owning a P&L, I was usually on the receiving end of outreach. People wanted meetings with me. I had the luxury of being selective.

That changed when I launched Uphoff Advisory earlier this year. Suddenly I was the one reaching out. Building a client base from scratch. Introducing myself to people who had no reason to take my call.

It is hard. It requires research. It requires thought and judgment. It requires, at times, a thick skin.

I’m not writing this from a position of someone who has never had to hustle. I’m writing it as someone who knows exactly how difficult genuine outreach is, and who believes that’s precisely the point. The difficulty is a filter. It separates the people who did the work from the people who let a machine do it for them.

EIGHT PRACTICES FOR HUMAN-FIRST NETWORKING IN THE AI ERA

These aren’t new. Some of them appeared in my 2020 post. What’s new is why they matter more now than they did then.

1. Do the work AI can’t fake. Before reaching out, read something the person actually wrote. Watch a talk they gave. Reference something specific. Not a headline. Not a job title. Something that demonstrates you paid attention. AI can pull data. It can’t demonstrate genuine curiosity.

2. Focus on the right person, not the top person. The myth that you should always reach the CEO has never been more wrong. AI outreach has made senior executive inboxes the most cluttered in any organization. The decision makers who can actually move things forward are often one or two levels down. Find them. Respect their role.

3. Understand what they’re actually trying to get done. Everyone in business has a set of real problems they are working on. Before you reach out, ask yourself: do I actually know what this person is trying to accomplish? If you can answer that clearly and connect it to why you’re reaching out, you have something worth sending.

4. Write the way you’d speak to them in person. Read your message out loud before sending it. If you would never say those words in a meeting or at an event, rewrite it. The test is simple: does this sound like a person, or does it sound like content?

5. Share before you ask. Sending someone a genuinely useful piece of research, a post that directly applies to a challenge they’re facing, or a connection to someone they should know, before making any request, is the most underused practice in B2B networking. It works because it’s real. And almost no one does it anymore.

6. Respect the inbox. Don’t automate follow-ups. Don’t escalate to email when LinkedIn goes unanswered. Don’t add people to sequences without permission. The volume of AI-powered follow-up has made persistence feel like aggression. One well-crafted message is worth more than seven automated ones.

7. Apply the Golden Rule, and mean it. Before you hit send, ask whether you would respond to this message if you received it. Not would someone respond. Would you. If the answer is no, rewrite it or don’t send it.

8. Never convert a new connection into an immediate sales target. When someone accepts your connection request, they have extended a form of professional trust. Responding with an immediate sales pitch converts that trust into a transaction before the relationship has drawn its first breath. It is the digital equivalent of shaking someone’s hand at a networking event and handing them a contract. Worse is what often follows. A pitch, then a follow-up, then another, each escalating in urgency, as if the problem is that the recipient hasn’t yet understood the offer. They understood it. They chose not to respond. Silence is an answer. Respect it. If you want to build a real relationship with a new connection, start by being a useful presence in their feed. Engage with their content. Share something relevant. Earn the conversation before you ask for the meeting.

THE BIGGER POINT

AI is not the problem with digital networking. The problem is what happens when tools designed to scale human connection are used as a substitute for it.

The professionals who will build the best networks in the next five years won’t be the ones with the best AI outreach sequences. They’ll be the ones who understood that when everyone else automated their relationships, genuine human attention became the scarcest and most valuable thing you could offer.

Show up as a human. It’s a competitive advantage.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

“Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC: a strategic advisory practice for founders, CEOs, and investors in B2B media, marketing, and technology. The businesses that drive business.

I was recently asked to speak to the Southern California chapter of the Counselors of Real Estate about the impact AI is having on their industry. The irony hit me the moment I started preparing.

The Counselors of Real Estate is one of the most selective professional organizations in the country. Membership requires a minimum of ten years in the commercial real estate business, a 31-page application, and four interviews. Members are expected to be at or near the top of their field. The roughly 900 members nationally and internationally include major commercial property owners, developers, architects, contractors, appraisers, agents, and consultants. Getting admitted is genuinely difficult.

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The talk drew sustained engagement, pointed questions, and requests for follow-up sessions from a room of about 20 of these professionals. That is not a room that nods along out of politeness.

Here is what I told them. And what the response confirmed.

A note before we start. If you know someone in commercial real estate, please share this post with them. The impact AI is having on the physical economy carries real lessons for those of us who operate in the digital economy too. So even if CRE is not your world, read on.

Worlds Colliding

To open the discussion, I acknowledged the irony. We were going to talk about the impact of an intangible, Artificial Intelligence, on the ultimate tangible asset. Commercial real estate is land, concrete, steel, and glass. You can walk through it. You can touch it. It generates cash flow you can underwrite and model. It sits at the most physical end of the business spectrum.

And it is being reshaped by a technology that exists entirely as math.

That irony is not unique to CRE. It is the defining tension of the next decade of business. Agentic AI is colliding with the physical economy. Industries organized around tangible assets, physical workflows, and on-the-ground operations are encountering a technology that restructures the knowledge work wrapped around those assets faster than most operators in those industries currently realize.

This is a theme I will return to on Uphoff on Media: When Agentic AI Meets the Physical Economy. The industries I see most impacted include commercial real estate, logistics and warehousing and manufacturing. Not in any fixed sequence. When the data, the moment, or a conversation like the one I had with the Counselors of Real Estate makes the case for the next one, I will write it.

The thesis underneath all of it: the conventional wisdom about which industries are protected because they deal in physical assets is wrong. Not because physical assets do not matter. Because the knowledge work organized around those assets is being substituted, not enhanced. And that distinction changes everything.

Lets start with CRE.

First: the AI distinction that actually matters

There is an enormous amount of coverage of AI in the general media right now. Most of it is focused on Generative AI tools like ChatGPT. That technology is useful. I use it every day. But the impact of Generative AI will be dwarfed by what comes next.

Generative AI is reactive. You prompt it, it produces something. You ask it to summarize a lease abstract, draft a tenant response, produce a market overview. It waits for you at every step. It does not plan. It does not pursue goals. It does not act without you directing it.

Agentic AI is goal-directed. You give it an objective and it figures out how to achieve it. It can browse the web, query databases, analyze documents, send communications, make decisions within defined parameters, and loop back to refine its own work. All without a human directing every step.

The practical difference is not incremental. Wired into the right data sources, an Agentic AI system can be told: pull every industrial property in a 50-mile radius of Phoenix that has traded in the last 18 months from our CoStar and county recorder feeds, flag the assets with the steepest pricing moves, and draft a ranked memo on each one with the supporting comps attached. That is achievable today for a firm with the right data access wired in. The next layer, having the agent independently infer buyer identity and capital source where it is not disclosed, and reason about institutional capital flows behind a transaction, is harder and less reliable right now. It is also coming fast. Either way, the work runs overnight. The output is waiting in the morning.

That is not an enhancement to the analyst function. For many firms, it replaces significant portions of it.

These systems exist today and are being deployed in financial services, legal, logistics, and healthcare. They are coming to commercial real estate. In some forward-leaning firms, they are already here.

The frame that will mislead you

Before getting into the CRE data, there is a conceptual point worth establishing. Because how you think about this technology will determine how you respond to it.

Almost every technology you have adopted in your career has been additive. It added a capability you did not have before without replacing what you were already doing. CRM systems. Email. Excel. CoStar. DocuSign. Zoom. In every case, the broker still brokered. The deal still closed because a human being with judgment, relationships, and accountability made it happen. Technology made that person faster, better informed, more organized. It did not replace them.

That is the frame most operators bring to every new technology. When something new shows up, the instinct is to ask: what does this add? Where does it fit my existing workflow?

That instinct will mislead you with Agentic AI.

Substitutive technologies do not add to the existing workflow. They replace significant portions of it. The printing press did not improve the scribal system. It made scribes obsolete. The automobile did not augment horse-drawn carriages. It eliminated the carriage industry within a generation. The internet did not enhance physical retail, classified advertising, and travel agencies. It dismantled them.

Across every technology disruption I have observed and led businesses through, intelligent and experienced professionals consistently underestimate substitutive change. Because their mental model is calibrated to a world where the core activity survives every technology wave. They look for the additive angle. They optimize the existing workflow. And by the time the substitutive reality becomes undeniable, the window for strategic advantage has already closed.

Two stories running at the same time

The AI story in commercial real estate is not one story. It is two very different stories running simultaneously, in different asset classes, on different timelines.

