Original article:
https://albertoromgar.medium.com/you-have-no-idea-how-screwed-openai-is-9481fe33f1db

The recent argument that OpenAI is structurally doomed is sharp, rhetorically satisfying, and serious.

It is correct about risk.
It is overstated about inevitability.

There is a difference between:

• Volatile
• Overleveraged
• Bubble-exposed
• Existentially doomed

The analysis slides between those categories without separating them.

That distinction matters.

1. “Too Big to Fail” — A Misapplied Analogy

The 2008 banking comparison is clever — and misleading.

The banks that failed were:

• Opaque
• Leveraged on toxic assets
• Interlocked through derivatives
• Systemically embedded in credit and payment plumbing

OpenAI is:

• Capital intensive
• Strategically embedded
• Deeply interconnected

But it is not the backbone of global finance.

If OpenAI disappeared tomorrow:

• Microsoft absorbs the majority of impact
• Competitors fill the gap
• Enterprises migrate workloads

There would be disruption.
There would be losses.

There would not be systemic economic collapse.

The “systemic risk” argument here is about market sentiment and capital exposure — not structural financial fragility.

2. Google / DeepMind — The Asymmetry Is Real

This is the strongest part of the critique.

Google has:

• Distribution
• Cash flow
• Infrastructure
• Hardware
• Cloud dominance
• Consumer surface area

OpenAI has:

• Brand
• Velocity
• Cultural dominance
• Microsoft backing

Google can afford patience.
OpenAI must move faster.

That asymmetry is real.

But AI is not search.

Search monetizes attention.
AI monetizes cognition.

The durable winner will not simply be whoever has the best chatbot. It will be whoever embeds AI deepest into workflows, enterprise systems, developer tooling, and operating layers.

That competition is unresolved — not settled.

3. Enterprise Shift to Anthropic — Hedging, Not Defeat

Enterprise markets are:

• Diversified
• Multi-model
• Risk-managed
• Slow to consolidate

No serious company will depend on a single model provider long-term.

Anthropic gaining share reflects hedging behavior — not OpenAI’s collapse.

Also, current enterprise preference is heavily coding-weighted. Coding dominance does not equal total enterprise dominance.

Broader AI integration — operations, agents, reasoning systems, workflow automation — is still forming.

It is early.

4. The Altman Variable — Governance, Not Drama

The CEO critique often becomes psychological.

Yes:

• He was removed.
• The board cited candor concerns.
• Former executives have criticized him.

But hypergrowth companies frequently require aggressive capital and narrative leadership.

The actual risk is not personality.

The risk is capital discipline under trillion-dollar infrastructure commitments.

That is a governance and financial management question — not a moral one.

5. The $1.4 Trillion Commitments — The Real Question

Yes, the scale of infrastructure commitments is staggering.

But so were:

• Amazon’s warehouse expansion
• Tesla’s gigafactory buildout
• Nvidia’s early GPU thesis

The question is not:

“Is the spending large?”

The question is:

“Is demand compounding faster than infrastructure cost?”

If yes → survivable.
If no → margin compression breaks the thesis.

We do not yet know.

The Missing Variable in This Entire Debate

The discussion above — both the critique and the rebuttal — focuses entirely on supply:

• Compute
• Capital
• Governance
• Competition
• Infrastructure

But AI revenue does not scale on supply alone.

It scales on execution.

And execution happens at the user–model interface.

There is an additional distortion most commentary ignores:

Because AI is conversational, it creates an illusion of mastery.

Truth bomb: You're not using it well.

The interface feels intuitive. It feels like talking to a capable colleague. As a result, organizations assume competence simply because interaction feels fluent.

But fluency is not mastery.

In most enterprises, structured AI operating discipline does not yet exist.

AI does not create enterprise value simply because it is powerful.
It creates value when operators know how to generate high-quality signal:

• Clear intent
• Constrained scope
• Structured iteration
• Explicit evaluation criteria
• Clean recovery after drift

Most enterprises have invested in:

• Licenses
• API access
• Pilot programs
• Vendor comparisons

Very few have invested in:

• Operator signal discipline
• Interaction standards
• Feedback loops
• Measurable output evaluation frameworks

As a result, AI performance appears inconsistent.

In many cases, that inconsistency is not model instability.

It is signal instability.

AI systems are probabilistic pattern completers.
They amplify the structure — or lack of structure — in user input.

When input is vague, contradictory, overbroad, or emotionally reactive, output degrades.

When input is structured, constrained, and iterated, output stabilizes dramatically.

The companies that win this cycle will not simply scale infrastructure.

They will scale operator competence.

That is where durable advantage forms.

And it is currently underdeveloped.

Why I’m Positioned to Work at This Layer

I’ve operated at the frontier of emerging technology before.

In AR/VR, I wasn’t observing the field — I was building inside it.

• Founder of two companies, including one expanding women’s participation in immersive technology
• Operator across product, strategy, visualization, and go-to-market
• Contributor at the standards and governance level through IEEE
• Direct experience translating emerging technical capability into enterprise execution

AR/VR revealed a consistent pattern:

Breakthrough capability does not guarantee adoption.

The friction points were rarely hardware alone. They were cognitive:

• Misaligned mental models
• Interaction design failures
• Workflow misfit
• Organizational resistance
• Poor translation from capability to usability

I’ve seen this movie before.

Powerful technology enters the market.
Capital floods in.
Narrative accelerates.
Adoption lags — not because the technology fails, but because the interaction layer is immature.

AI is at a similar inflection point.

The models are powerful.
The capital is flowing.
The infrastructure is scaling.

But enterprises are repeating a familiar mistake:

They are investing in capability without investing in structured interaction discipline.

I specialize in turning emerging technical capability into repeatable business execution.

That work happens at the boundary between system power and human cognition — the layer where most AI value is currently leaking.

If You’re Building With AI, Let’s Talk.

If you’re leading AI inside an organization right now, you already know the pattern:

The demos are impressive.
The licenses are purchased.
The pilots begin.

And then momentum stalls.

Outputs fluctuate.
Teams improvise.
Adoption becomes uneven.
Costs rise faster than measurable value.

That’s not a model problem.

It’s an operating discipline problem.

AI is conversational, which creates an illusion of mastery. It feels intuitive — so organizations assume they understand it.

But fluency is not control.
And access is not execution.

The companies that win this cycle will not simply buy more compute.

They will build structured interaction standards, signal discipline, evaluation frameworks, and internal operating models that turn probabilistic systems into reliable leverage.

That’s the layer I work in.

I’ve built at the frontier before.
I’ve seen this movie before.
And I know where emerging technologies break down between capability and real-world deployment.

If you’re serious about turning AI from impressive tool into durable infrastructure —

Reach out.

Let’s have a direct conversation.