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GM. This is Milk Road, the crypto newsletter that's more addictive than refreshing your portfolio during a bull run. |
βHereβs what weβve got for you today: |
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BITTENSOR'S GOT THE BIGGEST NAMES IN TECH TALKING. BUT WHOβS PAYING? πΈ |
ICYMI: Bittensor is a decentralized AI network with its own token (TAO) - and it is having a MOMENT right now. |
While most tokens/projects are suffering a narrative drought⦠|
It feels like everyone in the tech/crypto space is talking about Bittensor. |
E.g. Jason Calacanis from the All-In Podcast helped to push TAOβs price up ~10% (bottom to top) in the span of ~8hrs last night, after he tweeted "$tao > $btc": |
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So what's got everyone so excited? |
Of late, the main focus has been βCovenant-72Bβ, a 72-billion parameter AI model that was trained on Bittensorβs decentralized network. |
No data center. No billion-dollar GPU cluster. |
Just 70+ independent contributors training it across regular internet infrastructure. |
Chamath recently pitched it as "a pretty crazy technical accomplishment" to Nvidia CEO, Jensen Huang, on the All-In Podcast - and got this response: |
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Now, itβs worth flagging: |
The All-In crew isn't just cheerleading from the sidelines here. They've got chips on the table. |
Chamath has publicly discussed investing in Bittensor⦠|
Jason went further, launching Stillcore Capital last year, a fund exclusively focused on TAO and Bittensor subnet tokens (subnets are specialized mini-networks within Bittensor that handle different AI tasks). The fund's stated goal? Own 1% of the circulating supply⦠|
And their co-host David Sacks, is the current White House Crypto & AI Czar. |
Point is: they have clear financial incentives to talk TAO up. |
And so far? Itβs working. |
β¦ but, when something moves fast enough in one direction, not everyone is going to agree on whatβs driving it. |
Pine Analytics just published a deep dive on Bittensor, and the findings paint a very different picture. |
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BITTENSOR'S GOT THE BIGGEST NAMES IN TECH TALKING. BUT WHOβS PAYING? (P2) πΈ |
The question Pine Analytics asked: |
Can the network generate enough real revenue to justify its $2.6B (now $2.9B) market cap? |
Their answer? Not yet. And maybe not for a while. |
Right now, the total identifiable revenue across the entire Bittensor network is roughly $3M to $15M per year. |
The biggest subnet, Chutes (which sells AI computing power, kind of like a decentralized version of Amazon Web Services), looks great on paper. |
400,000+ users, 5M+ daily requests. Impressive stuff! |
But those cheap prices aren't from efficiency. They're from subsidies. |
Chutes receives roughly $142,000/day in TAO emissions (that's ~$52M annualized). Estimated actual customer revenue? Around $1.3M to $2.4M per year. |
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Translation: for every $1 customers pay, the network kicks in $22 to $40 in token rewards to keep things running. |
"But this is the Uber playbook! Subsidize now β raise prices later!" |
Pine thought of that too. Uber built a strong foundation of βswitching frictionβ during its subsidy phase (driver networks, proprietary platforms, enterprise integrations, etc.). |
Bittensor's models are open source. The APIs are standard. Users can bounce to any provider with zero friction. |
Pine is essentially asking: when subsidies shrink, what is there to keep anyone around? |
And on top of that⦠|
Against a $2.6B market cap, Pine estimates TAOβs revenue multiple sits somewhere between 175x and 200x. |
So what is TAO actually priced on? |
AI sector hype. Bitcoin-like scarcity (21M hard cap on tokens). A pending Grayscale ETF (exchange-traded fund). Some very loud co-signs from Silicon Valley's inner circle.
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A TAO position built on narrative and scarcity? That might do great regardless. |
A TAO position built on Bittensor becoming a real AI services business? From what Pine Analytics found, the evidence for that may still be lacking. |
The takeaway: |
If youβre seeing the headlines about TAO and considering taking a position - understand the bet you're making. |
(Narrative and scarcity seem to be in the driverβs seat right now.) |
But hey - if it works, it works Β―\_(γ)_/Β― |
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