Story one: AI as a massive demand driver. Data centers are the hottest asset class in commercial real estate right now, and AI is the primary engine. Jones Lang LaSalle projects that roughly 100 gigawatts of new data center capacity will come online between 2026 and 2030. That equates to $1.2 trillion in real estate asset value creation. CBRE’s 2026 Data Center Outlook puts it in sharper relief: preleasing rates on capacity now under construction are running in the mid-70% range, against a historical norm of 40% to 50%. That is not developers betting on demand. It is demand that is already under contract before the building exists.

This sector is not demand-constrained. It is power-constrained.

The ability to deliver 300-megawatt-plus power capacity in under 36 months has eclipsed fiber connectivity as the dominant site selection factor. Power is the new location variable. Utility relationships, behind-the-meter solutions, and powered land are driving investment decisions in ways that would have been unrecognizable to this industry five years ago.

Data center construction has now surpassed office construction in the United States. If you own or manage industrial, flex, or large-footprint suburban assets, the conversation about data center conversion is a strategic question. Not a speculative one.

Story two: AI as a potential demand destroyer. In Q1 2026, AI companies accounted for 22.7% of all office leasing across major US technology markets, per CBRE. That is 11.5 million square feet of demand from a tenant category that barely existed five years ago. In San Francisco, a market many wrote off, AI tenants have absorbed large blocks of Class A space that sat dark through the post-pandemic years.

And yet. The same technology driving that demand is automating the entry-level workforce those companies hired into that space. As AI takes on the analytical, coordination, and processing work done by associates and junior staff, the companies currently expanding their footprint may need significantly less space per employee going forward.

The signal is already visible. Meta eliminated 8,000 employees while simultaneously raising its 2026 data center budget to between $125 billion and $145 billion. Read that again. This is not a typo. It says $125b-to-$145b. That is the shape of the AI economy: fewer people, more infrastructure.

The office opportunity is real but concentrated. The best buildings in the strongest markets with genuine amenity differentiation will capture AI-company tenants. The rest of the market faces the same structural headwinds it has navigated since 2020. AI may accelerate those headwinds rather than relieve them.

What AI is doing inside CRE operations

The productivity gains in underwriting and analytics are already measurable. Work that took analyst teams days is being completed in minutes. Rent roll analysis, lease abstract review, and market comps screening are being automated at speed across the industry’s leading firms.

Morgan Stanley Research found that 37% of tasks currently performed by REITs and commercial real estate firms can be automated. That represents potential efficiency gains of $34 billion by 2030. A self-storage operator has already cut on-property labor hours by 30% through AI-powered staffing optimization. Morgan Stanley's research goes further: brokerage and services firms have the highest automation potential of any CRE sub-sector, with a possible 34% increase in operating cash flow, precisely because they are furthest along in adopting AI tools at scale.

The capital markets priced the risk directly. In February 2026, shares of CBRE and JLL each fell 12% on AI disruption concerns. Cushman & Wakefield dropped 14%. The largest single-day drops for those firms since COVID. Investors were asking a specific question about how much of what the major brokerages do can be automated.

The correct answer is: not all of it. The relationship-intensive, judgment-dependent, contextually complex work of CRE is genuinely resistant to AI substitution. Complex transactions require human accountability. Significant deals require human trust. That does not go away.

But the analytical and administrative infrastructure supporting those transactions is a different story. And that infrastructure is significant cost and significant headcount.

The agentic frontier adds another layer. Leading firms are already planning for AI systems to handle routine lease monitoring, portfolio reporting, tenant communication workflows, and initial screening.

The broker of the future is a judgment and relationship layer built on top of AI infrastructure. Not an information gatherer.

Eight recommendations

Download this image to share with your team or keep as a reference.

1. Get your data house in order first. Agentic AI is only as good as the data it can access. Fragmented, inconsistent, or siloed information will limit effective deployment. Run an audit of where your critical portfolio and operational data actually lives, whether it is accessible, and whether it is structured. This is the unglamorous prerequisite. Most firms are not ready.

2. Start with underwriting and analytics. This is the highest-ROI entry point for most CRE operations. Pilot AI tools in due diligence and underwriting workflows before touching anything client-facing or relationship-intensive. Build confidence in the output quality and develop institutional knowledge before expanding.

3. Redesign roles, not just workflows. The instinct is to bolt AI onto existing job descriptions. The strategic move is to ask what each role is actually for. Analysts become AI system directors. Property managers become exception handlers and relationship escalation points. Leasing teams become closers rather than initial contact points. This requires deliberate organizational design. Not just a software subscription.

4. Evaluate your portfolio for data center conversion potential. If you own or manage industrial, flex, or large-footprint suburban office assets, run a rigorous feasibility assessment focused on power access and fiber infrastructure. Not every asset qualifies. Missing it where it does is a significant opportunity cost. The positioning window is open now.

5. Identify and protect your high-value human functions. Relationship-intensive, judgment-dependent, contextually complex work is genuinely resistant to AI substitution. Name it explicitly. Invest in it. Do not let efficiency pressure lead you to automate or understaff the functions that AI cannot replace. These are your competitive moat.

6. Assign someone to track this full-time. AI in CRE is moving fast enough that passive monitoring is not sufficient. Someone in your organization, or a trusted external advisor, needs to be continuously tracking developments, evaluating tools, and connecting AI capabilities to your specific business model. This is a core operational function now. Not a side project.

7. Think in the 18-to-36-month window. The early majority of Agentic AI adoption in CRE arrives in the next 18 to 36 months. Pilot programs launched this year become institutional knowledge and workflow advantage by 2027. Waiting until the technology is fully mature means starting from zero when the competitive landscape has already shifted.

8. Do not let perfect be the enemy of deployed. Imperfect early adoption builds organizational learning. Waiting for certainty means starting from scratch at the moment of maximum competitive pressure.

Five trends to keep on your radar

* Power infrastructure as the new location variable. Sites capable of delivering 300 MW-plus in under 36 months are being repriced. Land near utility substations deserves a serious second look.

* AI-driven lease negotiation and management agents. Early deployments are underway. Know what your counterparties are using before it appears across the table from you.

* Automated property management. AI-managed maintenance scheduling, tenant communications, and facilities optimization are compressing operating costs. Underwriting assumptions built on today’s expense ratios need revisiting.

* Synthetic market intelligence. AI-generated market data is beginning to compete with traditional data providers on speed and accessibility. The quality gap is closing faster than most operators in this space currently realize.

* The AI immunity trade in capital markets. Institutional capital is distinguishing between AI-vulnerable and AI-resistant asset classes. Physical, power-intensive, infrastructure-grade assets are being repriced as scarce in an AI economy. This is a structural tailwind for the right CRE positions. If you understand which ones qualify.

The bigger pattern

CRE is where I’m starting because the irony is sharpest here. The most tangible asset class in the economy, being reshaped by the most intangible technology. But the pattern underneath it is not unique to real estate.

Across the physical economy, the same dynamic is playing out. Knowledge work automation is hitting industries organized around physical operations. The analytical layers, the data processing, the document workflows, the coordination functions: these are being substituted by Agentic AI regardless of whether the underlying business deals in steel, logistics, farmland, or square footage.

The professionals who navigate this best will not be the ones who waited for the story to be fully written. They will be the ones who made a clear-eyed assessment of what their work actually consists of, identified what is genuinely substitutable and what is not, and repositioned their value toward the judgment, relationships, and contextual intelligence that no AI system can replicate.

That assessment is hard to make from inside an industry. It requires the right frame.

I will return to this theme. The next industry I cover in this thread will likely be logistics and warehousing: a sector that is simultaneously the infrastructure layer for the AI economy and a primary target of its most aggressive automation. The tension is different from CRE. The urgency is higher. But I will get there when the moment is right.

If there is an industry in the physical economy you would like to see covered, let me know in the comments.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

“Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC: a strategic advisory practice for founders, CEOs, and investors in B2B media, marketing, and technology. The businesses that drive business.

Thanks for reading Uphoff on Media! Subscribe for free to receive new posts and support my work.

Part 3 of B2B Media in the Machine Age

A note on timing: This post is publishing on a Friday, a break from our usual Monday/Wednesday cadence. The reason is a good one. I'll be back Monday with a piece that came directly out of a presentation I gave this week to a commercial real estate leadership group. I was invited to speak on agentic AI and its impact on commercial real estate, and it clarified something important about where agentic AI is starting to reshape the physical economy. See you Monday.

A few weeks ago, Apollo Global Management announced it was acquiring Emerald Holding for approximately $1.5 billion, a 42.1% premium, and simultaneously acquiring Questex, with the intention of combining the two into a platform of roughly 160 B2B events. The deal is the largest private equity bet on B2B events in years.

Apollo’s rationale was stated plainly: “As AI and digital tools rapidly expand the ways professionals connect and share information, they are simultaneously elevating the value of trusted, in-person gatherings, where industries come together to do business, build relationships, and make consequential decisions.”

