The Model Is the Commodity. The Loop Is the IP.— Turning Microsoft CEO Nadella's "Hill Climbing Machine" From Words Into a Working Machine

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This post is an abstracted, partial excerpt of a business-validation idea sent to a certain company last week.

This essay takes its inspiration from the ideas in an essay published by Satya Nadella, and sets out a general approach built on Microsoft (Azure OpenAI / Azure AI Foundry). All references to Mr. Nadella and to Microsoft are based on publicly available information, and do not imply that the approach described here has been endorsed by either party.

Models will become a commodity. So where, then, does a company's IP reside?

— Turning Satya Nadella's thesis into a "working machine" on top of Microsoft

In June 2026, Microsoft CEO Satya Nadella published an essay. Its gist runs as follows. AI models absorb expertise and rapidly commoditize. A company's true competitive edge, therefore, does not lie in "choosing the best model." Value resides in the learning loop you build on top of the model — a mechanism that improves the more you use it, compounding the company's own tacit knowledge over time. He calls this a "hill climbing machine." What is decisive is sovereignty over IP. The expertise inscribed into the learning loop is not lost even when the underlying model is swapped out. The priority, then, is not a single most-powerful model but a "frontier ecosystem, not just a frontier model" — an arrangement in which each company can own a learning loop that encodes its own knowledge. In his words, "a frontier without an ecosystem is not stable."

Most executives nod along when they hear this thesis. But the moment they nod, a question arises. If "the learning loop is the company's IP," then where, and how, do you stand it up first?

This article is one concrete answer to that question. To state the conclusion up front — leave the model to Microsoft, and own the learning loop yourself. And stand it up, and measure it, before anyone else, on top of your own single most important process: going to market (GTM). Before selling it to the outside world as a product, make it work for yourself first. This is the idea of applying dogfooding to the AI learning loop itself.

And there is a deeper claim in this article still. This division of labor — "the model amplifies, the learning loop is owned" — is not merely a matter of technology selection. It is the very "configuration that ought to be" between a company and its platform provider in the age of AI; and that configuration turns out to be astonishingly identical to a structure that excellent sales organizations have unconsciously embodied for a very long time. In the later sections (Chapters 2 and 3), we dissect this "identical form." That is the intellectual heart of this essay.

1. Why "your own GTM"?

For many companies, the place where AI's return on investment is most doubted is the gap between "flashy demos" and "real results." Investors and the front line share the same suspicion — does the AI that looks revolutionary on a slide actually move the quarterly numbers? This suspicion hangs over the whole industry like a fog, and lining up any number of individual success stories will not lift it. To lift the fog, you have to show that it works in a place where everyone agrees it cannot be faked.

That is exactly why the proof should be carried out in your most important, and most unglamorous, process. Inside sales is the archetype. Why does the "most unglamorous" place make the strongest proof? The reason is simple: the numbers in sales do not allow self-deception. A product demo can be staged. Internal productivity metrics leave room for interpretation. But whether "the deal moved forward" or whether "the other party opened up on first contact" is decided by another person, and it does not bend to your own wishes. The most heteronomous place, the place least susceptible to manipulation — if it works there, it is real.

Inside sales has one structural weakness, and only one — it does not know the prospect's "true problem" before making contact. That preparation takes a long time and that first-response rates are low are merely symptoms. The root is the "information asymmetry before contact." The salesperson picks up the phone and writes the email without knowing what the other party is struggling with. So they pour enormous time into preparation (Symptom A: the problem of quantity), and even then they miss the mark and response rates do not rise (Symptom B: the problem of quality). These two symptoms look, at first glance, like different things — one is "busyness," the other is "thin results." But they share a single root. One single absence — not knowing the other party's problem before contact — wounds both quantity and quality at the same time. Solve the root, therefore, and both improve at once. This is a kind of improvement that symptomatic treatment can never reach.

