Jensen's Polite Warning: The Coming VARification of Software

Summary

• Jensen Huang's reassurance that software companies will become "value-added resellers of AI tokens" is actually a polite death sentence, implying a 90%+ valuation compression from SaaS multiples to VAR multiples.
• With AI collapsing both the cost of building software and the cost of maintaining it, the last structural defense of "buy, don't build" evaporates — which is why Kirkland & Ellis spending $500 million on internal AI is existential strategy, not merely a productivity initiative.
• Foundation model labs are taking over the high-margin "cerebrum" of the software value chain, pushing software vendors down to the coordination-layer "cerebellum" and toward the low-margin, relationship-heavy work that vendors themselves once pushed onto VARs and systems integrators.
• The radiology precedent suggests AI will reshape and expand the legal profession rather than destroy it: automation makes the routine work cheap, demand grows as prices fall, and human judgment becomes the scarce, premium input — but only for firms that make the transition.
• The deeper cause is the harness flywheel: model labs design their products and models together as one system, while app companies build workarounds for model limitations that the next model release makes obsolete — leaving app companies three options: post-train their own models, climb into the model layer, or accept VAR economics.

Jensen's Polite Warning

Jensen Huang is a nice guy — so nice that he seems constitutionally incapable of saying harsh things in public. So when he addresses the future of the software industry and the SaaS depression of the past few years, his way of calming people down is subtle. More software engineers will be hired, he says. The software industry is here to stay as the plumbing layer. Software companies will become value-added resellers — VARs — of AI tokens.

Read that last sentence again. It sounds reassuring. It is actually one of the most brutal things anyone has said about the software industry, delivered with a smile. VARs and SIs trade at 10-15x P/E and 0.5-1.5x EV/S. SaaS companies have historically traded at 15-20x EV/S. If Jensen is right, he is politely announcing a 90%+ valuation compression for an entire industry. Amusingly, some SaaS companies like Wix already trade closer to VAR multiples than to classic SaaS multiples. The market may be sniffing this out before the narrative catches up.

The Kirkland Disagreement

I've been chewing on this since listening to Factory AI's founder comment on Kirkland & Ellis reportedly spending $500 million to build AI internally rather than relying on legal AI startups like Harvey. His argument, roughly:

"We're so used to a world where the moat in software was: 'I know how to do this and you don't, so you're going to pay me because I have the engineers who know how to build this and you simply cannot.' Going forward, there is going to be nothing that no one can build. Every single piece of software, in theory, anyone will be able to build. But then it comes back to resource allocation. Is it worth your time and energy to build it, or should you go to someone who has already built it? I could go pick up lunch for everyone on the team. I know how to do it. But just because I know how to do it doesn't mean it's an efficient use of my time. Just because you can build a lot of these things does not mean you should. If something is not relevant to your core business or core competencies, outsource it."

The lunch analogy is clean, and it's wrong — or rather, it misses the big picture from Kirkland's perspective. For Kirkland, this is not lunch. This is not a resource-allocation optimization at the margin. This is a project they must execute, because the alternative is becoming the Blockbuster that gets steamrolled by Netflix. They know it's not their specialty. They know they have a low probability of delivering a solution as polished as a dedicated startup's. They must do it anyway, because in the world that's coming, the AI stack is the core competency of a law firm. Getting lunch was never going to become the core of Factory's business. Legal AI is absolutely going to become the core of Kirkland's.

The deeper point the lunch analogy obscures: when "core competency" itself is being redefined by technology, outsourcing your future is not efficiency. It's surrender.

The Maintain-vs-Buy Objection — and Why It's Dying

The strongest counterargument to internal builds has never really been about building. It's about maintaining. The old rule of thumb in IT was that for every dollar spent building software, you spend three dollars maintaining it — patching, securing, upgrading, complying, fixing the integration that broke when a vendor changed an API. Kirkland's $500 million is not a one-time cost; under traditional economics, it's a down payment on a permanent tax.