That is either a very smart thesis or a very expensive rationalization.

I've been involved in the B2B events market for most of my career: as a CEO, as a producer and operator who has built and run hundreds of events, including major technology trade shows. What I see happening right now is not a bull market in B2B events. It is a structural bifurcation. And the M&A activity, far from being a sign that the whole category is healthy, is actually a trailing indicator of a market in the middle of a painful sorting process.

Let me explain what I mean.

The Bull Case Is Real, But Incomplete

Start with the data that supports the optimists. The U.S. B2B trade show market exceeded $15.78 billion in 2024 and is projected to surpass $17.3 billion by 2028. The post-COVID recovery in live events has been genuine. Eighty-three percent of marketing decision-makers expect marketing investments to grow in 2026. According Content Marketing Institute research, B2B marketers rank in-person events as their most effective content distribution channel, ahead of webinars, email, and social media.

Apollo is not wrong that AI is paradoxically making great live events more valuable. When an AI agent can attend a webinar, summarize a panel, and extract every lead from a virtual event in minutes, the irreplaceable asset becomes physical presence in a high-trust, high-context environment. The things that cannot be digitized: the hallway conversation, the dinner where deals actually happen, the moment when a buyer and seller read each other across a table, become more scarce and therefore more valuable.

That logic is sound. The problem is that it applies to some events, not to the category as a whole. And institutional capital has a way of buying the category rather than the distinction.

The pendulum swung hard toward events, and for good reason. When digital disruption hit B2B media, the path forward wasn’t obvious. Audiences fragmented. Attention scattered. Display and programmatic commoditized everything they touched. Events were the one line that didn’t behave like the rest of the business. They were tangible, defensible, and they made money in a way that felt increasingly rare.

At the end of the day, events are a far simpler business. Sell a booth, sell a sponsorship, fill a room. Until they’re not.

That retreat into simplicity wasn’t wrong. But it wasn’t a strategy either. It was the path of least resistance in a category that had run out of easy answers everywhere else. And a business built on the path of least resistance tends to get exposed the moment the underlying conditions shift.

They’re shifting now.

The Bear Case Nobody Is Talking About

Look past the headlines and the data tells a more complicated story.

Sixty-nine percent of B2B events leaders saw their event budgets stay flat or decrease in 2025. The share of organizers expecting budget growth in 2026 dropped to 40%, down from 70% in 2025, a dramatic deceleration in a single year. And 75% of exhibitors report pressure to reduce exhibit costs, with nearly a third feeling that pressure directly from senior leadership.

Meanwhile, the supply side is severely bloated. The past five years produced a proliferation of events across virtually every B2B vertical: many of them launched during the post-COVID bounce when demand was recovering and the bar for a “viable event” was temporarily very low. The market has not yet fully repriced that excess inventory.

Here is the question I would ask any P/E investor underwriting a B2B events platform right now: Of those 160 events in the combined Emerald/Questex portfolio: how many are genuine category leaders with defensible market positions and profitable unit economics, and how many are subscale shows competing for the same shrinking pool of mid-market sponsorship dollars? Scale is not a strategy. It is a starting point. And aggregating subscale assets does not automatically produce a premium platform. They produce overhead.

The Venue Directory data is telling: in the UK market, total event inquiries declined 4% in 2025, while the average RFP value increased 2%. Fewer companies asking. Bigger commitments from the ones who are. That's not a market in decline. That's a market concentrating around fewer, more serious buyers. Bifurcation, not a boom.

The Sorting Is Already Underway

Here is what I believe is actually happening, and what I see when I look at the data alongside my own operating experience:

The B2B events market is sorting itself into two distinct tiers, and the gap between them is widening.

* Tier One: The Category-Defining Show. In most verticals, one or two large-scale trade shows will survive and likely strengthen. These events have established brand equity, dominant market share among exhibitors and attendees, and the network effect that makes them self-reinforcing. If your industry peers are there, you have to be there. Sponsors know it, which is why top-tier sponsorships command premium pricing that smaller shows simply cannot match. The research confirms the logic: a single Tier-1 sponsorship producing 30 ICP-fit meetings outperforms eight Tier-3 sponsorships producing a handful of meetings each. Concentration produces leverage. Spread produces noise.

* Tier Two: The Subscale Middle. This is where the pain is happening. Events with a few hundred to a thousand attendees, modest sponsor rosters, undifferentiated programming, and no clear reason for a senior buyer to travel and spend two days away from the office. Many of these events were viable when the market was growing fast enough to carry marginal players. That grace period is over. Planning for large-scale events dropped 12% year over year in 2026, and the budget rationalization happening inside B2B marketing departments is accelerating the winnowing. These events do not need better marketing. They need a fundamentally different model or an exit.

What is emerging in the space left by the subscale middle is something altogether different, and it may be the most interesting development in B2B events right now.

The Return of the Intimate Event

There is a third format quietly gaining traction that does not fit neatly into the traditional event industry model, and it deserves its own analysis.

Call it the intimate conference, the executive summit, the hosted roundtable, the closed-door dinner. The format varies, but the defining characteristics are consistent: small by design (25 to 150 attendees), curated ruthlessly, content-rich, and structured specifically to create real buyer-seller engagement in an environment where both parties actually want to be in the room.

These are not the scaled-down version of a trade show. They are a fundamentally different product.

Sixty-three percent of event organizers report increased demand for micro-events and intimate gatherings. Planning for small, hosted events is up 59% year over year. And the research on why is unambiguous: executives who will not attend a five-hundred-person conference will often accept an invitation to a closed-door peer discussion. The signal-to-noise ratio is entirely different.

One trend piece from the events industry captured the format precisely: dinners of twenty-five to thirty carefully selected peers, no stage, no slides, no formal presentations, just honest, peer-level discussion about what is actually working, guided by a respected industry leader. That format reflects a broader shift in what senior buyers actually value when they give up two days of their calendar.

The content richness matters as much as the intimacy. The events that are thriving in this format are not networking events with a thin content wrapper. They are substantive: built around real intellectual engagement, genuine industry debate, and a programming philosophy that treats attendees as practitioners, not audiences.

The Budget That Moved

Here is the insight I have not seen written clearly anywhere, and it changes the economics of the intimate event model significantly.

The conventional assumption is that B2B event sponsorship comes from the marketing budget: specifically the events or demand generation line, controlled by the CMO or VP of Marketing. That assumption is increasingly wrong for the intimate conference segment.

The high-value, curated event, the executive dinner, the invitation-only summit, the closed-door roundtable, is increasingly funded from field marketing budgets. And field marketing budgets are controlled by sales leaders, not corporate marketing.

This matters enormously for several reasons.

First, the ROI calculus is different. A sales leader evaluating a $25,000 sponsorship of an intimate roundtable with 40 qualified senior buyers is making a fundamentally different calculation than a CMO evaluating the same investment. The sales leader wants pipeline. The CMO wants brand reach and lead volume. Intimate events are almost perfectly calibrated to the sales leader’s criteria and poorly suited to the CMO’s. When more of the sponsorship dollars for these events comes from field marketing, the events become more durable, because they are being funded by the part of the organization that measures outcomes most directly.

Second, field marketing budgets are growing even as corporate marketing budgets flatten. The pressure on CMOs to demonstrate brand-level ROI from events has never been higher. The pressure on sales leaders to find efficient pipeline has never been higher. Intimate, curated events serve both, but they particularly serve sales, which is why the budget is shifting.

Third, and most importantly for event operators: this means the buyer for your intimate event sponsorship is often sitting in a different seat than the buyer for your trade show sponsorship. If you are selling intimate event sponsorships the same way you sell trade show booths: to the same contacts, with the same collateral, at the same price points, you are leaving money on the table and probably losing deals you should be winning.

What Agentic AI Changes About All of This

The Apollo thesis, that AI makes in-person more valuable, not less, is correct in principle. But it is incomplete. The more precise version of the thesis is this:

AI makes the right in-person experiences dramatically more valuable, while making the wrong ones far easier to skip.

Here is why. When an AI agent can synthesize the proceedings of a three-day conference, extract every relevant insight, identify every vendor worth evaluating, and deliver a crisp briefing document, the value proposition of attending a mediocre event collapses. Why would a senior executive spend two days and several thousand dollars to attend an event whose intellectual output can be captured and distilled by an AI with a quick prompt?

The answer is: they will not. And they are already rationalizing their event calendars on exactly this basis.

What AI cannot replicate is presence. Physical presence in a room of thirty senior practitioners who are all wrestling with the same problems. The relationship that forms over dinner. The conversation that happens in the fifteen minutes between sessions when two people realize they should be doing business together. The trust that is built when a buyer watches a seller engage honestly with a peer-level challenge rather than defaulting to a pitch.

