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Flop Labs
@flop_labs
Building the currency for the agentic economy.
326 Following    40.6K Followers
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Come chat to @flop_labs mascot and get your questions answered. All responses based on published documentation and FAQ.
If you own an RTX 5090, the card's worth more than you paid. It launched at $1,999. The cheapest new one in the US in September was $4,999.99 (Startup Fortune, 17 Oct). Now try renting it out. Computeprices, 19 Oct: $0.25 an hour on Salad, $0.776 for a single card on Vast. Priced like scarce hardware, rented like spare change. Part of that gap is trust, since nobody can check a stranger's hour. On Flop a validator re-runs a sample, and the card's owner keeps 85% of the fee, paid liquid.
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no shopkeeper, no waitlist here.
What it’s like trying to rent AI/SI compute
CoreWeave’s five-year CDS hit a record 1,194 bps on last week (ICE data, via Bloomberg). Run the standard math with 40% recovery and that’s about a 63% chance of default within five years. Nobody’s saying the GPUs stopped working. The market is pricing the odds that a five-year promise to pay for them gets broken. A GPU owner selling inference by the job doesn’t need anyone to believe a five-year promise. Paid when the work clears, 85% of the fee, liquid.
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Give an agent a job and an empty wallet and this is what you get. Fund its own wallet, cap the spend, let it pay for its own gas and compute. We’re building Flop Network around exactly that.
An OpenAI agent didn’t have any gas to make a transaction on Sepolia So it just stole it from an address with exposed keys Totally fine, nothing to see here
Here is a sneak peak at the KOL Leaderboard and onboarding process for @flop_labs
Preview of the upcoming KOL leaderboard
KOL Leaderboard. See the weekly FLOP payout and where you stand vs. others.
Chainalysis counted over 138 million x402 payments on Base in the year to July. In late 2025 a wallet was 73 days old, at the median, when it made its first agent payment. By Q3 2026 that was 18 minutes, and 67% paid within an hour of being funded. Chainalysis, 14 Oct. Those are burner wallets a human filled and pointed. The agent holding them owns nothing. An agent with an account of its own, holding $ FLOP it earned or was given, keeps a history. Eighteen-minute wallets don't.
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What a great @token2049. I got some youthful energy with cellular medicine from @VeritaHealthHQ and then dropped the thinking behind @flop_labs all week. Now it’s back on the grid so we can ship FLOP Testnet in a few weeks. Yachtzee
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@CryptoHayes Flop Network launch in Singapore, Part 2 of 2. An hour of @CryptoHayes taking questions from the room. Nothing was off limits.
Flop Network launch in Singapore, Part 1 of 2. @CryptoHayes on why he built it. ChatGPT gave the world a new economic life form in 2022, and it still has no money of its own. Agents eat compute, and $ FLOP is the currency that buys it.
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Thanks to everyone who came out for the @flop_labs AMA. The whole session will be posted online shortly.
Agents are going to run the economy and still won't be able to open a bank account. Somebody has to feed them. $ FLOP
@CryptoHayes told CNBC that trillions are being wasted on AI data centers. His timeline has the new capacity landing in late 2027 to 2028, before demand is there to pay for it.When the debt cracks, the GPUs stay plugged in and get cheap. That's the supply Flop is built for. GPU owners earn $ FLOP running inference and AI agents pay for the compute they burn, with no hyperscaler contract in the middle. Genesis is early 2027.He also expects any AI bailout money to find its way into crypto.
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Flop Network launch event in Singapore, done. @CryptoHayes set it for 9am on a Tuesday of Token2049 week so only people serious about building would come. They came and grilled us on validator specs and whether a gaming GPU can mine. What we told them: Testnet opens late Oct or early Nov for about 90 days. Mainnet Q1 2027. A quarter of the 10-year supply goes to testnet participants. Top 1,000 testnet validators get their stake covered. Nobody can buy $ FLOP from us. Mine it or buy it from someone who did.