But this objection assumes AI can build and not maintain, which gets the difficulty ordering backwards. Building is the harder problem — it requires understanding ambiguous requirements, making architectural decisions, and creating something from nothing. Maintenance is largely boring, repetitive, well-specified work: dependency upgrades, security patches, regression fixes, compliance updates. AI agents are spectacular at boring, well-specified work. It is precisely the kind of task where agents already outperform bored human engineers who consider maintenance career poison.

So the economics don't shift from $1-build/$3-maintain to $0.01-build/$3-maintain. They shift toward $0.01-build/$0.01-maintain. And once maintenance collapses alongside construction, the last structural defense of "buy, don't build" collapses with it. The total cost of ownership argument that justified an entire industry's existence quietly evaporates.

What SaaS Actually Was

To see where this goes, it helps to be honest about what the software business actually was. In the pre-AI era, software vendors occupied the most enviable position in the entire technology value chain. They had the highest operating leverage: build once, sell infinitely, at near-zero marginal cost. They built standardized products serving 90%+ of customer demand, and for the messy tail — the customer who needed the weird integration, the on-site deployment, the bespoke workflow — they had two answers: adapt to our product, or here's the number of a VAR or systems integrator who will hire armies of labor to do the non-scalable dirty work.

Vendors kept the high margins, the scalability, and the distance from tricky customer relationships. VARs and SIs got the residual: low-margin, labor-intensive, relationship-heavy, unscalable work. It's the same structure as Apple keeping the high-value design and brand while Foxconn runs the assembly lines. In this value chain, software vendors were the brain; VARs and SIs were the limbs.

VARification: The Brain Gets a New Tenant

The brain has a new occupant. Foundation model labs have taken the cerebrum — the center of cognition and the high-value core of the software value chain.

What remains for software vendors is the cerebellum: the coordination layer. The cerebellum matters; without it you lose balance and fine control. But it is not where thinking happens. As models improve, the cerebrum expands while the cerebellum is pushed outward — toward the limbs of integration, configuration, hand-holding, and relationship management.

This is the VARification of software. Model labs inherit what software vendors once enjoyed: standardized products, extreme operating leverage, high margins, and distance from the messy end customer. Software vendors inherit what VARs used to have: fierce competition over a commoditizing offering, deal-by-deal wins, tailored low-margin services, and heavy human touch.

There is a dark irony in the common claim that “AI won’t replace jobs involving human relationships and negotiation.” That statement is true — and it is precisely the low-margin territory that most AI application businesses (the companies building on top of the models) will be forced to occupy. Human touch is where the margin isn’t.

The China Precedent: SaaS Was a Local Maximum

Maybe it should have been this way all along. The premium on software businesses in the West arguably existed for one simple reason: software engineering talent was scarce and expensive. China shows what the equilibrium looks like when that constraint is removed.

China has essentially no enterprise SaaS industry. Most enterprise demand is addressed by internally built software stacks tailored to each company. Three reasons: China has a K-shaped company-size structure where value accrues to ultra-large state-owned enterprises and internet giants, with little room for the mid-sized companies that are SaaS's natural customers; there is an ample supply of highly capable, comparatively cheap software engineers; and big customers demand deeply customized stacks that are ruinously unprofitable for standardized SaaS vendors to serve.

The uncomfortable conclusion: the golden era of licensed software and then SaaS was a local maximum — a monetization of a specifically American market structure with abundant mid-sized enterprises and scarce, expensive engineering talent. AI removes the scarcity. Every company on Earth is about to have access to what Chinese giants have had for a decade: effectively unlimited, low-cost engineering capacity via AI agents. The Chinese equilibrium — tailored, internal, no external software premium — may simply be the global equilibrium, arriving late to the West.