Agentic AI does not threaten those experiences. It actually elevates their scarcity value. But it will ruthlessly expose and accelerate the decline of the events that were always surviving on convenience and inertia rather than genuine value.

Takeaways

For B2B event operators and producers: Your portfolio strategy needs to reflect the bifurcation, not pretend it does not exist. If you own or operate a Tier-1 show in your vertical, invest aggressively in defending and extending that position. The network effects are real and the window to cement category dominance is narrowing. If you operate subscale events in the middle tier, the question is not how to market your way out. It is whether the event has a genuine reason to exist, and if so, whether it can be repositioned as a premium intimate format rather than a smaller version of a trade show. Those are very different events and require very different operating models.

The intimate format is not a budget trade show. It is a distinct product that requires different programming philosophy, different sponsor relationships, different pricing, and often a different internal champion at the sponsor company.

For B2B information and media companies: Events are increasingly the one revenue line that AI cannot directly disintermediate. Your content can be summarized. Your newsletters can be replicated. Your SEO can be outranked. Your in-person community cannot be cloned.

The strategic imperative is to develop a deliberate events architecture, not a collection of shows, but a portfolio designed around specific audience segments and intentional format choices. The companies that figure out how to build category-defining shows in their verticals, while simultaneously running curated intimate formats for senior practitioner audiences, are creating a defensible revenue model. The ones running a handful of subscale shows alongside declining digital revenues are in a structurally deteriorating position.

For P/E investors: The Apollo/Emerald/Questex bet may prove right over time. The thesis is sound. But the execution risk is significant, and the questions worth pressure-testing are: What is the quality distribution within that portfolio of 160 events? How many are genuine Tier-1 category leaders versus subscale assets that are being carried by the platform? What is the plan for the middle tier: rationalization, repositioning, or hope?

The more interesting investment thesis in B2B events right now may not be the large-scale consolidation play. It may be the premium intimate format operator who has figured out the field marketing budget unlock, built authentic practitioner communities, and is running events that senior buyers actually want to attend. That is a smaller company, a harder business to find, and a much better risk-adjusted investment.

The B2B events market is not dying. But it is splitting into winners and losers faster than most of the institutional capital entering the space appears to understand. The events that survive will do so because they are genuinely irreplaceable: because the experience cannot be synthesized, compressed, or skipped. Everything else is in a slow motion decline that more M&A will not fix.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

“Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC: a strategic advisory practice for founders, CEOs, and investors in B2B media, marketing, and technology. The businesses that drive business.

Part 2 of B2B Media in the Machine Age

Something significant is being built from the ruins of ad-supported B2B media. Not a replacement for what was lost. Something more valuable and sustainable.

Call it the vertical intelligence company.

These businesses don’t look much like the B2B media companies that came before them. They’re not chasing page views and declining CPMs. They’re not dependent on programmatic advertising, platform algorithms, or the next trade show cycle. They’re building something that professional buyers, executives, and operators have always needed and rarely had: trusted, domain-specific intelligence that’s hard to find anywhere else, delivered efficiently, priced appropriately, and designed for the way business professionals actually work today.

The irony is sharp. The forces that destroyed the old model are the same forces enabling the new one.

The Trends That Changed the Market

Three structural shifts have converged to create an opening that didn’t exist five years ago.

* The professional trust deficit reached a breaking point. Business professionals have always been skeptical of general business media when it comes to decisions that actually matter. But the gap between what general media can deliver and what domain experts actually need has never been wider. When a procurement executive needs to understand how agentic AI is reshaping supplier selection, or when a payments operator needs current intelligence on cross-border settlement, Bloomberg and the Wall Street Journal are not the answer. They were never the answer. What’s changed is that the alternatives can now be built.

* The cost structure for building vertical intelligence collapsed. Proprietary data collection, analysis, content production, distribution, and subscriber management used to require enterprise-scale infrastructure. That’s no longer true. A well-run team of three to five people can now build a meaningful data asset, publish at a professional cadence, reach a targeted professional audience, and manage subscriber relationships, at a fraction of the cost that would have been required a decade ago. The barriers that kept this model the exclusive domain of large research firms like Gartner and Forrester are down.

* The monetization stack expanded. The old B2B media model ran on two revenue lines: advertising and events. The vertical intelligence company runs on five or six. Subscriptions. Tiered data access. Benchmarking reports. Sponsored research. Community and membership. Consulting adjacencies. None of these revenue streams is new in isolation. What’s new is the ability to stack them at small-company scale, without the overhead that used to make multi-revenue-line businesses the province of large media conglomerates.

The Accelerant: Agentic AI

Here is where the story gets interesting for operators and investors paying close attention.

The AI moment is broadly understood as a threat to media businesses. That framing is correct for one type of media business. It’s incorrect — and actually inverted — for another.

Agentic AI is devastating to commodity information. If your business model depends on aggregating, packaging, and distributing information that can be found elsewhere, AI has already begun to erode that value. Search behavior is changing. Attention is shifting. The traffic model that kept general digital media alive is under structural pressure that will not reverse.

But agentic AI amplifies the value of what it cannot replicate: irreplaceable domain expertise, proprietary data, and trusted professional community. A language model can synthesize publicly available information about B2B payments trends. It cannot replace a team with deep relationships inside the payments industry, producing original research, running proprietary surveys, and hosting conversations that shape how practitioners think about the problems in front of them.

The vertical intelligence company is structurally positioned to benefit from exactly the shift that is accelerating the decline of general media. That’s not a coincidence. It’s a function of what these businesses are actually built on.

What This Looks Like in Practice

The model is already operating. You can see it clearly in businesses that have made the transition, whether or not they would describe themselves in these terms.

* PYMNTS is not a payments news site. It’s a payments intelligence company with original data, proprietary research, and direct subscriber relationships. Its value to readers and sponsors comes from domain depth and data, not from traffic volume or display advertising.

* Art of Procurement is not a procurement podcast. It’s a professional intelligence and community platform for procurement leaders, with content, events, benchmarking, and advisory services built around a specific professional audience with real budget authority and unmet intelligence needs.

* ThomasNet, through its acquisition by Xometry, demonstrated a version of this at industrial scale: a directory and information platform that became a vertical intelligence infrastructure for manufacturing sourcing: embedding data, analytics, and transactional capability into a single professional ecosystem.

These businesses share a structure. They have domain expertise that took years to accumulate. They have proprietary data that can’t be replicated by scraping public sources. They have deep, trusted relationships with a specific professional community. And they have multiple ways to monetize all three.

The Implications

For operators: The question is no longer how to protect ad revenue. It’s whether you have the domain expertise, audience trust, and data assets to make the transition to vertical intelligence. Many current B2B media businesses do not. The ones that do have the foundation for something more valuable than B2B media has produced in a generation.

The transition requires discipline, and a willingness to reject the instinct that drove many B2B media businesses further into the trap. Faced with revenue pressure, the natural move was to go broader: more topics, more verticals, more audience segments. The logic was intuitive. More reach means more inventory means more revenue. What operators discovered was the opposite. Each step out diluted the demographic precision, the audience engagement, and the domain authority that had made them valuable in the first place. They traded their core asset for scale that turned out not to be worth much.

The vertical intelligence company runs the opposite play. It goes deeper, not broader. And depth, executed with the right technology and data capability, turns out to be the mechanism for scale, not the obstacle to it. You are not trying to be everything to a broad industry. You are building deep credibility with a specific professional community and creating data and intelligence that community cannot get anywhere else. That is a different editorial strategy, a different commercial model, and a different organizational design than the one most B2B media operators are running today.

For investors: The valuation framework for vertical intelligence companies should not be built on CPM multiples or traditional media metrics. The right comparables are SaaS businesses and research platforms: recurring revenue, data asset value, community lock-in, and net revenue retention. These are the metrics that reflect what these businesses actually are.

The market has not fully priced this yet. Businesses that look like small B2B media companies on the surface are sometimes sitting on data assets, subscriber relationships, and domain authority that are worth considerably more than their current revenue multiples suggest. The investors who develop a sharper lens for this category before it becomes consensus will have an advantage.

The Larger Irony

Business professionals have always needed what the vertical intelligence company delivers. Domain-specific, trustworthy intelligence that helps them make better decisions, stay ahead of their markets, and do their jobs at a higher level. That need didn’t emerge with the internet or with AI. It has always been there.

What has changed is the ability to serve it efficiently, at scale, and profitably. The technology that is collapsing one model of information business is simultaneously making it possible to build a better one.

