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Matt Prusak
500 posts
Matt Prusak
@MattPrusak
President @ABTC | Run lean, build fast, stack sats
Austin, TX Katılım Eylül 2011
5.9K Takip Edilen9K Takipçiler
Great conversation - @ABTC is relentlessly focused on low-cost BTC accumulation and long-term conviction
Blockspace@blockspace
NEW POD: $ABTC President @MattPrusak says the company is staying the course on Bitcoin while peers pivot to AI. "Strategy boils down to two things: where to play and how to win. We choose Bitcoin." Is @ABTC the last major pure-play Bitcoin Miner?
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Great earnings by $ABTC
One of the strongest setups in the market
The company now holds 7,021 BTC after adding 1,620 BTC in a single quarter, all against a $1.33B market cap. That breaks down to 662 sats per share (~$0.54 at $81.5k BTC), giving good intrinsic value
What stands out is the production edge, ABTC is mining Bitcoin at roughly a 47% discount to spot. That cost advantage turns the treasury into a compounding engine, steadily increasing BTC per share faster than most are pricing in…
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Matt Prusak retweetledi
In just over 8 months since our Nasdaq debut, @ABTC has rapidly scaled to 7,300+ Bitcoin, nearly 90,000 mining machines, and an amazing 52.4% gross margin. Today we rank as the #16 largest public Bitcoin treasury in the world — and we’re adding to our holdings every day.
In Q1 2026, we delivered our strongest quarter yet: 817 BTC mined at a 47% cost discount, while growing our reserves by over 1,600 BTC.
Massive congratulations to this incredible team and the entire @ABTC family!
@Hut8Corp @ashergenoot @mikehomkh @MattPrusak
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Matt Prusak retweetledi
Join @erictrump, @ashergenoot, and the leadership teams of @ABTC and @Hut8Corp for a live discussion of the quarter
x.com/i/spaces/1DxLd…
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Current default AI modality assumes Coase when the reality may look more like Hayek…Managers guess. Markets discover.
rohit@krishnanrohit
🚨 New Experiment: Everyone thinks AI firms will look like little companies. A manager model decomposes the task and worker models do subtasks. The manager red-teams, revises, and recombines. A seemingly simple org chart. But when I ran the experiment, the current in-vogue org setup, manager-subagent, cost 4x more and performed worse than letting a rather simple market do the trick. I tested 3 ways to organize multiple AI models: 1. Solo: Onefrontier model does everything itself 2. Hub-Spoke: A "manager" model splits tasks, delegates, red-teams, revises 3. Market: Models bid on tasks, winner gets the job, reputation updates I also tested were 3 types of tasks - Coding, Reasoning and Synthesis. - Coding required most "global state" management, which the solo model did best at. In future @a1zhang's RLM will probably do even better here - Reasoning is the hardest to cleanly decompose, and the market worked the best here - Synthesis too, the market beat hub-spoke as the framing could be ambiguous The reason is, a hub isn't a "manager" as we know it. It's a model that must somehow know: - What the subtasks are - What good recomposition looks like And if either fails, as it does for complex or not-easily-decomposable tasks, competent workers still produce garbage. As we move from coding to letting multi-agent systems do work across the entire economy we'll end up with more not-easily-verifiable tasks with ambiguous settings and uncertain payoffs. In those, we won't be able to use the factory approach to get work done. The Coasean argument is that firms will get smaller, and the smaller firms will transact more, since the organisational premium reduces with AI. But how? Through central hubs, or markets? The fact is, Coase here needs Hayek. Setting up markets is not trivial, as @AndreyFradkin and I looked in our recent paper. Essay: strangeloopcanon.com/p/why-smart-pl…
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Hope @AnthropicAI remembers to invite this guy to their coalition, per @xkcdComic
Anthropic@AnthropicAI
Introducing Project Glasswing: an urgent initiative to help secure the world’s most critical software. It’s powered by our newest frontier model, Claude Mythos Preview, which can find software vulnerabilities better than all but the most skilled humans. anthropic.com/glasswing
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X Space for @ABTC recap:
• 7,000+ BTC on balance sheet (from 0 in 12 months)
• SPS >660, up every quarter since listing
• Dual engine: mine at ~50% of spot + accretive purchases
• ~$185M mining revenue, ~50% gross margins
• 14.1 J/TH fleet efficiency, scaling to ~28 EH/s
• Lean model, capital into BTC + miners, not overhead
• Industry pivoting to AI → fewer pure-play BTC miners
• ABTC staying 100% focused on Bitcoin
• Quantum: not a near-term threat, actively monitored
We’re not here to track Bitcoin. We’re here to run lean, build fast, and stack sats.
