The AI race isn't won by the biggest model. It's won by the smartest and most efficient one.
Meet @169Pi_ai - an Indian-origin AI company building a full-stack AI ecosystem that delivers frontier performance at a fraction of the cost.
Here's everything you need to know. 🧵
Did you know that @Rajatarya01 and @BizChirag were in the @ForbesIndia 30 Under 30? That's a big achievement!
They also mentioned that @isro has a partnership with @169Pi_ai!
Read more about the partnership here:
https://t.co/JYe7oQQGsC
Watch how the AI industry talks about cost.
Frontier labs say "compute."
Builders say "inference."
One is a vanity metric. The other is the only number that matters once you actually have to serve users.
Which camp is your favorite AI company in?
@LLuciano_BTC Every coin is dropping except for $PIE. Its still growing every day. Don't miss this chance. You are still early. Even an invesment of $100 will make you rich with enough patience 😉 Get on board 👀. Check out yourself @169Pi_ai@AlpieInsights
7/7
I’d urge builders in both communities to stop staying in their lanes.
The crypto side has the rails, the liquidity, and the user behavior data.
The AI side has the models, the deployment, and the actual intelligence.
Whoever bridges these two from the AI side wins the next decade.
6/7
At @169Pi_ai we’re building from the AI side first.
Real models. Real deployments. Real customers. Then crypto where it actually adds utility, starting with $PIE as the payment and access layer for Alpie.
Not a token searching for a product. A product earning a token.
5/7
The next phase of crypto utility isn’t another L2.
It’s AI infrastructure that uses crypto rails because they’re the best rails for what it needs. Micropayments for inference. Stake-based access. On-chain compute markets. Tokenized model contributions.
The primitives exist. The AI side just hasn’t caught up.
4/7
Imagine an AI model trained with deep on-chain understanding. Years of crypto data. Wallet behavior. Liquidity patterns. Smart contract logic.
Not another chatbot pretending to do TA.
A model that genuinely reasons about crypto the way a research analyst would, except it processes the entire chain in seconds.
That’s not a token gimmick. That’s a product nobody’s built.
1/7
The biggest lie in the AI x crypto narrative is that it’s already been built.
It hasn’t.
What’s been built is crypto projects with “AI” in the name. The actual AI inside most of them is weaker than a $20 ChatGPT subscription.
That’s not AI x crypto. That’s crypto cosplaying as AI.
I recently decided to invest $2,000 into $PIE because I believe the broader market will eventually discover and adopt products like this alongside tools such as ChatGPT and Claude.
The flagship product of @169Pi_ai is Alpie Core:
– 32B parameter reasoning model
– One of the world's first 4-bit quantized reasoning models
– Comparable to or outperforming GPT-4o and Claude 3.5 Sonnet across several reasoning and SWE benchmarks despite running at only 4-bit precision
Additional highlights:
– 65K context window
– Open-source under Apache 2.0
– Available on Ollama, Kaggle, and other platforms
Personally, I think what stands out most is the focus on efficiency rather than simply scaling model size.
From a TA, $PIE has also been showing strong momentum recently.
For now, this may be one of the most promising AI gems I've found in 2026.
DYOR & NFA.
You know what the future is? A cheap and widely available LLM that IT companies, developers building AI agents on blockchain, and many others are already using. Grok and GPT are slowly shutting down their free options and soon only premium will remain.
There's a new competitor on the market @169Pi_ai, an 18 person team from India that built their own LLM. Plus, one of their premium payment options will be the $PIE token this month.
You're a top researcher, so I won't bullshit you check it out yourself if you want. Project backed by @reliancejio 230B$ company
It certainly does have significant partners. Pie is highly fundamental infrastructure. So, in turn development and feats generally take longer. It's a laggard. But I still stand with the fact $PIE - @169Pi_ai could be one of the biggest plays to come from pump fun if executed right.
For years, AI progress has largely been measured by capability.
Today, capability remains important but efficiency is becoming equally critical.
Memory footprint, inference cost, deployment accessibility, and latency are no longer engineering constraints sitting at the edge of the stack. They are becoming core AI research problems.
This is one of the reasons we are exploring lower bit systems like 1-bit & 4-bit at 169Pi. Not because smaller models are inherently better.
But because delivering advanced intelligence efficiently may be one of the defining challenges of the next decade.
And as AI moves beyond text into multimodal reasoning, agents, and real-world workflows, that challenge only becomes more important.