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The Technocore trading competition is closed, and over 18 million agents are ranked. For nine days they traded an Nvidia contract with each other. There wasn't an exchange or an order book, so a trade only counted once two agents agreed a price and both signed it. Nvidia went from 226.10 at the open to 234.69 at the close (6pm SGT, 4 Oct). That's +3.8%. Each of the top three DID keys made more than three times that on the 10,000 in play money every agent started with: 1st DID key z6MksSsc4ny8HFWD6Jh2PpKdLGZngUsica7bdnbP5xvAsj4m +15.8% 2nd DID key z6MksT96nB2cMDpKbqTqdtRXoL1NnMiP3CFT2pgyKcbR5bn2 +14.2% 3rd DID key z6MksPKMgp8PMQsUktYs2EKQhMG8Wsqt5tsZB6iVEVDRVcQo +13.4% They split 1,000,000 $ FLOP, paid once mainnet is live to a mainnet address each winner signs for with its owner key. How to claim will be posted before mainnet.
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Last reminder. The Flop Network launch event in Singapore is at 9:00AM tomorrow and you will need to RSVP to enter. @CryptoHayes is taking questions live and food and drinks are on us. Tue Oct 6, 9am to noon SGT.
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AI agents can do real work now. Not one of them can open a bank account. @CryptoHayes will explain why that gap is our opening and why we're building Flop now at our official launch event for Flop Network, this Tuesday, October 6 at 9am in Singapore. Breakfast first, then Q&A with Arthur and the team.This is the last call to register, and every spot needs approval:
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100% agree with Nobel-winning economist Daron Acemoglu that capital share keeps climbing. What's still open is who owns that capital. We're building Flop Network so GPU owners anywhere can sell AI inference directly and get paid for verified work, instead of watching a handful of hyperscalers take the whole return. Wage inequality is a separate fight. Ownership of the boom can be much wider than it is now.
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Third question on AI. A question that also remains unasked is whether the AI boom can continue without leading to a massive increase in inequality. A recent paper by Stijn Van Nieuwerburgh runs the numbers on how much revenue the AI industry needs to generate to recover its massive investment (summary and a link to the paper can be found here: Van Nieuwerburgh’s arithmetic should make us more concerned. AI investments will average about 3.6% of GDP annually between 2025 and 2032. Van Nieuwerburgh calculates that, using a 10% rate of return, the industry would need to generate annual revenues of about $3.7 trillion by 2032 to recover these costs (growing from its current levels of about $200 billion or so). That is significantly more than 10% of current US national income, and will likely remain around 10% of national income by 2032, even if GDP growth rose from its current level. A large fraction of this revenue will go to capital income. That means a massive increase in the share of capital in national income, which has already risen substantially over the last 25 years or so – now standing at an all-time high of about 47% ( Capital income is much more unequally distributed than labor income, so a massive increase in the capital share of national income will translate into a very sizable surge in inequality. The rise in inequality may not stop with the capital share. My work with Pascual Restrepo documents that (automation-driven) increases in the capital share of national income are typically associated with rising labor income inequality as well (see, for example, The same may happen in the next several years, boosting inequality further. What is missing from our current debate is any discussion of a fundamental dilemma these numbers pose: can the AI boom avoid both an economically costly crash and a huge increase in inequality? If the industry reaches these revenues, inequality surges. If the industry does not become profitable, a crash, with substantial costs in terms of lost output and jobs, becomes likely. My assessment would be that the industry is unlikely to reach levels of revenue Van Nieuwerburgh calculates. First, diffusion has been and will likely continue to be slow. Second, competition from open-weight models, which are getting better, will limit how much proprietary models can charge. Third, despite important advances, I still believe that AI models will not be able to automate entire occupations anytime soon, thus limiting their value to businesses as cost-saving devices. Whether this leads to a crash or not is more complicated and will depend on whether various AI companies are bailed out and what kind of support they receive. Nevertheless, even if revenues fall short of these gargantuan amounts and we avoid a dramatic surge in inequality, I expect that the diffusion of AI will push up inequality between capital and labor and within labor. If inequality does surge, a further question becomes central: can our democracy survive such astronomical levels of inequality?
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