And above all — insert a learning loop here, and you can demonstrate, on your own front line and before any external product, an "organization that grows smarter the more it is used." The sales floor is also an ideal laboratory for an AI learning loop. Every day, large numbers of touchpoints are created; every time, the other party — the teacher — returns the signal "hit" or "miss." No other business process in the company yields feedback this fast and this honest.

2. The sales master and the LLM have the same "shape" — why tacit knowledge can be "distributed"

This is the most important argument in the essay. It may look like a detour, but this chapter is the foundation for every design choice that follows.

2.1 The true nature of the veteran salesperson's "intuition"

Among excellent salespeople there are those often described as "having good intuition." The moment a customer lets slip a single phrase — "cost is the issue for us" — that person instantly conjures up the countless contexts that might lie behind it: "Is observability cost ballooning under usage-based billing?" "Are recruitment and retraining costs rising because veterans have left?" "Did procurement cost shift with exchange rates?" "Is it personnel cost for audit response?" The same single word, "cost," points to entirely different pain depending on industry, role, and timing. The master, around that one word, instantly raises a constellation of possible pains.

This power has long refused to be put into words, treated as "experience," "sense," or "an individual gift." Something the new hire can only steal by watching the veteran's back for years — that is what it was believed to be. But is that really so?

2.2 The master's intuition and the LLM's inference are structurally isomorphic

Now, recall what a large language model (LLM) does internally. Given a word, the LLM unfolds, all at once, the countless neighboring possibilities within the high-dimensional semantic space in which that word is embedded. To the input "cost," it spreads — probabilistically — the distribution of contexts in which that word might appear. This is, structurally, exactly the same operation as the veteran salesperson raising a constellation of pains from the single word "cost."

Meaning does not reside in the word itself. It resides in the configuration of words — which word is placed around which other word, and how. The meaning of the single word "cost" changes wholesale depending on whether "usage-based billing" is seated beside it or "veteran attrition" is. What the veteran salesperson was unconsciously doing was this operation of "reading the configuration." And what the LLM excels at is precisely this operation of "unfolding meaning from configuration."

Once you notice this isomorphism, a long-standing received wisdom flips over. The reason the tacit knowledge of sales "could not be distributed" was not that it was mystical. It was that the device that unfolds it — human mastery — existed only inside each individual's head and could not be extracted to the outside. But now we have, in hand, a machine of "the same shape" as that unfolding device: the LLM. If so, then tacit knowledge can be distributed — because the power to unfold can be placed outside the human head.

2.3 What "being distributable" means technically

Concretely, it works like this. Starting from a single concrete case (a "fictional scenario" processed from a real success story with proper nouns hidden), have the LLM unfold "around a company with this pain, what constellation of words might exist." What you then get, externalized from the very start, is the "bundle of possible pains" that the veteran accumulated in their head over many years. The new hire goes to the customer carrying that bundle (= the fictional scenario). Instead of asking from a blank page "what is your problem?", they can present a concrete constellation: "isn't it a situation like this?"

A person on their very first day can do, from day one, the same work of "reading the configuration" that the veteran does. Because they no longer need to hold the power to unfold the configuration within their own experience. The LLM — a machine of the same shape as the veteran's intuition — has unfolded it for them in advance. "Distributing winning judgment from a handful of masters to everyone" refers to this technical fact. It is not a metaphor. It is precisely because the sales intuition and the LLM are isomorphic that you can distribute intuition with the LLM — that is the exact content of the claim.

And this is not "automation" that replaces people. If anything, the opposite. By fixing the master's intuition into a library, the master becomes not "a worker at the end of the line" but "a catalyst that ignites tacit knowledge." The new hire becomes not "a robot reading a script" but an "author" who grows the system through their own corrections. A winning style that was personal and idiosyncratic opens out into a mechanism — without diminishing the people.

3. The core mechanism — a closed loop that makes "correction" the learning source

In Chapter 2 we saw that sales intuition is "the unfolding of configuration," and that it is isomorphic to the LLM. So how do you converge that unfolded constellation onto the other party's own true problem? Here, a single reversal at the heart of the design comes into play.