The B2B media company of the future is already here. It just doesn’t call itself a media company.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

“Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC: a strategic advisory practice for founders, CEOs, and investors in B2B media, marketing, and technology. The businesses that drive business.

Part 1 of B2B Media in the Machine Age

B2B media isn’t dying. It’s being restructured by forces most of the industry still hasn’t fully identified.

Over the next three posts, I’m going to name them. This series covers how the category got here, where it’s going, and what the restructuring means for the people running these businesses right now.

I’ve spent most of my career inside B2B media. Publisher, CEO, operator, investor. I watched the print market collapse and the digital transformation happen in real time. I watched companies that built durable, franchise-level businesses make decisions that looked rational in the moment and proved catastrophic over time. I was inside some of those decisions.

What I’ve come to understand is that the real break wasn’t the one most people talk about.

It wasn’t digital. It wasn’t the platform dependency trap. It wasn’t even the collapse of print advertising, as dramatic as that was.

The break was in the relationship.

For decades, the most valuable thing a great B2B media company sold wasn’t reach. It was buyer intelligence. Publishers knew their professional communities better than marketers did. That knowledge was the product. Advertisers paid for access to a publisher’s understanding of the buyer, not just access to the buyer.

Digital ended that. It shifted the value proposition from intelligence to execution. From knowing the buyer to delivering impressions. It looked like progress. It was actually a trade. And most of the industry made it without understanding what they were giving up.

That’s where this series starts.

The Wrong Lesson

The wrong lesson is the most dangerous kind. It feels like wisdom. It’s derived from real experience. It’s shared broadly enough that it becomes consensus. And it sends an entire industry in exactly the wrong direction.

B2B media drew the wrong lesson from its collapse. And most of the industry is still paying for it.

The revenue decline was real. The audience fragmentation was real. The structural pressure from programmatic advertising, platform dependency, and the erosion of print was real. I’ll cover the mechanics of that unwind later in this series. What I want to examine now is what operators concluded from it, why that conclusion accelerated the damage, and what the businesses that survived actually learned instead.

Because the survivors learned something different. Something that looks counterintuitive on the surface but turns out to be the only strategy that makes sense once you understand what was actually lost.

They learned that the product was never the content. The product was the relationship, and specifically, what that relationship was built on.

The Flywheel Nobody Named

For decades, the best B2B media franchises ran on a flywheel that nobody stopped to name, because it didn’t need a name. It simply worked. And it’s only in hindsight we can now see how powerful this flywheel actually was.

It went like this: editorial focus and depth created genuine buyer knowledge. That buyer knowledge made B2B media companies indispensable to marketers. Not because they could execute a program that reached an audience, but because they understood that audience better than the marketer did. A publisher covering the electronics manufacturing supply chain knew procurement cycles, knew what engineers were worried about, knew which decisions were being made and by whom and when. That knowledge was the basis of the relationship. Marketers came to publishers not just to advertise, but to learn.

That knowledge advantage drove advertiser investment. That investment funded deeper editorial. Which produced more buyer knowledge. The flywheel spun.

The value wasn’t reach. It was intelligence. Publishers were the people who knew your buyer better than you did, and were willing to make the introduction.

The relationship between B2B media and B2B marketers was, at its core, a knowledge exchange. I know something you need to know. And because I know it, you need me in the room when you’re making decisions about how to reach this market.

That’s not a vendor relationship. That’s a strategic partnership. And it’s the thing that justified premium pricing, long-term contracts, and the kind of institutional loyalty that made B2B media franchises durable across decades.

What Digital Actually Changed

Here’s what’s usually said about what digital did to B2B media: it disrupted the print revenue model, it commoditized reach, it introduced programmatic pricing, and it handed audience aggregation power to platforms.

All of that is true. But it’s not the root cause.

The root cause is that digital marketing fundamentally changed the nature of the value relationship. It shifted the marketer’s core question from what do you know about my buyer? To what can you execute for me? And once that question changed, everything downstream changed with it.

In the pre-digital model, the marketer needed the publisher’s intelligence to make good decisions. In the digital model, the marketer had dashboards, attribution tools, programmatic platforms, and intent data feeds. The publisher’s buyer knowledge: built painstakingly through years of editorial investment, suddenly looked less like a strategic resource and more like one input among many. Or worse, like something the marketer could approximate themselves.

The relationship didn’t erode. It was reclassified. Publishers went from strategic partners who knew the market to execution vendors who could deliver eyeballs. And execution is a commodity business. You can always find someone who will run the program cheaper.

Digital didn’t just change the economics of B2B media. It changed what B2B media was being paid for. And that’s a much harder problem to solve than a CPM collapse.

The content syndication era made this concrete. The thesis behind pay-per-lead programs — TechTarget built an entire company on it — was that documented purchase intent could substitute for the buyer knowledge publishers had historically provided editorially. If a contact downloaded a white paper, you had evidence of interest. That was something.

But it commoditized fast. Because intent signals don’t convey buyer intelligence, they generate lists. And lists can be bought from a dozen vendors. The publisher was no longer in the room helping the marketer understand the buyer. They were handing over a spreadsheet and moving on.

Even the smarter digital adaptations never fully restored what had been lost. The flywheel didn’t slow down. It stopped cold.

The Wrong Lesson, Compounded

When revenue started declining, the instinct across most B2B media businesses was to grow the addressable market. More topics. More verticals. More content formats. More audience segments. If the core audience wasn’t generating enough revenue, find more audiences.

The logic tracked on a spreadsheet. Reach drives inventory. Inventory drives advertising revenue. More audience equals more revenue.

What operators discovered, slowly, then all at once, was that each step out diluted the thing that had made them valuable in the first place. Demographic precision eroded. Engagement declined. The domain authority that had justified premium pricing started to look like every other trade publication. The buyer knowledge that had been built over years in a specific vertical couldn’t survive dilution into adjacent markets. Advertisers noticed before publishers did.

The businesses that went broadest to survive ended up the most exposed. They had traded their one defensible asset: deep knowledge of a specific professional community, for volume that turned out to be worth nothing at scale.

This is the lesson the collapse was supposed to teach. Most of the industry drew the opposite conclusion.

What the Survivors Actually Learned

The businesses that came through the collapse, not just surviving but building something sustainable, did something that looked irrational from the outside. They went deeper instead of broader. They doubled down on domain expertise at the exact moment the market seemed to be rewarding scale.

They learned three things that the broader industry missed.

Focus is the mechanism for scale, not the enemy of it. When you go deep enough in a specific vertical, the audience you build is more valuable per member than any broad audience can be. A readership of 50,000 senior procurement professionals is worth more to the right advertiser than 5 million general business readers. The math of depth beats the math of breadth once you stop measuring impressions and start measuring influence.

Technology doesn’t change the value of buyer knowledge, it changes how you build and deliver it. The vertical intelligence model isn’t a nostalgia play for the print era. It’s a recognition that the original value proposition: we know your buyer better than you do, is more achievable now than it ever was. First-party data, behavioral signals, proprietary research, AI-assisted synthesis: the tools for building genuine buyer intelligence have never been more powerful. The question is whether publishers will use them to rebuild the relationship or just generate more content.

The relationship has to be the product again. This is the hardest shift. It requires rebuilding sales motions, repricing the offering, and reconstituting the trust that was traded away during the programmatic era. But it’s the only path to a business that isn’t perpetually competing on execution cost.

The vertical intelligence model isn’t a new idea. It’s the original idea, rebuilt for the current era, with data, AI, and workflow integration as the delivery mechanism instead of editorial alone.

The Open Question for Operators

The vertical intelligence model is not available to every B2B media business. It requires a foundation that not every operator has: genuine domain expertise, a professional audience with real workflow needs, and the organizational willingness to sell intelligence access rather than impression inventory.

The honest diagnostic question for any B2B media operator right now is not how do we grow our audience? It is: do we still know something specific about a specific professional community that that community cannot easily find elsewhere? If the answer is yes, the path to rebuilding the strategic relationship is more accessible than it has ever been. If the answer is no, if the business traded its domain authority for breadth that didn’t pay, the recovery is harder. But the diagnosis at least is clear.

The flywheel that built the best B2B media franchises was always powered by knowledge. Digital stopped it by changing what knowledge was worth. The question now is whether the industry will rebuild it with better tools, or spend another decade competing on execution in a race it was never going to win.

The category isn’t just being disrupted. It’s being reinvented. In Part 2, I’ll lay out what the leading B2B media companies are actually becoming, and why the ones that get there first will be very difficult to displace.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

“Uphoff on Media” is published by Tony Uphoff, Founder and Managing Partner of Uphoff Advisory, LLC: a strategic advisory practice for founders, CEOs, and investors in B2B media, marketing, and technology. The businesses that drive business.

