POKT Network & AI: First Principles

Thanks @Adrienne. Yes, I can and will share details about what I’m thinking. I’m working on that this week.

1 Like

I think that after 2023 bear, that’s not the case anymore, no proofs tho…

You still require a lot of hardware for big models and ollama is not a good option if you want inference speed (what everybody wants).

In time yes, for Morse I just expect a dashboard that shows that we are cheaper and have quality stuff running.

The same questions we could have posed for blockchain nodes:
why would someone spin up blockchain nodes when they are easy to deploy, there are API available and those who have them might not be willing to connect to Pocket?
We don’t know why, but Pocket is alive and now even Infura has partnered with us.
With ML endpoints, LLMs particularly, we have the same doubts, but if we build the correct incentives they will come, and I think that we know how to build incentives.

I don’t want to deflect the questions, I believe that you have valid concerns and I wont be able to give you straight answers. Strong benefits are hard to see, in fact the only real one in v0/Morse in censorship resistance.
However we are in a great position to be the first doing this and we will learn along the way, we have done it before.


You are not wrong. Due to flawed tokenomics in Morse node running is only sustainable for large providers due to requiring substantial hardware renting. This is not a sustainable future for POKT. With Shannon you won’t have to be a multi-continental, hardware renting titan to be a supplier, which opens the door to different types of supplier growth.

So I wouldn’t look at Morse’ current supplier typology to gauge what the supplier ecosystem could look like in new markets, like AI.

This is a good summary of what market research is required. POKT should make sense in a market when it is diving into it. My gut has been that POKT does make sense in AI, as I see POKT as data protocol that specialize in for heavy data sources. Blockchain nodes are a good example of sources that require a lot more compute than what folks what to run for their business. To me, LLMs are similar.

Just like POKT’s RPC market revolves around serving gateways (which are essentially UX businesses), there is a real potential for LLM gateways. LLM gateways that specialize in specific user experiences… some will be enterprise, and some will be consumer facing.

I think it is worth understanding what the general LLM market will be, where users/business don’t need specialty LLMs. POKT would first be able to provide access to general LLM deployments… so I’m curious what those kind of markets will be like.

The LLM API ecosystem is still young, and it not fully realized, so I definitely see a place for POKT.


Beyond general inferencing, there is definitely an opportunity to connect AIs to existing POKT sources (like blockchain nodes). Think of it like putting an AI layers on top of POKT current data sources.

Example: POKT currently serves blockchain data, so what about creating an AI ServiceID which has access to the blockchain data? With any data source on POKT, there could be a complimenting ServiceID for that data that is AI powered.

Basically a regular RPC ServiceID and an AI powered ServiceID for each data source (or chain).

That kind of use-case wouldn’t require heavy models, but could be run more efficiently with small, specialized models that build on using existing data sources on POKT. I think this provides one of the most direct utility for POKT’s current market.

Instead of trying to compete with big AI, it makes sense to think AI layers on top of POKT… where access to heavy data sources is the AI’s strength.

That is the kind of market research I think would be worth trying to explore.


@shane This is precisely the opportunity I’m imagining also! Pocket is uniquely positioned to become the standard for enabling trustless collaboration between AI systems/agents. I’ve started working on a whitepaper detailing what I’m referring to as Trustless Agent Orchestration, one of the biggest challenges facing multi-agent AI system builders. It’s also one of the most significant opportunities for blockchains, and Pocket could begin offering TAO services today—even before Shannon or any new development is completed. [EDIT: @Jinx mentioned that TAO is not the best name since it’s Bittensor’s token. It’s also AI-related, so I agree we need a better name. But it’s the general positioning that’s key.]

Pocket’s already-supported blockchains provide so much potential utility, and AI system builders are beginning to consider how they fit into the mix. Mostly, it’s about figuring out how AI agents will collaborate across organizational and system boundaries in a trustless way, which is obviously what blockchains are all about.

Projects like LangChain, AutoGPT, AutoGen, BabyAGI, and CrewAI are rapidly gaining popularity because multi-agent orchestration is the path to more capable AI systems. Also, emerging standards like Agent Protocol are quickly gaining traction, accelerating the development of multi-agent systems that cross organizational and platform boundaries.

^^Yes, this is it!

I’m working on some concrete examples of exactly how I think a TAO offering could be positioned. I hope to have details to share in the coming days.


Can you expand, or share more details on how Agents would work on Pocket?

Agents are basically heuristics mounted on Language Models engines that can have (or not) access to several data sources.
As I see it Pocket will only offer access to these LM engines and data-sources (blockchains only right now). So, the Agents will be outside the Pocket Network, built by companies that want to use our services (devs, gateways, etc).
Agents can be part of the narrative but they are not part of the tech stack of Pocket Network right now, this should be really clear.


I’m not saying agents will run on Pocket. I’m using the term AI Agents to describe AI systems that can work together, use tools, and learn from one another. Here is an older OpenAI post and related white paper that describes the general concept. ChatGPT (and similar systems) are much more than just LLMs. They are multi-agent systems that provide functionality by combining multiple language models, general models, coded systems, runtime environments, etc. But it’s their ability to use ‘tools’ that I’m seeing as the opportunity for Pocket.

I’m working on a more detailed write-up that I hope to share in the coming days. But here is the abstract I’ve written that hopefully provides a decent TLDR in the meantime.


Today, if you ask ChatGPT or Gemimi for the current temperature in a given city, you’ll get an accurate answer. But if you ask for the current balance for a public cryptocurrency address, you’ll be told to go elsewhere. The reason is that there is no go-to tool that AI systems can use to access blockchain data. Pocket could be that tool without any new Pocket development efforts.


Next steps following the call last week

Thanks everyone for a great discussion last Wednesday - see here for the recording and here for a summary that Adz put together.

POKT Network has clear strengths to build on in the worlds of AI inference, RAG and acting as an API for AI agents more generally. And it’s great to see builders like C0d3r, Grove, Matteo, and POKTScan working to turn this potential into a reality already.

While R&D continues in parallel, we (PNF) are proposing some next steps to develop a litepaper for POKT Network in AI that will provide focus as we move forward at pace.

We propose an empowered working group to execute on this.


An agile and informed AI Working Group made up of experts in AI, with a strong understanding of the protocol and the market, reporting to the PNF board.

PNF proposes: Olshansky (lead), Bowen, and Ramiro.

  • Olshansky has over 6 years of experience doing AI/ML at Magic Leap and Waymo and unparalleled knowledge of our protocol.

  • Bowen previously led a team that bootstrapped and built out a foundation model and LLM inference platform for Apple and is now a principal engineer at EigenLayer.

  • Ramiro is an ML scientist who has already deployed an LLM to support the POKT ecosystem and has a deep understanding of the protocol and its economics thanks to his work at POKTscan and as a community contributor.

Additional expertise in the community will be consulted and involved on an as-needed basis. Including but not limited to, Steve, Mike, C0d3r, Shane, Jinx, Gabi, Matteo, and our broader network of advisers, investors, contributors and supporters.


  • Produce a litepaper that articulates a clear vision for POKT Network’s AI potential, including a high-level roadmap and necessary future R&D. The Target date for publication is 17 May, but the consultation period will start much earlier than this to allow considerable time for input from all relevant stakeholders and experts.
  • PNF will use this litepaper to develop more accessible communications and documentation for a broader audience, as well as to work with all interested parties on unlocking funding from the DAO towards the opportunities highlighted within.