There is an important caveat. China’s model produced heavy duplication, uneven quality, and zero global enterprise software champions. The reason is fundamental: human engineering capacity — even when abundant and cheap — does not compound or standardize well. Every company reinvents similar systems, quality varies by team, and little reusable infrastructure emerges. AI agents behave differently. Once built, they compound, improve, and can be reused across many organizations at near-zero marginal cost. The AI-powered version of the Chinese model therefore keeps the deep customization that large customers demand while eliminating most of the waste and duplication that human-built internal software creates.

Professional Services: The Legal Deep Dive

Professional services — law firms, consultancies, investment banks, accounting firms — are where this collision gets most interesting, because they run on pyramid structures: many juniors, fewer mid-level managers, few senior partners.

If AI automates junior work, the naive prediction is “fewer juniors.” But outsiders consistently misunderstand what junior professionals actually do. Junior work is not just research, drafting, analysis, document review, client communication, and internal coordination. It is also the apprenticeship system through which the firm builds and transfers its knowhow to the next generation of mid-level and senior professionals. That on-the-job learning is how the firm reproduces its own expertise over time. Automating this layer therefore does more than shrink headcount — it marginalizes, and in extreme cases can destroy, the very mechanism that creates the firm’s future knowhow, while simultaneously reshaping leverage, margins, and firm economics.

For law firms specifically, trace the trajectory. Previously, software was marginal to legal work — document search, case databases. Limbs. Lawyers were the brain. A copilot-stage legal AI first lets firms keep middle and senior staff while shedding junior labor. But as legal AI passes the agent stage and climbs in expertise, middle and senior functions get absorbed too. And here's the trap: if every law firm has access to the same legal AI, then legal service itself commoditizes, and the firm's remaining differentiation collapses to landing deals, building trust, and maintaining relationships through unscalable human means — while the AI does the substance.

At which point the client asks the fatal question: why go through the law firm at all, rather than directly to the AI provider whose agent the firm is using? If most legal tasks can be served by a standardized software product, law firms become — there's that word again — VARs and SIs of legal intelligence, rather than premium, high-margin, prestigious service providers. Humans stay in the loop for responsibility and judgment. But the brain-space allocation inverts: legal agents grow both the limbs and the majority of the brain, with human lawyers retaining a minority stake for accountability, subjective evaluation, and final judgment.

This is exactly why Kirkland is spending the $500 million. It's not IT procurement. It's a fight over which side of the VARification line they end up on.

The Radiologist Lesson

Before concluding that lawyers are doomed, consider radiology — the most instructive failed prediction in AI history. In 2016, some of the most prominent voices in AI declared radiologists obsolete. The logic seemed airtight: deep learning matched or beat humans at reading medical images. Why keep paying the human?

A decade later, radiologists are in higher demand than ever, with shortages rather than gluts. Three reasons, each mapping directly onto law.

First, the job was never just "look at image, find thing." A radiologist integrates images with patient history, correlates across studies, communicates with referring physicians, weighs ambiguity, and signs their name to a diagnosis carrying real liability. AI automated the most legible slice of the work, not the job. Likewise, a lawyer's value was never merely "find the clause" — it's judgment, accountability, and contextual synthesis sitting on top.

Second, Jevons paradox: make a component of a service cheaper and total demand often expands. Cheaper imaging meant more scans ordered, meaning more reads requiring a responsible human signature. If legal work that cost $10,000 can be delivered for a few hundred, vast latent demand — small businesses, individuals, underserved markets that could never afford lawyers — becomes economically viable. The market grows.

Third, the human moved up the value chain. Do the arithmetic: if volume grows from 100 cases to 100,000, and humans handle only the final 10% of last-mile judgment, that's 10,000 cases of genuinely human work — two orders of magnitude more than before — and each unit commands a premium precisely because it's the scarce, non-commoditized input.

The lesson is not "the machine replaced the expert." It's that automation unbundles a profession into a commoditized layer and a judgment layer, collapses the price of the former, and amplifies the volume and value of the latter. Honest caveats: the transition is uneven, practitioners who refuse to move up will be displaced, and the timing of demand expansion is uncertain. But structurally, legal agents are more likely to reshape and expand the legal profession than extinguish it — for the firms that make the transition.

