Bernie Sanders wants you to believe that artificial intelligence belongs to everyone. The political rhetoric sounds beautiful on paper: nationalize the compute, distribute the algorithms, and treat LLMs like municipal water systems. It is a comforting fantasy that completely misunderstands the mechanics of technological progress.
Treating AI as a public utility is the fastest way to turn a hyper-dynamic frontier into a stagnant, bureaucratic DMV.
The argument for "public AI" rests on a flawed premise. Activists look at the massive datasets harvested from the open internet and claim the resulting models are a common good. They argue that because the public generated the data, the public owns the output. This ignores the reality of compute economics, engineering risk, and the actual history of infrastructure development.
Data in its raw form is not oil; it is dirt. The value is not in the existence of the information, but in the millions of dollars of hardware, proprietary alignment techniques, and specialized engineering required to refine it. Forcing companies to open-source everything or surrender their models to public committees does not democratize AI. It destroys the incentive to build it in the first place.
The Tragedy of the Public Compute
When politicians demand a public AI resource, they imagine a neutral, benevolent system that serves everyone equally. They miss the inevitable bottleneck: compute allocation.
Supercomputers are not infinite. They require massive amounts of electricity, constant cooling, and continuous hardware replacement. If a government entity manages these resources, how do they decide who gets access?
- Does a medical researcher trying to cure pancreatic cancer get the same priority as a college student writing a term paper?
- Does a local business optimizing its logistics pipeline get pushed aside for a state-funded art project?
When resources are managed by bureaucracy rather than market pricing, allocation becomes political. We have seen this play out in public university research labs for decades. Getting access to high-performance computing clusters involves months of paperwork, committee approvals, and political jockeying. Meanwhile, a private startup with a credit card can spin up thousands of GPUs on AWS in five minutes.
Speed is the only metric that matters in technology. A public utility structure introduces friction at every layer. By the time a government committee approves a model update to fix a structural bias or optimize inference speed, private competitors will have shifted the entire architecture three generations forward.
The Myth of Private Monopoly
The core fear driving the public AI movement is that a handful of tech giants will lock up the technology behind massive paywalls, creating a permanent economic underclass. This fear is detached from current market realities.
I have spent years advising enterprise companies on infrastructure deployment. The biggest shock to executives is always how quickly the cost of intelligence is dropping. Open-source models like Meta's Llama series or Mistral are already competitive with proprietary systems, and they cost nothing to download. The market is naturally decentralizing because training efficiency is compounding.
Imagine a scenario where the government steps in to regulate AI as a utility to prevent monopoly. To do this, they must define what an acceptable model looks like. They must establish compliance frameworks, safety boards, and licensing requirements.
Who wins in that environment? Not the open-source developer working in a garage. The winners are the exact tech giants the politicians claim to fight. Megacorps love regulation because they can afford the army of compliance lawyers required to navigate it. A public utility framework acts as a moat for incumbents, cementing the monopoly it was designed to break.
Open Source is Not Public Ownership
We need to establish a sharp distinction between open-source software and public infrastructure. Open source thrives because of decentralized, meritocratic contribution driven by personal or commercial incentives. Developers contribute to Linux or PyTorch because it solves a specific problem for them or builds their professional reputation.
Public ownership introduces state control. When the state controls the model, the state defines the truth.
Consider the alignment problem. Every large language model requires a set of guardrails and behavioral guidelines. In a private market, consumers choose the alignment that fits their needs. A defense contractor uses a model optimized for tactical analysis; a children's book publisher uses one optimized for safety and creativity.
If a model is a public resource, its alignment must be legislated. It becomes a battleground for culture wars. Every election cycle would bring a new set of content moderation guidelines, shifting the model’s core logic based on who controls the legislature. You do not get an objective, neutral intelligence; you get a bureaucratized echo chamber funded by taxpayers.
The Infrastructure Blindspot
Proponents of public AI like to compare it to the interstate highway system or the electrical grid. This is a false equivalence.
A road is static. Once poured, asphalt stays in place for years, requiring only baseline maintenance. AI models are living systems. An LLM deployed today will be obsolete in eighteen months. The underlying hardware architectures are shifting from standard GPUs to specialized neuromorphic chips and custom ASICs.
Governments are fundamentally incapable of managing this level of lifecycle depreciation. Look at how state agencies handle basic IT infrastructure. Most government databases still run on legacy codebases that security professionals abandoned two decades ago. Entrusting the development of foundational intelligence models to institutions that cannot secure their own payroll systems is reckless.
The capital expenditure required to stay at the frontier of AI development is staggering. Meta, Microsoft, and Google are spending tens of billions of dollars annually on infrastructure. If the public sector takes over, that financial burden shifts to the taxpayer. When a private company spends $10 billion on a cluster that becomes obsolete in two years, the shareholders bear the loss. If a government agency does the same, it is a national scandal that drains funding from schools, roads, and healthcare.
The Real Bottleneck to Access
The question we should be asking is not "How do we nationalize AI?" but "Why is compute deployment still so expensive?"
The answer is not corporate greed; it is energy policy and supply chain bottlenecks. We do not have an AI distribution problem; we have a power generation problem. Training and running frontier models requires massive amounts of baseload electricity. The same political factions advocating for public AI are often the ones blocking the construction of nuclear power plants and next-generation energy grids.
If you want to democratize AI, you do not seize the models. You build nuclear reactors. You deregulate energy transmission so data centers can scale without crashing local grids. You streamline the permitting processes for semiconductor manufacturing plants.
Lowering the cost of the inputs—power and silicon—is the only sustainable way to lower the cost of the output. Market competition will handle the rest.
Stop Demanding Handouts, Start Building
The narrative that AI is a public resource is driven by a desire for a free lunch. It assumes that the hard work of innovation is finished, and all that remains is to divide the spoils.
This mindset is dangerous. We are in the earliest innings of cognitive computing. The architectures we use today will look like steam engines to the next generation of engineers. Forcing the industry into a public utility mold today freezes the technology in its current, imperfect state.
True democratization does not come from a government mandate or a public distribution committee. It comes from relentless, cutthroat market competition that drives the cost of intelligence down to near zero. The best thing the public sector can do for AI innovation is to get out of the way, fix the energy grid, and let the engineers build.
