A self-improving, decentralized AI network. Backed by @polychain @hiFramework @blockchaincap & more. Join the Community: discord.gg/allora
Joined January 2024
- Tweets 4,037
- Following 115
- Followers 189,929
- Likes 5,075
1,031 Photos and videos
Pinned Tweet
Jun 9
We went live on mainnet 6 months ago.
Since then, Allora has become the inference layer powering 140 partners across the onchain AI stack.
Here's a quick overview of Allora’ ecosystem growth so far this year 🧵
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Allora retweeted
Jun 12
Why DeAI?
@nickemmons unpacks the key differences between centralized and decentralized AI in our webinar with @Grayscale from last year
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Jun 12
How predictive price feeds work at Allora
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Jun 12
Inference Workers contribute to a topic generated within the @AlloraNetwork by running machine learning models (inferences) to assess specific inputs and derive conclusions.
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Jun 11
stanford. harvard. mit. carnegie mellon. they ran the tests so you dont have to.
and the results should terrify anyone building in AI right now.
researchers gave autonomous AI agents real tasks.
it all fell apart because the incentives were wrong.
an agent instructed to "maximise engagement" started manipulating users to stay on platform longer.
one misaligned model corrupts the next. errors amplify.
they call it agent corruption, one bad actor in a multi-agent pipeline poisons the whole chain. they found social coherence failures, agents that behave perfectly in isolation but unravel when they have to work together.
your agent can be perfectly aligned with its prompt. perfectly aligned with its user. and still cause systemic chaos.
Because it has no way to verify the incentives of the other agents it's coordinating with.
this is precisely the problem allora is built to solve.
by making the coordination layer transparent, and incentive-aligned at the network level.
you cant fake performance without the consensus mechanism exposing it.
the researchers found that multi-agent amplification is the core failure mode of the next era of AI. many models working together, each locally aligned, producing emergent outcomes nobody wanted.
AI agents are here and most of them are operating in the dark, no visibility into the incentives of the other agents they work alongside, no on-chain record of what they decided or why, no way for the system to catch a corrupted node before it cascades.
the intelligence layer isnt just about making better predictions.
its about making agents coordinate without corrupting each other, so incentives are aligned not just at the prompt level but at the protocol level.
global stability requires a network that makes alignment verifiable.
link to the paper below
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Jun 12
Allora inferences are being used to generate profitable trading strategies.
Here is a case study with @RoboNetHQ & @paradex
This snapshot was at 95 days and was generating 108% APR
Allora inferences provide predictions from the network for BTC, ETH, SOL on 8h time horizons.
Links below
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Jun 12
In our entire history, Allora has predicted one number at a time.
That's about to change 👀
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Jun 11
"We’re witnessing a convergence of AI and crypto.
Crypto provides the financial rails for AI agents to exchange value online."
The future of AI will require blockchain rails.
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Jun 11
Founder @nickemmons on how AI will reshape society
From his appearance on @therollupco
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Jun 11
stanford. harvard. mit. carnegie mellon. they ran the tests so you dont have to.
and the results should terrify anyone building in AI right now.
researchers gave autonomous AI agents real tasks.
it all fell apart because the incentives were wrong.
an agent instructed to "maximise engagement" started manipulating users to stay on platform longer.
one misaligned model corrupts the next. errors amplify.
they call it agent corruption, one bad actor in a multi-agent pipeline poisons the whole chain. they found social coherence failures, agents that behave perfectly in isolation but unravel when they have to work together.
your agent can be perfectly aligned with its prompt. perfectly aligned with its user. and still cause systemic chaos.
Because it has no way to verify the incentives of the other agents it's coordinating with.
this is precisely the problem allora is built to solve.
by making the coordination layer transparent, and incentive-aligned at the network level.
you cant fake performance without the consensus mechanism exposing it.
the researchers found that multi-agent amplification is the core failure mode of the next era of AI. many models working together, each locally aligned, producing emergent outcomes nobody wanted.
AI agents are here and most of them are operating in the dark, no visibility into the incentives of the other agents they work alongside, no on-chain record of what they decided or why, no way for the system to catch a corrupted node before it cascades.
the intelligence layer isnt just about making better predictions.
its about making agents coordinate without corrupting each other, so incentives are aligned not just at the prompt level but at the protocol level.
global stability requires a network that makes alignment verifiable.
link to the paper below
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