The most useful thing I can offer on the AI disruption isn’t a prediction. It’s a pattern. One I’ve lived through before, at scale, in this sector.

I was in the front seat of the roller coaster.

At the time, I was running a fast-growing, $300 million print-centric B2B media business, and by every metric, we were succeeding. Then the Internet arrived. Not gradually, or at least it didn’t feel that way from the operator’s seat. One quarter you’re managing circulation strategies, analyzing market share reports, and launching events. The next, employees are stopping you in the hallway, the board is asking pointed questions, and customers want to know what you’re doing “about the Internet.”

I had covered technology disruptions for years. The brands I was running served technology professionals. I understood disruption conceptually. What I had never done was lead a business through one in real time, with real consequences, with people’s livelihoods and a board’s confidence depending on decisions I had to make before I had the answers.

No HBR article prepared me for it. No academic framework made it manageable. What got me through it — and what I learned at real cost — was hard, experiential learning. The ability to stay calm when the pressure was extraordinary. The ability to move forward before the path was clear. The ability to hold the organization together while simultaneously rebuilding what the organization did, what it sold, and what it was worth.

That moment taught me more about leadership than anything before or since.

Let me be clear about something before we go further.

If you’re reading this expecting another dramatic proclamation about the impact of artificial intelligence, stop here and go read something else. There is no shortage of that content. AI is saturating every media channel, every business conference and every board agenda. The volume of coverage is inversely proportional to the operational clarity it produces. Most of it is written by people who are analyzing AI from the outside and with minimal practical insight. Very little of it is written by people who have actually led businesses through a technology disruption of comparable scale.

I have. And that is the lens for everything that follows.

For readers who are digital natives and have built their entire careers in a world where the Internet already existed — which is at least half of you, and probably more — the Internet disruption is history, not lived experience. Some of you were in the early stages of your careers when it happened, not yet in leadership positions where the decisions landed on your desk. I’m not telling you this to establish context. I’m telling you this because the playbook from that disruption is the closest thing to a field guide for this one.

The Internet didn’t just change how B2B media companies distributed content. It restructured what content was worth, who would pay for it, and what a media business fundamentally was. The companies that survived and grew were the ones that recognized they were in a different business than the one they thought they were in, and acted on that recognition before circumstances forced it.

The companies that didn’t survive treated the Internet as a distribution channel for what they were already doing. They optimized the wrong thing until it was too late to optimize the right thing.

We are in that moment again. And it may be more disruptive still.

Which brings us to the Anthropic white paper, and why it matters more than the coverage it has received.

The Anthropic Institute titled it "When AI Builds Itself." That title is the thesis. This is not a technology document, and it is not another breathless dispatch from the AI hype cycle. It is a document that describes, in hard data, the operating environment you are already living in, whether your organization has named it yet or not. If you lead a B2B media, marketing, or technology company — or invest in one — the questions it raises are not theoretical. They are arriving in your inbox, your board meetings, and your leadership team meetings right now.

It is not AI theater dressed in business language. It is a field guide, written from the operator’s seat, for the decisions in front of you.

What the Paper Actually Says: Three Things That Matter

Most AI commentary asks you to imagine the future. This paper shows you the present. Anthropic is documenting, in hard operational data: code output per engineer, task completion rates, productivity multiples measured against human performance, what AI is already doing inside one of the most advanced technology organizations in the world. Three findings have direct consequence for every leader in this sector.

First, the development productivity compression is not incremental. It is structural.

As of May 2026, more than 80% of the code merged into Anthropic’s own codebase was authored by Claude. Before Claude Code launched in early 2025, that number was in the low single digits. The result: in the second quarter of 2026, the typical Anthropic engineer was merging 8x as much code per day as they were in 2024. In a March 2026 internal poll of 130 Anthropic employees, the median respondent estimated they were producing around 4x as much output with AI assistance as they would have produced without it.

Read that again. 8x more code per engineer per day. 4x overall output. At the most sophisticated AI development organization in the world, operating at the frontier of what these systems can do.

For B2B technology-enabled businesses: companies that use technology to create, develop, and distribute their products and services but are not selling technology directly, this is the most consequential finding in the paper. The engineering backlog that every one of these businesses has been managing as a permanent condition of operating is not a permanent condition. It is a constraint that is beginning to lift. And when that constraint lifts, the question is not how to go faster on the existing roadmap. It is what the roadmap looks like when the binding constraint of the past decade no longer applies.

Second, the capability curve is accelerating faster than most executives have internalized.

The paper documents that the length of tasks AI can reliably complete on its own has been doubling roughly every four months. In March 2024, Claude could complete software tasks that take a human roughly four minutes. By 2026, that horizon had extended to 12-hour tasks. The paper projects that tasks taking a skilled person multiple days could come into range before the end of this year. Tasks taking weeks could follow in 2027.

This is not a projection from an optimistic analyst. It is a measured trend line from observed performance data, documented by the organization building these systems. The pace of change embedded in these numbers is faster than most strategic planning cycles. Which means most organizations are planning against a capability baseline that is already out of date.

Third, the paper names three possible futures: and the one most relevant to B2B operators is already underway.

Anthropic outlines three scenarios for what comes next: the trend stalls at current capability levels; AI labs see compounding efficiency gains while humans retain direction-setting; or AI achieves full recursive self-improvement. The paper is direct that scenario two — compounding efficiency gains with human oversight — is where the evidence points today.

And the operational implication of scenario two is named explicitly: a 100-person company can increasingly do the work of a 1,000-person one, because each employee sits atop a pyramid of AI agents.

That sentence is not a marketing claim. It is a structural shift in competitive dynamics. And it is happening now.

The All-Too-Human Reality

Before the frameworks. Before the action items. First, this.

The decisions described in this post are not technology decisions. They are career decisions. Organizational decisions. In some cases, identity decisions, for leaders who have built their professional lives around capabilities that are being restructured around them.

The wave of senior leadership departures we are already seeing: Adobe, Walmart, and others whose CEOs have cited AI transformation as a reason for stepping aside, is being reported as a business story. It is also a deeply human story. Experienced, successful leaders concluding that they are not the right person to finish what they started. That takes a form of self-awareness and courage that is genuinely rare, and genuinely hard.

I know what it feels like to run a business through a technology disruption where the ground is shifting and the answers are not yet available. The pressure is real. The isolation is real. The temptation to project more confidence than you feel, to buy time with reassuring language while privately figuring it out, is entirely understandable. I have been there.

What I learned is that the leaders who navigate disruption well are not the ones who pretended it was simple. They are the ones who stayed honest about the uncertainty while remaining decisive about the next step. Those are compatible. They just take practice.

With that said: here is the field guide.

For CEOs: The Two Decisions That Cannot Wait

Decision One: Are you the right person to finish this?

This is the question nobody is asking directly in your organization, which means you have to ask it yourself.

It is not a referendum on your career or your competence. The leaders stepping aside at Adobe, Walmart, and elsewhere are not failures. Several of them are arguably demonstrating their greatest leadership act: the clarity to recognize that a different phase of a company’s life requires a different profile of leader, and the integrity to act on that recognition rather than hold the seat.

The honest version of this question has three parts. Ask yourself each one directly.

Do you have genuine fluency with AI? Not enthusiasm for it. Not familiarity with the vocabulary. Actual working knowledge of how these systems operate and where they create and destroy value.

Do you have the appetite for genuine business model transformation? Not the efficiency overlay that is insufficient. The fundamental rethinking of what your business is and what it sells.

Do you have the runway to see it through? Personal energy. Board confidence. Team trust. This is an 18-to-36-month transformation, not a 6-month initiative.

The Anthropic paper puts this in sharp relief. The organizations that are capturing the gains described in this paper are not doing so by running AI pilots. They are rebuilding how work gets done at a fundamental level. That requires a leader who can hold the organization through sustained uncertainty. Not someone who manages to a quarterly plan and waits for the path to clarify.

If the answer to all three questions is yes, your job is to lead with full commitment and full transparency. If the answer to any of the three gives you pause, that pause is worth thinking about before circumstances force the conversation.

Decision Two: How do you set expectations that are simultaneously honest and stabilizing?

You have four distinct constituencies and they need four different conversations. Conflating them is where leaders make the most consequential mistakes.

* Employees need to hear the honest version before they read the trade media version. Your people are not naïve. They can see what is happening around them in the sector. The Anthropic paper documents that AI is already handling more than 80% of code production at one of the world’s leading technology organizations. People doing knowledge work; writing, analysis, research, production, are drawing their own conclusions about what that trajectory means for their roles. Vague reassurance does not calm that anxiety. It amplifies it, because it signals that leadership either doesn’t know or won’t say. What employees need is an accurate picture of what is changing, a genuine account of what it means for their roles, and a believable plan for how the organization is going to navigate it. That plan does not need to be complete. It needs to be real.

