Bittensor Doesn’t Need to Beat OpenAI or Anthropic

There is an uncomfortable question hanging over Bittensor: Can it realistically compete with OpenAI or Anthropic?

If “compete” means building a better general-purpose model, the answer today is clearly no.

OpenAI and Anthropic have the capital, infrastructure, talent, distribution and enterprise relationships to operate on a completely different scale.

Bittensor’s roughly $3 billion network, spread across 128 subnets, is not going to suddenly reproduce that machine.

But perhaps that is the wrong comparison.

Bittensor is not a company trying to build the next Claude. It is attempting to build an open market where different teams can build, compete, get evaluated, and earn based on the intelligence they produce.

The Skeptic Has a Point

It is easy to romanticize decentralization while ignoring the actual economics.

Bittensor still has problems with emission farming, miner cheating, validator weight copying, and teams extracting value from subnets.

Some ambitious training efforts have also shown just how difficult it is to compete with frontier labs. Covenant’s 72B training run was significant, but it remained far behind frontier models, and the team eventually left the network.

But as the network progresses, there are massive improvements to the network economics to curtail these exploit vectors, and it’s getting harder to be a bad actor in Bittensor.

How Bittensor Keeps Making TAO Harder to Earn

There Is Another Way to Look at It

The better argument for Bittensor is not that it will replace OpenAI.

It is that AI may become too large and too varied to be controlled by a handful of companies.

Chutes, Targon, Lium, Engy, and other subnet teams are already operating in real markets around inference, compute and specialized AI.

Ridges is experimenting with software-engineering agents. Large-scale permissionless training continues on the network despite teams coming and going.

All of this challenges the idea that Bittensor’s only meaningful benchmark is whether it can produce a model better than Claude.

A specialized model does not need to beat Opus at everything. It needs to be better, cheaper or more open at one valuable job.

The Real Test Is Brutal

This is where the “Bittensor is like the internet in 1997” analogy becomes useful. Most websites in 1997 were worthless. The internet still worked after its rough start.

Likewise, most Bittensor subnets may fail. That is not necessarily evidence that the mechanism failed. It could be evidence that the mechanism is doing what an open market is supposed to do: experiment widely/early and eventually concentrate value around what works.

As Bittensor advances, it needs to reach a point where it extracts value outside of the emissions system. If real customers, real revenue, and real demand begin to grow faster than subsidies disappear, then the network is getting close to being successful.

So What Are You Betting On?

This is where the debate gets interesting.

Holding TAO is not necessarily a bet that Bittensor will build a better ChatGPT.

It could be a bet that open markets can become a better way to coordinate AI production than a few centralized companies doing everything themselves.

And this means Bittensor should be judged differently.

By whether its best teams are becoming businesses, whether useful intelligence is being produced, whether weak subnets are actively yanked off, and whether the network can keep attracting builders after the emissions become less attractive.

Bittensor does not need to beat OpenAI to justify its essence. But it does need to prove that its market can produce things worth paying for.

That is the real competition, and it has barely started.