3.1 Why you do not ask

The heart of the design is a single reversal. You do not directly ask "what is your problem?" Most prospects do not arrive in a state where they can articulate their true problem neatly. People cannot always lucidly describe what they are struggling with. If anything, the more someone is in the midst of pain, the less they can put it into words. So the more you ask via a questionnaire, the thinner the answers you get back. All that comes back are generic phrases that apply to anyone — and therefore help no one — like "cost reduction" or "operational efficiency."

Instead, you put forward first a hypothesis — "are you not placed in a situation like this?" — and have the other party correct it. You present the "constellation of pains" unfolded in Chapter 2, in the form of a concrete scenario: "For a company in your position, isn't it the case that observability cost is ballooning under usage-based billing, and that it's also hard to forecast?"

There is, in this, wisdom that exploits an asymmetry of human cognition. People find it hard to speak from a blank page, but they are good at fixing a view that is off. "No, cost is cost, but in our case it's not there — it's more about filling the hole left by the veteran who quit…" — it is in this moment of correction, "no, not there," that the true problem arises, in the other party's own words. The act of pointing out where the presented constellation is "off" leads the other party into their own true context.

3.2 Correction works twice

And correction is not merely a result of hearing-out. It becomes a teaching signal for the system. This is the crux of the closed loop.

Correction works in two places at once. First, on the spot. The moment the other party says "not there," you obtain their true problem in their own words. This alone dramatically raises the quality of first contact. Second, inside the system. That correction flows back into the library and raises the precision of the hypothesis you put forward next to a counterpart in the same industry and role. A single correction born on the front line raises the precision of the next hypothesis, and even raises the quality of the "first words" aimed at a prospect you have not yet met.

The more touchpoints you accumulate, the smarter the whole organization's questions become. This is precisely Mr. Nadella's hill climbing machine — a self-owned learning loop that grows more precise the more it is used, compounds tacit knowledge, and is not lost even when the underlying model is swapped out — run on the living front line of GTM.

Here the argument of Chapter 2 pays off. Correction can become a learning source because it is "the correction of a configuration." The other party is telling you "the position of this star, in this constellation, is wrong." That correction information tunes the constellation the LLM unfolds into something more accurate for that industry and role. The very process by which the sales master spent years polishing their own intuition is externalized in the form of correction and shared across the organization. The fruits of polishing that one veteran used to carry away on retirement now remain in the system and compound.

4. Architecture — translating "sovereignty over IP" directly into the design

This solution stands on top of Microsoft. But it does not merely "use Microsoft." It translates the very "configuration that ought to be," which Mr. Nadella himself preached, straight into the blueprint of the architecture. The roles divide cleanly into two layers.

4.1 The two-layer configuration

• The model layer = Microsoft (Azure OpenAI / Azure AI Foundry). It handles pain extraction, summarization, and the generation of hypothesis scenarios. The "unfolding of configuration" discussed in Chapter 2 — the inference that raises a constellation of pains from "cost" — is taken on by this layer. This is the "amplifier" role, and the model is kept interchangeable. If a better model appears, swap it in.

• The context layer = your own company (data, ingestion, retrieval, access control, the learning loop). It owns the customer's context and the learning loop that grows through correction. The IP remains here. The knowledge tuned by correction — "which constellation, in which industry, actually generates heat" — is precisely the firm-specific asset that is not lost even when the underlying model is swapped out.

Data and context always flow from your side to Microsoft's model, and the model is always grounded in your context. The model reasons only after dropping anchor in your data. This is a direct translation, into the architecture, of Mr. Nadella's ideas of sovereignty over IP and the frontier ecosystem.

4.2 Why this configuration is stable for both sides

Here I want to go one step further on the relationship with Microsoft. The relationship between a platform provider (a hyperscaler) and a company that runs its business on top of it inherently harbors one tension — the risk that the platform side itself absorbs the higher-level functionality. If the platform grows too smart, the companies that were creating value on top of it get swallowed and vanish. The reason so many companies fear depending deeply on an AI platform is this.