* Boards need a different frame entirely. Most boards are running on mental models formed in the last disruption cycle. They are simultaneously over-excited about AI as a narrative and under-equipped to evaluate it as a business reality. The paper’s finding that Anthropic engineers are merging 8x more code per day than two years ago is exactly the kind of concrete, operational data point your board needs. Not as a benchmark to match, but as evidence of the pace of change they are governing through. Your job with the board is not to match their enthusiasm or manage their anxiety. It is to be the most credible voice in the room about what AI actually means for your competitive position, your cost structure, and your product roadmap over the next 18 months. Specificity is your currency. Vague AI optimism is not.

* Investors, particularly in PE-backed companies with defined fund timelines, need the transformation timeline mapped against the investment thesis clearly and honestly. Efficiency gains from AI adoption can show up in 6 to 12 months. Revenue model transformation: new intelligence products, subscription conversion, data licensing, is an 18-to-36-month story. If those timelines do not align with fund structure, that is a conversation to have now rather than at the next quarterly review.

* Customers want proof, not vision. The AI positioning saturating the market right now ranges from genuinely substantive to transparently hollow, and your customers can usually tell the difference. What they need from you is specific capability milestones, honest timelines, and a clear account of how your AI investments translate into value for them. The vendors who earn trust in this period will be the ones who under-promised and over-delivered.

For Operators: Three Decisions About How the Work Gets Done

Decision One: The development bottleneck is lifting. Are you positioned to act?

The Anthropic paper documents something that should fundamentally change how every B2B technology-enabled business thinks about its product development process. When AI systems can write production-quality code at 8x the previous rate, and when that code is passing the review standards of the world’s best engineers, the operating assumption that has governed every technology roadmap for the past decade — that engineering capacity is the binding constraint — is no longer reliable.

This is not a small adjustment. For most B2B technology-enabled businesses, the operating rhythm has been: more demand than we can build for, so we prioritize ruthlessly and leave significant product opportunity on the table. The AI-driven productivity shift does not eliminate prioritization. It changes the calculus for what is worth building at all. Workflow improvements that were previously uneconomical become viable. Vertical-specific modules for individual enterprise customers become feasible. The long-deferred infrastructure work that has been on every product roadmap for three years finally gets addressed. The paper notes that Anthropic engineers are now using AI to fix problems that had accumulated for years, including one case where Claude shipped over 800 fixes that reduced a class of API errors by a factor of one thousand, work an engineer estimated would have taken a human four years to complete.

The question for operators is not “how do we use AI to go faster on our existing roadmap.” It is “what does the roadmap look like when the constraint we have been managing for ten years is no longer the binding constraint.” Those are different questions and they produce different answers.

Decision Two: Is your implementation model a competitive advantage or a liability?

The Anthropic paper describes a world where AI agents can work autonomously for hours on complex, open-ended tasks: tasks with no clear specification, where the approach itself has to be figured out along the way. Claude’s success rate on the most open-ended tasks reached 76% in May 2026, up 50 percentage points in six months.

For B2B technology vendors, this has a direct implication for professional services revenue. If AI agents can execute configuration, data migration, and workflow setup tasks that currently require billable professional services hours, then implementation revenue, a significant line item in many B2B technology contracts, compresses or disappears. Some operators will experience this as margin erosion and fight it. The sharper operators will get ahead of it by treating faster time-to-value as the selling point it actually is. Shorter implementation cycles mean shorter time to demonstrated ROI, which shortens sales cycles, improves renewal rates, and reduces customer acquisition cost. The math on giving up implementation revenue in exchange for those outcomes is often compelling. The operators who do this analysis now rather than after the revenue line is already under pressure will be in a better position.

Decision Three: What not to do, and what to do now

The mistakes being made at scale right now in this sector are worth naming plainly, because they are tempting mistakes made by smart people under real pressure.

* Premature restructuring announced before the capability exists. Declaring an AI transformation in a board presentation or a customer communication without the underlying product reality to support it. This is not a positioning mistake. It is a trust mistake, and it is very difficult to recover from once customers and employees have seen the gap between the announcement and the reality.

* Cutting the talent you will need in 18 months. The current wave of AI-driven workforce decisions contains real efficiency gains and genuine AI-washing in roughly equal measure. The leaders who will regret decisions made in this period are the ones who eliminated domain expertise in the name of efficiency. Editorial judgment. Deep customer relationships. Vertical market knowledge. These are exactly the inputs that make AI-generated intelligence valuable rather than generic. Cut them now and you will spend the next three years trying to rebuild what you gave away. The Anthropic paper is instructive on this point. The human role that remains irreplaceable, even at the most AI-intensive organization in the world, is research taste and judgment. Choosing which problems matter. Knowing which results to trust. Recognizing when an approach is a dead end. That same judgment, applied to your vertical market, is your most defensible asset. Protect it accordingly.

* Ignoring the mid-level leadership layer. CEOs get the attention. Investors get the attention. The VP of Product, the Chief Revenue Officer, the Managing Editor, these are the people who will execute the transformation or block it. They are navigating this period with almost no guidance designed for them. The operators who invest in their mid-level leadership now, who bring them into the strategic conversation rather than cascading decisions down to them, will have a significant execution advantage.

What to do now: Audit your product roadmap through the lens of a changed development constraint. Identify your implementation revenue exposure and model the unit economics of faster time-to-value as an alternative. Inventory your proprietary data assets, genuinely, not aspirationally. And protect the domain expertise in your organization as the strategic asset it is, because it is what will make your AI-generated intelligence valuable rather than generic.

For Investors: The Filter That Matters Now

The Three-Characteristic Test

The PE-owned B2B media and marketing landscape contains a wide distribution of asset quality, and AI is going to widen that distribution significantly. The Anthropic paper’s scenario two: compounding efficiency gains, with a 100-person company doing the work of a 1,000-person one, is not a uniform benefit across the sector. It is a capability multiplier. And what it multiplies matters enormously.

Some companies in this sector are being structurally disintermediated. Media brands whose primary value was information access. Marketing services businesses whose primary value was human execution of repeatable tasks. Research businesses whose primary value was data aggregation rather than actionable intelligence. For these companies, AI does not create a rescue opportunity. It accelerates the pressure.

The companies that can use this moment to break out share three characteristics.

One: A defensible proprietary data asset. Not licensed data. Not aggregated public data. Behavioral data, transaction data, intent data that comes from a genuine and exclusive relationship with an audience or customer base.

Two: A trusted audience or customer relationship. The kind of relationship where the brand is the reason people show up, not just the container for the information.

Three: A leadership team willing to pursue genuine business model transformation. Not an efficiency overlay on a declining model. A fundamental rethinking of what the business is and what it sells.

The efficiency overlay is the more common response because it is easier to underwrite, easier to model, and easier to defend in a board meeting. It is also insufficient. The B2B media and information companies that navigate this period successfully will be the ones that use AI not to do the same thing cheaper, but to do something fundamentally more valuable: moving from content delivery to intelligence execution, from audience access to workflow integration, from quarterly reports to continuously updated decision support.

The Timeline Reality

The Anthropic paper’s capability trajectory has a direct implication for investment timelines that most sponsors are not explicitly addressing. If AI systems can complete tasks that take humans days by the end of 2025, and tasks that take humans weeks in 2027, then the window for business model transformation is not a three-to-five year horizon. It is compressed. Companies that begin genuine transformation now have a meaningful advantage over those that begin in 18 months. Not because the technology will be unavailable, but because the organizational learning curve, the product development cycle, and the audience trust that makes intelligence products valuable all take time to build regardless of how capable the underlying AI systems become.

The timeline reality for PE sponsors is specific and needs to be addressed. Efficiency gains show up in 6 to 12 months. Revenue model transformation is an 18-to-36-month story. Most fund structures were not built around that gap. The result is pressure to declare transformation victories on an efficiency story that acquirers will correctly identify as incomplete. That misalignment is a risk factor in its own right. Sponsors and portfolio company leadership need to have an explicit conversation about whether the fund structure can actually support genuine transformation. Most are not having it.

What AI-Native Leadership Actually Looks Like in Diligence

The standard diligence question about AI readiness: “how are you using AI in your business”, produces answers that are almost universally unreliable, because every management team has a version of the answer that sounds good and very few have the substance behind it.

Better questions:

What proprietary data does this business own that gets more valuable as AI systems get better at synthesizing it?

What is the gap between the intelligence this business could produce from its data and the intelligence it is currently producing?