Three Objections, Addressed

Regulation and licensing. Unauthorized-practice-of-law rules, malpractice insurance, and bar regulations legally prevent clients from "going directly to the AI" in many cases, and could preserve law firm economics longer than technology alone suggests. Possibly. But we have reasonable faith in the US legal system's historical willingness to accommodate innovation — and more importantly, there's now a forcing function: China. Chinese law firms already operate at incredibly low margins with none of the premium American firms enjoy — the same dynamic as Chinese software engineering. If China moves first on AI-native legal services, competitive pressure will drag the US along, because the alternative is watching an entire professional-services export advantage erode. Regulation buys time. It doesn't change the destination.

The apprenticeship problem. This is the big one, and I won't pretend it away: if juniors are automated out, where do future senior partners with judgment come from? Judgment is traditionally trained through exactly the grunt work AI is eliminating. Honestly, nobody knows how this resolves. The likely answer: new juniors collaborate with agents from day one, and the career ladder bifurcates brutally — either you learn fast and jump to the rank of managing agents and exercising judgment early, or you stay in low-level tasks and get smoothed out entirely. The comfortable middle rungs of the ladder disappear. Darkly funny footnote: this is roughly what already happened to junior white-collar workers in China's hyper-competitive labor market. The pyramid becomes an hourglass, then a diamond, then just the top.

The proprietary data moat. The fashionable rebuttal is that incumbents like Kirkland hold decades of privileged deal documents no lab can access, and this data is the real moat. Data matters. But I think this is currently the single most mispriced belief in the market. People systematically overvalue incumbent data and undervalue newcomer ingenuity. We have a live experiment: GitHub and VS Code sat on the largest repository of code and developer behavior data in human history, plus total distribution — and Claude Code, with none of that, ran straight past Copilot on the strength of a clean-slate, agent-native design. Incumbents have data; disruptors have the clean slate. History says bet the clean slate. Data is a moat only when the game stays the same; when the game changes, data about the old game is inventory, not moat.

From Seats to Outcomes: Who Bears the Risk

The endpoint of all this is that "Software as a Service" finally becomes literal — not service in the deployment sense (subscription instead of license) but in the outcome sense. You don't buy seats of legal software; you buy resolved contracts. You don't buy a support platform; you buy resolved tickets.

The obvious objection: outcome pricing means the provider bears the risk when outcomes fail, and enterprises are conservative about risk transfer. True — adoption will take time for exactly this reason. But we've run this movie before. The cloud transition was itself a massive risk transfer: infrastructure and operational responsibility moved from the customer to AWS and Azure, and customers who initially found this unthinkable ("our data, on someone else's computers?") eventually found it unremarkable. Outcome-based AI is the continuation of the same risk-offload continuum — higher stakes, yes, perhaps crossing thresholds today's buyers aren't comfortable with. But we'll get there, because there's no alternative: once one provider credibly prices on outcomes, seat-based competitors look like restaurants charging you for the kitchen instead of the meal.

Note what outcome pricing does to the VAR analogy: it either breaks it or completes it. The provider who successfully bears outcome risk at scale captures service-industry TAM at software-industry economics. The one who can't is just a VAR with extra liability.

What Happens to the Actual VARs and SIs?

If software vendors become VARs, what happens to Accenture, Infosys, and the Big Four advisory arms — the actual VARs? The ironic answer: the VARs get VARified too. They face a pincer. From above, newly VARified software vendors descend into their territory — and those vendors understand harness engineering far better than traditional SIs do.