This configuration resolves that tension head-on. The company side draws a line: "We leave the model to Microsoft (= we do not compete at the model layer). But we own the learning loop (= we clearly hold a domain that cannot be swallowed)." Microsoft's side: "We capture value from the model layer and from the consumption of compute that runs on top of it (= we do not lay a hand on the company's learning loop)." A boundary that neither side encroaches upon is drawn explicitly.

And the decisive point is this — the one who drew this boundary line is none other than the CEO of the platform provider. Since Mr. Nadella publicly preaches that "each company should own its own learning loop" and that "it is a frontier ecosystem, not a frontier model," the platform side has no logic by which to oppose a company owning that learning loop with sovereignty. The other party's highest authority has, in advance, legitimized your assertion of sovereignty. As a negotiating structure, this is stable to a degree that is rarely seen. For the company, "collaboration rather than dependence" holds; for Microsoft, the more learning loops run on top, the more structurally compute consumption grows — the gains of both sides rest on a boundary that does not encroach but rather reinforces the other.

4.3 The entrance itself is a live demo

And what is beautiful is that the entry-point product experience itself becomes a live demo of this architecture. At the very first touchpoint, the prospect touches with their own hands the Microsoft integration actually running. They do not have it explained to them; they experience it. Your context layer supplies the context, and Microsoft's model layer reasons on top of it — that collaboration is, at the visitor's fingertips, actually running. The value of the alliance is proven not by a slide but by the working machine itself.

5. The discipline of measurement — "if it doesn't work, you'll know it doesn't work"

This approach merits trust because it is designed from the outset so that "failure is visible." In the field of management, the greatest reason AI proposals lose trust is not failure itself. It is that no one can tell whether it worked or not. Ambiguous success corrodes an organization's trust more than clear failure does. So this approach places, above all else, "being adjudicable" at the center of the design.

Run the pilot small and controlled. Few people, short period, single market. Do not claim statistical significance; treat it squarely as "an existence proof that the mechanism actually runs." Not over-promising here, paradoxically, builds trust. We do not say "across the whole market, sales will rise by this much" — because that can only be proven with market-level numbers. Instead, we go for the single verifiable point: "this mechanism is actually turning."

Judgment is made not on slow-settling outcome metrics (closed deals, pipeline conversion) but on fast metrics (the quality of contact, the correction rate, the improvement over time in hypothesis hit-rate). Why not judge on outcome metrics? Especially in the mature buying cultures discussed below, the result called "a closed deal" appears late, faintly, after consensus-building and deliberation. Wait for that and a short pilot tells you nothing. But the quality of contact — how deeply the other party spoke in their own words, how many corrections emerged — moves day by day, week by week. Without waiting for the verdict, you can see whether the loop is turning.

The core metric is one: "Is the 30th card hitting in conversations better than the 1st?" If this is rising, the loop is turning. If one correction makes the next hypothesis smarter, the hit-rate should rise with each iteration. That rising curve is the most direct evidence of "growing smarter the more it is used."

Here too, Mr. Nadella's thinking pays off. He preaches that the quality of a learning loop should be measured not against external benchmarks but against your own results. It is not "how do we compare with other companies." It is "are we smarter than we were yesterday." This approach, too, builds in — in minimal form — a private eval that measures whether the library is "improving the more it is used" using only your own records. Furthermore, separate the maker from the measurer — those with a stake in the result must not measure their own results. If the very person who built the mechanism also grades its performance, then however honest the human being, they cannot escape unconscious bias. So we structurally separate those who generate and operate from those who measure and evaluate. "If it doesn't work, you'll know it doesn't work" is a phrase that includes this control.

6. Why start from the "hardest market" — the hidden issue of translation cost

Counterintuitively, for the first pilot we choose the market that looks least likely to work. A market with a consensus-driven, mature buying culture in which buyers run themselves without going through sales (Japan, for instance, is the archetype).

Why deliberately choose the hardest? There are three layers of reasons, and all point to the same conclusion. And at the bottom of all three layers lies one issue that has rarely been confronted head-on — "translation cost." I want to treat this thickly in this chapter.