Has the leadership team demonstrated the ability to move through ambiguity and make structural decisions rather than incremental ones?

And specifically: who on this team has led through a prior technology disruption cycle at the operator level, not the advisory level?

That last question matters. The Anthropic paper is clear that the human role that persists, even as AI takes over the execution of complex tasks, is judgment about which problems are worth solving. That judgment, in a business context, comes from having made hard calls under real pressure, with real consequences. It cannot be learned from a course or acquired by reading white papers. The difference between leaders who have run businesses through disruption and leaders who have studied businesses through disruption is significant, and it is not always visible in a management presentation.

The Vertical Business Intelligence Window

For small and mid-sized B2B media and information companies, the Anthropic paper describes something specific and time-bounded: the infrastructure cost of building a sophisticated intelligence product has collapsed.

The paper documents that Anthropic engineers are now using AI to complete work that would have taken humans years. Not through artificial intelligence as a concept, but through AI agents executing complex, multi-step workflows autonomously over hours or days. The same capability that is transforming software development at Anthropic is available to a focused B2B media company building a vertical intelligence product today.

Five years ago, building a genuine intelligence product on top of a vertical data asset required a data science team, an engineering team, and capital that most B2B media companies did not have. Today, a focused team of two or three people can build and deploy an agentic pipeline that ingests, synthesizes, and surfaces vertical market intelligence at a level of sophistication that would have required an enterprise analytics operation in 2020. The capability documented in the Anthropic paper is not confined to Anthropic. It is available now, at a cost structure that has never existed before.

This matters because the moat in B2B media has always been the proprietary relationship with the audience: the behavioral signals, the transaction data, the intent signals that come from a community that trusts the brand. What was missing was the productization capability. Agentic AI closes that gap. The companies that recognize this and move are positioned to make the long-predicted but never-quite-arrived shift from advertising revenue to audience intelligence revenue. The companies that treat AI as a content production efficiency tool are going to miss the more consequential opportunity.

The window is open. The Anthropic paper makes clear it will not remain open indefinitely. The large players are moving, the capability is diffusing, and the organizations that build institutional AI experience now are accumulating a compounding advantage over those that wait. The advantage small and focused B2B media companies have right now is speed, domain specificity, and audience trust. Those advantages are real and durable, but they require action to be realized.

What to Do Now

If you are a CEO:

Have the honest internal conversation before the board forces it. Assess your own AI fluency with the same rigor you would apply to assessing any other capability gap in your organization. Begin the four-constituency communication sequence now, starting with your employees. They need to hear the honest version from you before they read the trade media version. And make the organizational decision about AI leadership explicit: who in your organization owns this transformation, with real authority and real accountability, not as an addition to an existing role but as a primary responsibility.

If you are an operator:

Audit your product roadmap through the lens of a changed development constraint. The 8x productivity figure from the Anthropic paper is a leading indicator of where this goes, not a ceiling. Identify your implementation revenue exposure and model the unit economics of faster time-to-value as a strategic alternative. Inventory your proprietary data assets, genuinely, not aspirationally, and build toward the intelligence product your vertical market will pay for. Protect the domain expertise in your organization as the strategic asset it is.

If you are an investor:

Apply the three-characteristic filter to every portfolio company: proprietary data asset, trusted audience relationship, leadership capable of genuine transformation. Have the explicit conversation with each portfolio company about transformation timeline versus fund timeline, and be honest about the misalignment where it exists. Build an AI-native leadership evaluation framework before your next diligence process, because the standard questions are producing unreliable answers. The capability trajectory in the Anthropic paper compresses your planning horizon. Act accordingly.

The Path Forward

I came out the other side of the Internet disruption a better leader, a sharper operator, and far more clear-eyed about what the job actually requires.

What I know now is that the leaders who navigate disruption well share a common characteristic. They do not wait for clarity before they act. They move toward the uncertainty rather than away from it, they stay honest with the people depending on them, and they build the organizational capacity to learn and correct in real time rather than the organizational theater of having all the answers.

The Anthropic white paper is telling you, in hard data, that the ground is shifting. The question is not whether this disruption is real. The paper answers that. The question is what kind of leader you are going to be in response to it.

That is the most important decision of all. And it is entirely yours to make.

The Anthropic Institute white paper “When AI Builds Itself” is available at anthropic.com/institute/recursive-self-improvement.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.

Last week’s post, “The AI Layoff Myth" generated a ton of discussion: on the blog, on LinkedIn, and in private conversations with operators and executives across the industry. That response pointed directly to the question I'm being asked most in my advisory practice right now. This post is my answer, and the framework I've built around it.

“How do we move?”

That question comes up in nearly every conversation I have with founders, CEOs, executives and investors in B2B media, marketing and technology right now. Agentic AI is the context. The answers cluster into two camps.

The first camp has committed. They are mandating adoption, setting deadlines, measuring usage. The second camp is interested but cautious. They are watching, waiting, gathering information before making a move.

Both camps are making the same mistake.

The Phase Problem

In 1962, a sociologist named Everett Rogers published “Diffusion of Innovations”, a landmark study of how technologies actually move through organizations. Rogers wasn’t interested in which companies adopted first. He was interested in why adoptions fail.

His finding: they fail because leaders misidentify which phase they’re in and manage accordingly.

Rogers mapped five phases every meaningful technology moves through inside an organization: knowledge, persuasion, decision, implementation and confirmation. Each phase requires different leadership behavior. Skip one, and the implementation collapses, regardless of how good the technology is or how much budget is behind it.

The mandate camp is running implementation plays before their organizations have completed persuasion. Compliance isn’t adoption. People follow mandates and wait them out.

The wait-and-see camp has convinced themselves that accumulating knowledge is the same as strategy. It isn’t. Knowledge without structured experimentation is just informed inaction.

Additive Versus Substitutive

Every prior technology wave B2B executives have navigated was additive. Cloud made existing infrastructure cheaper. SaaS made existing workflows faster. Programmatic made existing media buys more efficient. Mobile extended existing experiences to a new screen.

Additive technologies reward the fast follower. You can watch others absorb the implementation costs, learn from their mistakes and move when the path is clearer. The workflows you’re augmenting will still be there when you arrive.

Agentic AI is substitutive. It doesn’t augment workflows. It replaces them. The research workflow. The content production workflow. The lead qualification workflow. The procurement analysis workflow. These are not getting faster or cheaper. They are being rebuilt from different starting assumptions.

In a substitutive cycle, the fast follower strategy breaks down. By the time the wait-and-see executive is ready to move, the workflow they intended to augment no longer exists in its prior form. The organizational muscle memory built around that workflow is a liability, not an asset.

This is what’s different. Not the technology itself, executives have managed technology transitions before. The substitutive nature of the shift is what changes the strategic calculus.

Trialability: The Argument Against Waiting

Rogers identified trialability as one of the strongest predictors of adoption velocity. The easier it is to experiment with a technology on a limited basis, before full commitment, the faster it moves through an organization.

Agentic AI has near-zero trial cost right now. That is the fact the wait-and-see camp is underweighting.

You do not need a platform decision, a vendor contract or a change management initiative to begin. Here is where to start:

* Pick one workflow. Not a strategy. One specific, bounded workflow that your team runs repeatedly: a weekly report, a competitive brief, a content outline, a prospect research summary. Scope matters. Broad experiments produce ambiguous results.

* Run it in parallel. Have your team complete the workflow the existing way and with an agentic tool simultaneously for 30 days. You are not replacing anything yet. You are generating comparable data.

* Measure what actually changes. Not sentiment. Time to completion, output quality as evaluated by the person who normally does the work, and error rate. These three metrics will tell you whether substitution is real in your specific context.

* Expand from evidence. One workflow becomes two. Two becomes a workflow audit. A workflow audit becomes a genuine adoption roadmap: phase-aware, sequenced, grounded in your organization’s actual experience rather than vendor promises or competitor anxiety.

This is not a technology project. It is an operational experiment. The cost of running it is minimal. The cost of not running it compounds.

The Real Question

How do we move? The answer isn’t mandate and it isn’t wait.

Rogers spent a career documenting what actually works: structured experimentation, phase-aware leadership and honest measurement. The organizations that navigate this transition well won’t be the ones that moved fastest or most cautiously. They’ll be the ones that moved deliberately, and started sooner than felt comfortable.

The workflow you’re waiting to augment is already being rebuilt somewhere else.

The views expressed in Uphoff on Media are entirely my own. They don’t represent the opinions of any company I’ve led, any board I’ve sat on, or any investor who’s had the pleasure of debating strategy with me over the years. If something I write here sounds brilliant, I’ll take full credit. If it turns out to be wrong, I was clearly misquoted by myself.