From below, AI collapses the billable-hours labor arbitrage that is the entire SI business model. To see why this is fatal, it helps to understand how these firms actually make money. The traditional SI and consulting model runs on labor arbitrage: the firm hires large numbers of relatively cheap junior and mid-level staff — often in lower-cost locations — and then bills clients at a much higher hourly or daily rate for their time. The spread between what the firm pays its people and what it charges the client is the arbitrage, and it scales with the number of billable hours the firm can put on an engagement.

AI attacks this spread directly. When agents can perform large parts of the analysis, coding, documentation, testing, and configuration work that used to demand armies of billable humans, the volume of hours that can be billed shrinks sharply. Fewer hours means a smaller base for the arbitrage, and the profitable gap between cheap labor and expensive client billing collapses. The business model doesn't just get squeezed — its central engine loses fuel. Value-add and margins, already thin, compress further from both directions at once.

Their surviving asset is relationships — and relationships are real. But this is the incumbent-data story again: old guards holding relationships versus newcomers offering vastly better value. Newcomers win most of the time. Relationships delay the rollover; they rarely prevent it.

Why Incumbents Can't Just Adapt: The Organizational Argument

Everything above says software companies must move up the stack — toward the model layer, or at least toward owning part of the cerebrum. Here's why almost none of them will manage it.

Look at what's separating winners from losers at the frontier right now. Anthropic's surge past OpenAI, with Google and Meta lagging, traces to something unglamorous: technical founder retention and leadership continuity. Anthropic's technical co-founders stayed, caught the early RL-scaling signals in the Sonnet 3.5/3.7 coding era, moved decisively, and delivered the curve. OpenAI lost its technical founders, and without them at the table, high-conviction technical bets got harder; the company leaned on product and go-to-market — strong levers, but secondary while frontier progress remains the main game. Meanwhile scaling's reported death was exaggerated, and the labs that kept conviction and kept shipping lapped the doubters.

The lesson for the application layer follows directly. The winning AI software organism is a small, concentrated, visionary team with extreme talent density, willing to rewrite 80% of its codebase every six months as models evolve, where low-level technical insight gets spotted by senior leadership and becomes a company-wide priority within weeks. That organism is nearly the exact inverse of an incumbent software company. This is also why serious AI app startups must understand the model layer deeply — you cannot design a harness that fits the model, and evolve it as fast as the model evolves, from the outside. It's why "just train your own" is becoming real advice for ambitious verticals: take SOTA open-source bases and your proprietary data and RL post-training, and out-execute closed models on your domain — because if you build something exceptional in a big vertical without model leverage, the labs will verticalize right behind you.

Can incumbents respond? Only with something like Zuckerberg's play: a dedicated elite lab inside the larger org with complete independence and deliberately obnoxious prestige relative to everything around it. Most incumbents can't stomach that.

And the mindset gap is visible if you know where to look. Watching Wix's CEO on 20VC praise Figma's product craftsmanship — rather than grappling with what Claude Code represents (not well-crafted, moving insanely fast, incredibly ahead on utility) — my honest reaction was: this is a Japanese ICE executive praising the mechanical beauty of engine design while the future of the car becomes batteries, controllers, and motors. The kaizen mindset that made Japanese manufacturers great is the same one that left them behind in the internet era and the EV era. Craftsmanship optimizes within a paradigm. We're between paradigms. Future software winners will look temperamentally alien to current winners: hyper-agile, harness-obsessed, model-fluent, comfortable treating their own codebase as disposable.

There's precedent for how rarely this transition succeeds. IT services firms watched the software industry's superior margins for decades and almost never crossed over. Individual entrepreneurs made the jump — CrowdStrike's founder went from Big Four forensics to security software — but the Big Four themselves never became software vendors. Genes, focus, and know-how don't transfer. The same asymmetry now applies one layer up: software companies watching the model layer. They must attempt the climb, because not attempting it is the end of the valuation story. Most will fail anyway.

Scenarios and Signposts

Nobody can time this, but first-principles scenario analysis beats false precision.

