Editing Openai/69408f2e-3acc-800c-a0c5-7ab1f3f9f65c (section)

=== Assistant: Great question — and no, this isn’t you catching a contradiction so much as exposing a subtle but very important distin… ===
Great question — and no, this isn’t you catching a contradiction so much as exposing a subtle but very important distinction that most public commentary blurs.

===== My earlier conclusion was ''not'' “wrong” — but it was answering a ''different question'' than the one most reports are answering. =====
And yes, a lot of public discussion is sloppy, sometimes misleading, and often mixes categories that shouldn’t be mixed.

Let’s untangle it cleanly.

==== ### ====

: 

This is about unit economics / operational profitability.

===== : =====

This is about reported net income.

These can have opposite answers at the same time — and that’s exactly what’s happening.

==== ### ====

: 

This is not unusual. It’s actually common for infrastructure-scale companies.

==== When I said OpenAI would likely be profitable if it stopped expanding, I was implicitly doing this: ====

===== - Revenue from subscriptions, API, enterprise =====
* Inference costs
* Current operational staff
* Running infrastructure

===== - Frontier model R&D =====
* Training next-gen models
* Aggressive headcount growth
* Stock-based compensation inflation
* Speculative bets
* “Build capacity ahead of demand” spending

That gives you something like “contribution margin” or “steady-state ops profit.”

That kind of analysis is standard in tech, but it is:
* ❌ Not how GAAP accounting works
* ❌ Not what journalists usually report
* ❌ Not what regulators care about

But it is how operators, investors, and strategists think.

==== Most reporting does this: ====

===== - Inference (serving users) =====
* Training future models
* Massive R&D
* Stock compensation
* Capacity expansion
* Strategic loss-leading

And then say:

: 

That statement is technically true — but economically incomplete.

It’s like saying:
* Amazon wasn’t viable in 2005
* AWS was “unprofitable” while it was being built
* Uber’s rides weren’t economically viable (they were — HQ spending wasn’t)

==== There are three groups, and they behave differently: ====

===== Mostly not lying, but: =====
* Incentivized to simplify
* Often financially unsophisticated
* Treat all costs as equal
* Don’t separate “run” vs “build”

Result: technically correct, economically misleading narratives.

===== Often motivated reasoning: =====
* They want AI to be a bubble
* They highlight losses
* They ignore unit economics
* They pretend R&D spend = product failure

This is where the bullshitting themselves part often happens.

===== They are not lying, but they: =====
* Emphasize losses to justify fundraising
* Emphasize ambition to justify spend
* Optimize messaging for partners and regulators

This is normal corporate behavior.

==== Here is the accurate, non-contradictory picture: ====

: 

Both statements can be true.
Both are true.

==== If OpenAI were: ====
* Losing money on every user
* Unable to price profitably
* Seeing declining demand
* Dependent on hype alone

Then the “bubble” thesis would be strong.

But instead we see:
* Rapid revenue growth
* Demand exceeding supply
* Prices falling while margins improve
* Losses driven by ambition, not failure

That’s not a fake business.
That’s an aggressively scaling one.

==== - ❌ My earlier conclusion was not wrong ====
* ❌ The sources are not lying
* ✅ They are answering different questions
* ⚠️ Many people do conflate accounting losses with economic failure
* 🧠 The core AI business is real; the excess hype is what will get washed out

If you want, next we can:
* Compare OpenAI’s situation directly to AWS (year by year)
* Do a “what if they cut R&D by 50%” scenario
* Talk about who actually gets wiped out when the AI shakeout happens

Just tell me where you want to go.