Loop Engineering: The Bridge to Workflow Transformation

Loop Engineering: The Bridge to Workflow Transformation

A few posts ago, I made the case that Vibe Coding [https://tonyuphoff.substack.com/p/vibe-coding]eliminates the queue: the wait for an engineer to build the thing you need. Last post, Solo Scale [https://tonyuphoff.substack.com/p/solo-scale-the-new-business-model] 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 [https://uphoffadvisory.com/]: a strategic advisory practice for founders, CEOs, and investors in B2B information, marketing, and technology. The businesses that drive business. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

Solo Scale: The New Business Model AI Just Made Possible

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 [https://uphoffadvisory.com/]: a strategic advisory practice for founders, CEOs, and investors in B2B information, marketing, and technology. The businesses that drive business. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

AI Isn’t a Tool. It’s Infrastructure

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. Thanks for reading Uphoff on Media! Subscribe for free to receive new posts and support my work. 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 [https://tonyuphoff.substack.com/p/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. [https://tonyuphoff.substack.com/p/your-b2b-marketing-career-in-the] 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 [https://uphoffadvisory.com/]: 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

“I Just Stumbled Across Your Profile and Thought We Should Connect”

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 [https://uphoffadvisory.com/]: a strategic advisory practice for founders, CEOs, and investors in B2B media, marketing, and technology. The businesses that drive business. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]

When Agentic AI Meets the Physical Economy

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. Thanks for reading Uphoff on Media! Subscribe for free to receive new posts and support my work. 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 [https://uphoffadvisory.com/]: 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. This is a public episode. If you would like to discuss this with other subscribers or get access to bonus episodes, visit tonyuphoff.substack.com [https://tonyuphoff.substack.com?utm_medium=podcast&utm_campaign=CTA_1]