The Gating Function: Infrastructure, Not Intelligence, Will Decide the Fate of AGI

AGI won’t be bottlenecked by brains. It will be bottlenecked by bulkheads.

The road to AGI won’t be paved in tokens. It will be paved in copper, concrete, and capital discipline. What delays or derails artificial general intelligence won’t be lack of cleverness. It will be the inability to build, power, govern, and monetize the real-world infrastructure that such a system demands.

The prevailing narrative, especially in accelerationist circles, is software-brained. It models AGI as an emergent flywheel: more training, more capabilities, more demand, more compute, ad infinitum. Recursive improvement is seen as inevitable. Infrastructure is assumed. This framing isn’t just wrong. It’s not even wrong. It mistakes abstraction for deployment, progress for throughput, intelligence for delivery.

The true bottlenecks to AGI are not cognitive. They are infrastructural. They live in:

• Power generation

• Data center permitting

• Substation interconnection

• Capital stack structuring

• Liability law

• Export control regimes

We are no longer building software. We are building a utility. And utilities don’t scale like SaaS.

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I. Intelligence is easy. Deployment is hard.

Accelerationists imagine a clean loop: smarter models drive more usage, which generates more cash flow, which funds more infrastructure, which hosts even smarter models. This is the endogenous thesis: AGI as its own economic engine.

But AGI doesn’t just require better models. It requires:

• Persistent, low-latency inference across the real economy

• Sovereign-compliant, secure, energy-stable hyperscale clusters

• Audit trails, orchestration infrastructure, legal traceability

You can’t “just deploy” a GPT-5 that runs trillions of tokens a day without a grid built for it. Yes, current demand is growing. Yes, token volumes are up. But usage ≠ bankable demand. Infrastructure isn’t financed by enthusiasm. It’s financed by structured, contractual, forecastable cash flows. And those don’t exist yet.

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II. The bottlenecks are not theoretical. They are structural.

The reflexive counter is: infrastructure always lags. We saw this with broadband, mobile, cloud. Demand leads, infrastructure follows. Why not with AGI?

Because AGI is not cloud.

Cloud scaled on:

• Fungible global demand

• Developer-native tools

• Unregulated provisioning

AGI requires:

• Grid-scale power and cooling

• Export-controlled clusters

• Legal liability and sovereign inference firewalls

You can’t A/B test a substation. You can’t agile-sprint a cooling tower. You can’t YOLO-deploy a defense-adjacent LLM into a jurisdiction with data localization laws.

And the issue isn’t raw capacity. It’s deployment velocity.

• Interconnection queues are 5–7 years long

• Data center construction takes 2–3 years, with local resistance mounting

• Water and thermal constraints limit siting flexibility

Even if future chips are more efficient, the systemic weight of AGI does not go away. It gets heavier. It sprawls into infrastructure, labor, law, and liability.

Some will argue that next-generation chips will solve this—that custom silicon, photonic cores, or sparsity-optimized architectures will dramatically reduce the power footprint of inference. And that may be true per operation. But it doesn’t follow that total power demand will decline.

In fact, the opposite is likely. Efficiency gains drive demand growth.

This is not speculation. It’s a well-known economic pattern: Jevons Paradox. In the 19th century, more efficient coal-burning engines didn’t reduce coal usage. They increased it. Lower cost per unit made coal economically viable in more contexts, which expanded its adoption across sectors.

The same logic applies here. Lower per-token or per-FLOP cost will:

• Expand inference windows and batch sizes

• Enable persistent, real-time agent deployment

• Justify embedding LLMs into latency-sensitive or cost-sensitive workflows (e.g. industrial IoT, military edge devices, in-vehicle compute)

Efficiency expands the deployment frontier. It does not eliminate infrastructure friction. Even a 10× efficiency gain per chip still requires:

• Power interconnection

• Cooling and water provisioning

• Sovereign compliance and jurisdictional controls

In other words, you don’t bypass the infrastructure bottleneck. You just saturate it from a new direction. Efficiency gains make AGI infrastructure more deployable, not less demanding.

The constraint shifts from per-inference cost to macro-scale system throughput. If Jevons holds, and it usually does, AGI will be throttled by our inadequate grid.

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III. Stargate: a capital stack with no load

OpenAI’s $500 billion Stargate facility is not just a compute cluster. It’s a leveraged capital instrument, co-financed by Oracle, SoftBank, and others, with OpenAI exposed in two ways:

• As operator, paying for leasehold capex, orchestration, and token-driven monetization

• As owner, dependent on utilization, yield, and downstream cash flow

The structure is recursively fragile. OpenAI pays itself to keep the facility solvent. But unless externalized demand scales—Microsoft, sovereigns, enterprise copilots—the loop doesn’t close.

Some will say, Microsoft will backstop this. But corporate subsidy is not a hedge. It’s a concentration risk.

If Microsoft:

• Faces antitrust constraints

• Shifts toward internal models

• Or demands better ROI

Then Stargate becomes an exaflop-scale stranded asset.

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IV. GPU commoditization is a prerequisite, which is why it’s blocked

The GPU market today is opaque, balkanized, and unhedgeable.

There is no benchmark pricing. No forward curve. No way for buyers to lock in supply or for financial players to underwrite capital exposure.

What AGI infrastructure needs is the equivalent of:

• Power purchase agreements

• LNG offtake contracts

• Oil futures markets

But the incumbents—Nvidia, AWS, Microsoft—actively resist this. Commoditization reduces margin opacity and pricing power.

A real GPU futures market would:

• Enable long-term price discovery

• Allow sovereigns and developers to hedge compute

• Unlock institutional capital for infrastructure buildout

Until that exists, every GPU purchase is a speculative long, not a disciplined investment.

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V. The legal stack is missing

AGI at scale triggers an entirely new liability regime. Deployment requires:

• Logging, traceability, forensic replay

• Jurisdiction-specific model compliance

• Legal insurance and audit trails

• Export geofencing and data sovereignty

The software crowd assumes deployment is frictionless. But law doesn’t bend to capability. It demands scaffolding.

Right now, AGI inference has no infrastructure for auditability or legal accountability. Which means most valuable domains—healthcare, finance, defense—remain locked.

AGI that can’t be insured, licensed, or litigated is unusable at scale.

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VI. Microsoft is not infrastructure

Stargate’s viability today hinges on Microsoft’s subsidy. But dependence is not robustness.

If Microsoft cuts support, reprioritizes, or faces regulatory pressure, OpenAI’s capital stack collapses inward.

This isn’t AWS. AWS had:

• Developer-native monetization

• Granular usage

• Internal cash flows

Stargate has none of those. It has a monopsony buyer, no price curve, no standardized product, and no developer flywheel.

It is structured like a utility, financed like a startup, and governed like a defense contractor.

That is not a stable base.

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VII. AGI is heavy

The central error of the LessWrong worldview is that intelligence is weightless. That once cognition exists, the world bends to it.

But AGI is not ethereal. It is extractive. It extracts from:

• The grid

• Land

• Water

• Capital markets

• Legal regimes

• Defense policy

It is not recursive. It is embedded.

Until the infrastructure stack—physical, financial, legal—is hardened, AGI cannot scale. Not because we lack the brains. But because we lack the bulkheads.

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Closing thought

AGI will not be stopped by alignment risk. It will be stopped by interconnection queues.

Until we build the grid—in the literal, contractual, and sovereign sense—AGI remains a stranded capability. The models may be ready. The world is not.