In a fast-takeoff scenario — models keep scaling, agents reach senior-professional reliability within two or three years — VARification happens violently. SaaS multiples compress toward SI multiples within a market cycle, internal builds like Kirkland's proliferate across every industry, and the only defensible positions are frontier labs, compute, and the handful of app companies with genuine model-layer leverage.

In a capability-plateau scenario — models stall at today's agentic reliability — software vendors get extra years, not a pardon. Even current models make building and maintaining software cheap enough to undermine the case for buying it. Enterprise caution slows the shift, but the destination is the same.

The signposts to watch:

• Net revenue retention and seat counts at big SaaS vendors — seat-based models are the canary.
• Frequency of Kirkland-style nine-figure internal builds announced by non-tech enterprises.
• The first credible large-scale outcome-priced contracts.
• Open-source model proximity to the frontier — this determines whether labs or harness-builders capture value.
• SI revenue-per-employee — the first number to break when VARification cascades downward.

The Investor Takeaway

If this thesis is even directionally right, the sorting rule is simple: ask whether the cerebrum can absorb your function or merely needs it. Companies whose product is work the model can increasingly do itself — workflow orchestration, data entry and retrieval, the connective tissue between intent and execution — are being demoted to cerebellum status: necessary, commoditized, low-margin. Companies the cerebrum depends on but cannot replicate — compute beneath it, proprietary data loops beside it — capture value. Switching costs are the second-order adjustment: they set the timeline, not the outcome — high enough friction buys a doomed vendor years of bond-like cash flows, but it never moves them into the winning bucket.

Most exposed: horizontal SaaS whose product is a workflow wrapper around data entry and retrieval — tools where "an agent could do the workflow" is a sentence that parses. Seat-based pricing anywhere is a slow leak, because agents don't buy seats. Mid-tier point solutions with weak systems-of-record status get VARified first.

Temporarily protected: true systems of record — core ERP, core banking, EHR — where compliance, data gravity, and catastrophic switching risk buy real time. But protected is not the same as growing; these become bond-like cash flows, and the market will eventually price them that way.

Potentially escaping the trap: the small set of AI-native companies with genuine model-layer leverage — those doing serious post-training on proprietary interaction data, whose harness co-evolves with frontier models, and whose usage generates data that improves their models in a real loop. The test is not "uses AI" (everyone does) but "would a frontier lab verticalizing into this space start from behind?" Very few companies pass.

Clear beneficiaries: the labs themselves, compute and the infrastructure layer beneath them, and — the contrarian one — the consumers of software. Every enterprise on Earth is about to get its software bill structurally repriced downward. The value doesn't vanish; it transfers to customers and to the cerebrum.

Position accordingly: long the brain, long what feeds it — compute and the infrastructure stack beneath the labs — long the rare app-layer companies with genuine model leverage (proprietary data, real post-training and RL loops that frontier labs can't replicate from a standing start), long the balance sheets that buy software, and extremely skeptical of anything in between still trading like it's 2021.The software industry isn't dying. It's being demoted. Jensen already told us — he was just too nice to say it plainly.

Postscript: The Harness Flywheel — Why the Agent Era Still Belongs to the LLM

A recent observation from Moonshot AI's founder crystallizes the mechanism behind everything above — not just that model labs eat the application layer, but why the outcome is structurally predetermined.

The mechanism is a forward loop versus a backward loop. When a model lab builds an AI product, it designs the harness first, then trains the next-generation model to fit it. Harness and model co-evolve by design; every training run tightens the fit. A pure AI product company runs the same process in reverse: it receives a finished model as a black box, probes it to discover its limits, and constructs a harness around what it infers. The lab knows the model's true capability frontier because it drew that frontier; the app company is forever reverse-engineering it from the outside. One process is design, the other is archaeology — and the archaeologist is permanently behind, because the gap is in information access, not talent or effort. It compounds, too: because the lab designed the harness and trained the model into it, it understands the model's limits better than anyone, which lets it evolve the harness better than anyone, which informs the next training run. The loop feeds itself.

