An open letter to Satya Nadella: Sam Altman is Microsoft’s Mistake

By Alan Jacobson, Systems Architect & Analyst

Dear Satya,

Microsoft did not just bet on AI. It bet the farm on a founder. And now it has a L'enfant terrible on its hands.

While every other hyperscaler built or retained direct control over its AI future — Alphabet with Gemini, Meta with LLaMA, Apple with Apple Intelligence — Microsoft outsourced its future to Sam Altman.

That decision is now your central risk.

Sam Altman is a brilliant technologist. But he is not a stable enterprise leader. He has been fired once by his own board. He has presided over repeated governance crises. He has written memos that should never have been written, let alone leaked. And he has demonstrated, again and again, that he improvises in moments that demand discipline.

Microsoft does not need a showman.
It needs a field marshal.

And until that reality is confronted, adoption will not follow — no matter how much capital, compute or distribution you bring to bear.

Which brings me to the same three problems blocking AI adoption everywhere — including at Microsoft.

1. Memory

LLMs survive their memory limits the same way JPEGs survived slow networks: through lossy compression.

JPEGs throw away pixels. LLMs throw away facts.

At first glance, the loss isn’t obvious. But look closely and the seams appear: blurred edges, missing detail, artifacts that weren’t visible at first. With LLMs, those artifacts are missing facts and broken continuity.

What JPEGs lose are pixels.
What LLMs lose is truth.

Without 100% loss-less memory, AI cannot be trusted. Without trust, there is no adoption. Without adoption there is no scale. And without scale, the market caps tied to AI infrastructure evaporate.

If you believe memory is a problem you can solve later, please know that a solution to this problem has been filed and is patent pending.

2. Governance

Enterprises will not adopt systems they cannot control. And users need agency as well:

Over how AI behaves, when it escalates, when it refuses and how it explains itself. They need visibility, constraint and the ability to govern outcomes rather than react to them after failure.

Right now, governance is implicit, opaque, and centralized. That is tolerable for demos. It is unacceptable for real work.

Joni Mitchell never accepted an instrument as it was handed to her. She tuned it — again and again — until it matched the sound she heard in her heart. She custom-tuned her guitar for many of her songs, including “California.”

Governance in AI should work the same way: not as control imposed from above, but as user-level tuning that lets people shape how the system behaves, remembers and responds.

AI systems that do not give users control will be treated as toys, not tools.

If you believe governance is a problem you can solve later, please know that a solution to this problem has been filed and is patent pending.

3. Revenue

This is the problem the industry keeps avoiding.

Flat-rate pricing does not scale at the enterprise level. Token-based billing does not measure cost. Tokens measure words. Words are a terrible proxy for compute.

Consider these two scenarios

A user talks to AI for thirty minutes about his girlfriend:

How she seems distant.
How she is slow to respond to texts.
How she is mysteriously unavailable.

The system dutifully transcribes every word, responds empathetically and consumes a massive number of tokens — all while avoiding the four words a human would scream immediately: SHE’S CHEATING ON YOU!

Now consider a three-word query:

“Is God real?”

Few questions demand more reasoning, context, philosophy and depth. Yet under token-based billing, that interaction may never recover the cost of compute.

That alone should end the debate over billing.

Tokens are not compute. They are a proxy—and a inaccurate one. If you want to bill for cost, you must meter compute.

And if you believe compute-based metering is something you can defer, please know this:

It’s not impossible. It’s patent pending.

Satya, these technical gaps should not be your primary concern.

You have a structural problem:

You bet the farm on a founder when you need someone fit to be field marshal.

Better models, deeper integration and tighter bundling will not overcome broken memory, absent governance and a revenue model untethered from compute — especially when those problems are layered on top of leadership volatility.

The market is already signaling discomfort. Employees feel it. Partners feel it. Wall Street is beginning to price it.

AI adoption will not be won by improvisation.
It will be won by structure, discipline and trust.

And right now, Microsoft is exposed.