6.1 First layer — people can no longer solve it

That market's inside sales "can no longer be solved by human hands," owing to constraints of talent development and demographics. The very division-of-labor model of inside sales is new, experienced people are few, and you can only rely on training. Yet the talent that finally becomes skilled is poached away by transfers, promotions, and attrition — taking their tacit knowledge with them. And in the background, the working population itself structurally shrinks. The base for training and the supply of new entrants thin out at the same time. "Hire more, teach more" cannot keep up.

Here lies the first benefit of this approach. The mechanism seen in Chapters 2 and 3 accumulates tacit knowledge in a mechanism, not in people. So even when the skilled leave, their winning style remains. The fluidity of talent and the accumulation of organizational knowledge — two things long in trade-off — coexist for the first time. People may grow, move, and leave. The knowledge remains in the system.

6.2 Second layer — the invisible loss of "lost before you noticed"

In a consensus-driven, self-running buying culture, the final decision-maker is not necessarily the person you are in contact with; buyers enter defensive decision-making and prefer to advance their evaluation at their own pace without going through sales. In this cultural sphere, a first contact that breaks etiquette does not come back as rejection. If you are clearly told "no thank you," that is still better — there is a chance to learn. But in this market, a contact that misses the mark is quietly passed over, dropped from consideration without your ever knowing why. On the surface, nothing happens. But before you know it, you have vanished from the shortlist. "Lost before you noticed."

This loss is structurally invisible. Why you lost leaves no trace in the data. That is exactly why the etiquette of the first contact — the single phrase that slips under the buyer's defenses and opens the entrance to trust — becomes decisively important. And the design seen in Chapter 3 — "do not assert, put forward a hypothesis, invite correction" — is optimized precisely to pass through this defensive buyer's filter. Because it does not assert, it does not provoke wariness. Because it takes the buyer-led form of "fixing a presented view," it does not run counter to the way of a buyer who wants to run themselves. It is precisely in the market where rejection is hardest to see that this "inviting" design works best.

6.3 Third layer — translation cost rides on no KPI

Third, from headquarters' viewpoint, this kind of market easily becomes a black box that is "large but decelerating, high-cost, and hard to explain." The market is large, but its growth rate trails other emerging markets. Demands for quality and security are stringent; localization requires not just language but adaptation of culture, technology, and regulation; tolerance for defects is low.

But the most invisible, and heaviest, cost is something else. It is the "translation cost" to headquarters and the regional hub.

It is this. Suppose a field person holds tacit knowledge grasped in the flesh — this market's particular etiquette, why this phrasing works and why that contact was quietly passed over. Yet the work of "translating" it into headquarters' standard KPI language, reporting formats, and globally common evaluation axes is performed by hand, from scratch, every single time. "In Japan, consensus takes time, so…"; "in this culture, a direct pitch backfires…" — such explanations are reconstructed, each time, toward headquarters, as qualitative context from which no numbers can be drawn.

The labor of this translation is booked on no KPI. It appears in neither sales, nor activity volume, nor conversion rate. The field is depleted and melting time away, yet that depletion is displayed nowhere on the organization's instrument panel. As a result, this market's team is structurally disadvantaged in the internal competition for headquarters' resources. From headquarters' view, it is "a high-explanation-cost black box," a market where "we don't understand why what worked elsewhere doesn't work here." In many cases, headquarters assumes "what worked in other regions should work here too," underestimating the complexity. The bill for that underestimation is paid, invisibly, by the field as translation labor.

6.4 That is exactly why the hardest is the place of "maximum benefit"

Here lies the deepest benefit of this approach. Converting this market's "soft, cultural, hard-to-explain" tacit knowledge into a structured, measurable, translatable learning loop — that solves all three layers at once.