American Bitcoin@ABTC
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@ABTC Thanks all for joining - @CK_Cryptoklepto, @CoastTraders, @vco, @MainePilot, @OMMDailyMinute, @WebThreeAI, @BTC_Warden I appreciated your questions!
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Matt Prusak retweetledi
There are six companies in Bitcoin growth mode right now. 🚀 ⚡️ 🌔
1. Strategy @Strategy @saylor @phongle @shirishjajodia @CJ_Bitcoin @21MMforthe21st
2. American Bitcoin Corp @ABTC @EricTrump @ashergenoot @MattPrusak
3. Gemini @GeminiTrustCo @tyler @cameron
4. Strive @Strive @ColeMacro @Werkman @PunterJeff @IIICapital @LoganBeirne @ethanbpeck @VivekGRamaswamy @BitcoinPierre
5. ProCap @APompliano @dgt10011
6. DDC @ddcbtc_ @ddcnorma @ddckyu @TimKotzman
Tim Kotzman@TimKotzman
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What a year it’s been…7000 btc and counting. More to come!
American Bitcoin@ABTC
One year ago, American Bitcoin launched with a conviction: To Build America's Bitcoin Infrastructure Backbone. Today, that conviction is stronger than ever. We want to thank our shareholders and everyone who has supported us along this journey. We wouldn't be here without you. Thank you for believing in $ABTC. The best is yet to come.
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We’re making it easier for millions of businesses to accept bitcoin.
Starting today, eligible U.S. @Square sellers will begin having Bitcoin payments automatically enabled.
Sellers who accept bitcoin will receive USD as default.
This is how bitcoin as everyday money begins.
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Matt Prusak retweetledi
Matt Prusak retweetledi
I took Karpathy's Autoresearch concept and adapted it into AutoPredict: a research framework for evaluating, backtesting, and iteratively improving prediction market trading agents
AutoPredict evaluates agents on
- forecast quality
- calibration
- execution (slippage, liquidity, and fills)
- drawdown and risk adjusted returns
It also supports domain specialists for weather, finance, and politics under a shared evaluation harness
This framework is NOT for building agents but for agent improvement via a evaluation + mutation + selection loop
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There’s this quiet category forming that nobody really wants to name because it sounds like a personal failure when it’s actually structural. HELNO (high expectations, low-specialization, no opportunities) as a kind of default setting. High expectations baked in from two decades of “follow your passion” plus credential inflation, low-specialization because the economy kept rewarding generalists right up until the moment it didn’t, and then no real opportunities in the sense that matters, meaning roles that compound rather than just absorb time.
What’s unsettling is how cleanly it maps onto the current automation curve. The middle layer of cognition is getting eaten first, not the factory floor this time but the junior analyst, the associate, the “smart but not yet sharp” cohort. Which was always the apprenticeship layer. So you end up with a generation that was promised a ladder and instead got a trapdoor. And the response oscillates between nihilism and overproduction of identity, personal brand as a substitute for institutional role. Everyone performing optionality because committing to a path feels like locking yourself into obsolescence.
It also explains some of the weird cultural energy right now. The fixation on leverage, on asymmetric bets, on finding some angle that escapes the grind entirely. Rational, in a way. If the system no longer reliably converts effort into upward mobility, then the only coherent strategy is to look for discontinuities. The tragedy is that most people are not actually positioned to capture those, so you get this ambient sense of people waiting for their real life to start while quietly suspecting it already has.
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Matt Prusak retweetledi
Matt Prusak retweetledi
Over the past decade, Bitcoin didn’t just compete with other asset classes -- $BTC redefined the benchmark
Bloomberg's recent reporting reinforces what the data has been signaling for years @business @markets @crypto
What would $100 invested in Bitcoin ~10 years ago be worth today? Let's talk about it 👇📈🚀
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Your grandparents had grandparents. They had grandparents. Somewhere back there, someone got on a boat, or didn't. Someone changed their name, or had it changed for them. Someone is buried in a cemetery you've never heard of in a country you've never been to.
Most families lose track after two generations.
I used AI to push mine back nine.
One session with @karpathy's autoresearch pattern: over 100 organized research files. It found a 1940 Norwegian emigrant history with my ancestors in it. Resolved a maiden name question that confused my family for 70 years. Identified relatives no one alive knew existed.
The method is simple: set a goal, measure progress, verify against real records, repeat. The AI searches public archives, cross-references birth certificates against cemetery records against church books, and logs everything it finds (and everything it doesn't).
Open sourced the whole toolkit. Prompts that do the research for you, archive guides for 20+ countries, starter templates, even a framework for making sense of DNA results.
If you have a box of old photos and unanswered questions, this is where to start.
github.com/mattprusak/aut…
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