This is exactly why Anthropic rewriting 80% of its harness code every six months is not a symptom of chaos but the flywheel working as intended. Models evolve, so the harness must evolve — and only the party training the model knows which direction it will evolve before it ships. It also explains Claude's otherwise puzzling market position. There are two ways a model can reason. The first is monologue reasoning — one long, uninterrupted chain of thought before answering, the test-time-scaling paradigm other labs optimized for: a mathematician at a desk, deriving everything before touching anything. The second is agentic reasoning — short bursts of thought interleaved with action: think, run the code, read the error, revise, check, think again. A mechanic under the car. The two are not just different styles; they are trained differently. Monologue reasoning can be trained on static problems with verifiable answers — no harness required. Agentic reasoning can only be elicited, measured, and reinforced inside an environment where the model acts and observes consequences — inside a harness. The capability doesn't exist as a training target until you've built the loop. Claude was never the champion of raw monologue reasoning; Anthropic bet on the agentic kind instead — a bet that was only available to an organization building harness and model as one system. Anthropic didn't build a model and find agents inside it; it built for the agentic loop and trained the model into it. The forward loop, made visible.

Extend the ladder and the next rung comes into focus. Language models, chain-of-thought, and agents are consecutive rungs — knowledge, then reasoning, then action. But even the best agent today is amnesiac: it can act, but it cannot accumulate. Every session starts from the same frozen weights, which is why no agent yet replaces the employee whose real value is six months of absorbed context, not raw intelligence. The employee learns into their brain; the agent "learns" into a scratchpad. The next rung is closing that gap: persistent learning, perhaps eventually recursive self-learning — merging the monologue and agentic paradigms and, critically, writing what is learned into the model's weights rather than bolting it on as external memory. Note what that implies for the application layer. External memory, retrieval scaffolds, and context management are precisely the workarounds app companies sell today as their value-add. If the frontier's next move is to internalize learning, today's memory architecture is tomorrow's swallowed feature. Multimodality, search, and video generation matter for products, but they are pluggable components; the tasks that decide whether intelligence keeps advancing remain training, learning, and the model's own capability. The cerebrum, again.

None of this means pure-agent startups can't make money today. They can. Enterprises genuinely need permissions, audit trails, data access, and industry workflows, and decent software companies can be built on those needs. But their durable value comes from customer relationships, proprietary data, domain experience, and control over real-world work — the word "agent" itself confers no moat. Ingenious orchestration is, by definition, compensation for model weakness, and model weakness is a depreciating asset. As models improve, vast amounts of today's intricate orchestration get swallowed by a single model call, and many agent products that look brilliant right now will turn out to have had very short lifespans.

The formulation that closes this essay is simple: the harness decides what the model attempts; the model decides what it achieves. Orchestration routes intelligence — it cannot add it. When the agent era truly arrives, the winner will still be the LLM.

Which brings everything full circle. VARification is the industry-wide result; the flywheel is the cause, operating one company at a time. Every AI app company's product is, underneath everything, a bundle of workarounds for things the model can't yet do. The lab knows exactly which of those workarounds its next model will make unnecessary — it's training that model right now — and the app company finds out on release day. Each release deletes a chunk of someone's product. Run that squeeze across a thousand companies and the sum is VARification: the flywheel is not an abstraction beneath the thesis, it is the thesis, happening one product at a time. For AI application companies, the strategic menu collapses to the same three options identified earlier: post-train your own models, migrate up into the model layer outright, or accept — with clear eyes — a future of relationship-based, low-margin, VAR economics. And for enterprises like Kirkland, the flywheel is the final justification for the $500 million: if fit between harness and intelligence is where value lives, then owning your own loop — your data, your post-training, your evolving harness — is the only position that isn't someone else's flywheel.

For thirty years, software sold picks and shovels of the mind — because minds weren't for sale. Now they are, by the token. Everything else is distribution.