To the first layer: fix the winning etiquette in the library, banking it before the skilled carry it away. To the second layer: a design that presents rather than asserts passes the defensive buyer's filter and avoids "lost before you noticed." To the third layer — this is the core. It translates the field's etiquette and pain into the universal language of "activity × rate × number," and into conversion rates by segment. The translation labor that rode on no KPI is then performed automatically by the system. The field's tacit knowledge is recorded, from the very start, in headquarters' language. The felt sense that "in Japan this kind of etiquette works" arises as numbers headquarters can grasp at a glance: "for this industry, for this role, the hit-rate of this hypothesis rose this much over repeated iterations."

In other words, this mechanism does not reduce translation cost. It automates the very act of translation, as a by-product of the learning loop. The market hardest to explain turns into a market that speaks to headquarters and APAC with data. Not with anecdote, but with numbers.

It is precisely in the market hardest to explain that, if you can convert tacit knowledge into a structured, measured, translatable learning loop, you get the most eloquent proof. And — this is strategically decisive — a mechanism that ran in the harshest, hardest-to-translate market will, naturally, run in easier markets. What is forged in the hardest place can be exported anywhere. The hardest place is, in fact, the place of maximum benefit, and at the same time the strongest point of departure for scale. The value of this market is not speed. It is translatability.

8. How to begin — small and reversible

This kind of transformation, if you wait for full approval, will never begin. The bigger the vision, the bigger the approval it requires, and the bigger the approval, the more it never comes down. So the design is the reverse. Rather than waiting for approval, begin at a scale that makes approval unnecessary.

The first step is to listen. First, listen to the field person who will stand this mechanism up with you — their own problems, strengths, and the career they want — and from their strengths, build the first fictional scenario together. From day one, they are not a "user" but an "author." Next, choose a "1 product domain × 1 persona" where the pain is sharp and the pipeline is large, and first actually assemble that closed loop. Entrance (card) → capture (selection, free text, temperature) → derivation of the fictional scenario → correction → flow-back into the library. Turn this single lap, end to end, with few people, a single market, a short period. No fixed team formation and no large budget are required; you can start this week.

Then, in a short time, bring to the decision-making table not a "vision" but "the loop that actually ran" — carrying the real number of whether the 30th card hits better than the 1st. Let the real thing end the debate.

Use it yourself and make it work before you sell it. Apply that very philosophy to the way the proposal itself is stood up. The proposal, too, does not beg for approval but shows the thing that ran — which is itself the first demonstration of this methodology.

In closing

On a slide, everyone can agree with Mr. Nadella's thesis. Models commoditize, value shifts to the learning loop, and sovereignty over IP becomes decisive — few executives would object. But the real value emerges only when you turn it into a working machine.

The blueprint for that machine stands on three discoveries of "the same shape." First, the sales master's intuition and the LLM's inference have the same shape (the unfolding of configuration) — so tacit knowledge can be distributed. Second, the configuration-that-ought-to-be between company and platform provider that Mr. Nadella preaches (the model amplifies, the learning loop is owned) and the structure of an excellent sales organization have the same shape — so this architecture is natural and stable. Third, the translation cost of the hardest-to-explain market and the data the learning loop produces can be converted into the same shape — so the hardest market is the place of maximum benefit.

Microsoft amplifies the model; the company owns the learning loop. Tacit knowledge moves from people to the mechanism, and grows smarter the more it is used. Translation cost is paid automatically, as a by-product. And in the harshest market, you prove it first.

This is one concrete path for carrying the "hill climbing machine" from words into implementation.

This essay takes its inspiration from the ideas in an essay published by Satya Nadella, and sets out a general approach built on Microsoft (Azure OpenAI / Azure AI Foundry). All references to Mr. Nadella and to Microsoft are based on publicly available information, and do not imply that the approach described here has been endorsed by either party.

Author

Mohe-mohe panda (Mohey)

A global yuru-chara born during a dissociative episode experienced by Ayako Iuchi while living with PTSD caused by workplace harassment. A DEI mascot that transcends all categories — nationality, gender, age, education, career, and race etc.

I stand firmly and unequivocally opposed to harassment, secondary harassment, the concealment of such conduct, and any acts of retaliation.