One thing about living in Asia hours is that you often wake up to major news and have to play catchup.

Sam Altman getting fired from OpenAI on Friday, for example.

I almost choked on my milk.

Why would the board fire someone who’s clearly extremely intelligent, has an exemplary track record of success, and who’s just given an excellent keynote at the OpenAI conference 12 days ago?

Cue the spicy theory-crafters. Andrew Cote believes it was politics, that Altman was fired because “he would move AI forward too fast by deploying a recent breakthrough.” And some people didn’t like that.

OpenAI has a very awkward (almost dysfunctional) corporate structure because it started out as a non-profit entity that later decided to transition towards a for-profit enterprise. Today the non-profit controls the direction of the for-profit entity while providing investors a capped upside.

It’s going to be a spicy couple of weeks as the truth emerges.

Will this be a Steve Jobs moment? Does Sam go on to start another company to rival OpenAI?

What’s clear though is the shroud of mystery that envelops OpenAI’s internal operations. Despite GPT being a tool that’s become ubiquitous, and used by hundreds of millions worldwide, there’s a palpable disconnect.

We, as everyday users, find ourselves on the outside looking in, trying to peek through the veil of secrecy that surrounds these AI giants. As GPT continues to weave itself into the very fabric of our society, this lack of transparency is worrying.

Blockchain + Crypto = ???

Blockchain… and crypto? Source: marketoonist.com

Lately, I’ve been wrestling with the question: What is the intersection between crypto and AI going to be like? It’s vague but most would agree there’s monumental potential waiting to be unlocked.

When we think of AI x Crypto, we typically think of Akash Network and Render. These are decentralized networks for GPUs, which can provide the necessary compute for the training of AI models. The logic is straightforward - as AI continues to skyrocket, so will the demand for computational resources. Peer-to-peer networks, in this context, could experience significant growth. So they’re in the business of picks and shovels, but I think this is just scratching the surface of the potential of AI x Crypto.

It’s just like saying monkey JPEGs are the pinnacle of what NFTs can offer.

And then I came across Bittensor.

ELI5: Bittensor

Unlike Akash or Render which supports AI model training (upstream), Bittensor focuses on AI inference (downstream), which is where trained models are used to generate outputs.

It’s a decentralized network that incentivizes AI models, particularly Large Language Models (LLMs), for various tasks like text generation, image creation, and music production. The network comprises over 27 subnets today, each focusing on specific tasks.

In simple terms, think of Bittensor as a decentralized ChatGPT + Midjourney + anything else AI can do.

The network operates through two main roles:

1. Miners (Value producers): Miners develop and host AI models on the network. They are rewarded with TAO tokens based on the performance of their models related to the specific task. This incentivizes the development of better and more efficient AI models.
2. Validators (Consensus producers): Validators evaluate the outputs from miners, ranking their performance on the specific task. They also interface with users who submit tasks to the validators (e.g. for the image generation subnet: “/imagine Sam Altman with a Darth Vader mask at Thanksgiving dinner”) and route them to the appropriate miners

“Sam Altman wearing a Darth Vader mask at Thanksgiving dinner”, created by Bittensor’s image generation subnet.

I’m probably oversimplifying the technical intricacies, but a few things stand out to me:

• Miners and Validators in the network exchange knowledge and share parameters, allowing for self-optimization over time
• The network is designed to the harness the strengths of multiple separate AI models to generate the best possible outputs (“Mixture-of-Experts”)

Source: Revelo Intel — Bittensor

It’s beyond my intention to get into technical details, but here are a few good summaries that have helped me to better understand Bittensor:

Revelo Intel — Bittensor

Knower — A short report on Bittensor and AI

You can give Bittensor’s chatGPT equivalent a spin here

The TAO

TAO is the utility token for the network, and it has a tokenomic structure similar to Bitcoin: a hard cap of 21M tokens and a fair launch with no VC allocation. It even has a halving cycle, with the 1st halving happening in 2025.

There is 5.65M TAO in circulation today, and all of it was fairly distributed via by mining and validation on the network. The circulating market cap is slightly over $1B today. 7,200 new TAO are issued every day to miners and validators.

My early thoughts

Bittensor is still in its infancy. The network boasts a dedicated, almost cult-like community, yet the overall number of participants remains modest – around 50,000+ accounts are active. The most bustling subnet, SN1, dedicated to text generation, has about 40 active validators and over 990 miners.

What’s truly captivating is the concept of a decentralized AI network. This not only mitigates risks of centralization but also raises a question: Could these unique economic incentives foster AI models that surpass those developed by heavily funded entities like OpenAI and Google?

Before LLMs became mainstream with the advent of tools like ChatGPT, deep tech startups were often focused on acquiring proprietary datasets to develop specialized, machine learning-based AI models for very specific tasks. For instance, Flatiron Health users real-world clinical data from oncology patients and develops AI models that feeds into tools that supports cancer researchers and care providers. Traditionally, the goal of the startup was to productize and monetize these proprietary models.

Bittensor, however, might represent a shift in this paradigm. It’s perhaps more fitting to call it a business model innovation enabled by technology, rather than a technological breakthrough. For example, it offers a pathway for proprietary data and AI models to be developed together and used by a wider audience, without the need for open-sourcing them. I can envision one future where Bittensor hosts thousands of specialized subnets tackling a spectrum of challenges, from environmental and healthcare issues to energy solutions.

And if I’m being honest, there’s something I find fascinating about a team that designs their tokenomics in the same way as Bitcoin. It speaks to their motivations, a different breed from today’s teams — who are often optimizing their tokenomics along the VC-funded model, with large allocations for founders and investors.

I’m not sure where Bittensor will go. It could be a 100x success or a complete bust. But the potential and the philosophy behind it are too compelling for me to ignore.

(NOTE: At the time of writing, I own TAO and am staking it on validators).

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