ChainFeeds published a new edition of its Daily research briefing on Sept. 11, combining a market and policy news digest with five featured reads on meme launchpads, compute markets, zero-knowledge infrastructure, tokenized stocks, and Ethereum governance.
Top headlines in the Sept. 11 briefing
The newsletter’s headline items included a revised CLARITY Act unveiled by Republican lawmakers that would require non-decentralized DeFi protocols to register with the U.S. Commodity Futures Trading Commission, or CFTC; Polymarket’s hiring of its first chief financial officer as it works toward a $1 billion fundraising round; FLOP’s updated tokenomics, which project total supply at 18.1 billion tokens in year 10 and a long-run annual inflation rate of 0.5%; a planned $100 million investment by Nasdaq Ventures into Kraken parent Payward at a $21 billion valuation; and Bitrace’s statement that Fulilai Guarantee has begun removing money-laundering merchants, possibly in response to sanctions from the U.S. Treasury’s Office of Foreign Assets Control, or OFAC.
Meme launchpads on Robinhood Chain
The first featured research piece looked at competition among meme launchpads on Robinhood Chain. According to the article, the network is built on the Arbitrum Orbit stack, its Layer 2 gas costs are already very low, and wallet users receive a 90-day gas subsidy. That combination has sharply reduced trading friction and fueled rapid growth in token launch platforms.
Pons, launched on July 13, was highlighted as the leading example. Its model is simple: a 1% trading fee, with 70% going directly back to token creators and 30% retained by the protocol. Of the protocol’s share, 80% is used to buy back and burn the Pons platform token. The piece said that in less than two months, 30% of total supply, roughly 300 million tokens, had been sent to a burn address. Pons climbed from zero to nearly $1, and its market capitalization at one point moved above $600 million. It took fewer than 50 days for the protocol to rise from launch to fourth place in network-wide fee generation, while Pump.fun took more than two years to reach a comparable position.
On Sept. 3, Pons generated more than $6 million in daily protocol fees, ranking fourth across the market behind only Tether, Uniswap, and Circle. One day earlier, on Sept. 2, Robinhood Chain posted $4.01 million in daily chain revenue, while Solana-wide chain revenue on the same day was $81,700.
The report also pointed to a platform called LONG, which took a different path. Instead of pairing new tokens with ETH, LONG forms liquidity pools against stock-mapped tokens such as Nvidia and the S&P 500. Its tokenized equity products at one point reached more than $425 million in daily trading volume, equal to 20% of total stock TVL on the chain. It also packaged 3x leveraged stock tokens. Robinhood Chain, the article said, had listed more than 190 stock tokens, and peak daily DEX volume reached $3.7 billion, approaching Solana.
Pons’ V2 upgrade locks post-graduation liquidity into Uniswap V4 pools. The article noted that Uniswap’s own Pools.trade later launched on the same underlying liquidity rails. In practice, it said, no matter which front end wins, capital ultimately settles in Uniswap pools.
The same piece also raised questions about how much of the activity is organic. Because the chain is only two months old and Layer 2 gas costs are minimal, creators can wash trade at very low cost, pay 1%, and still reclaim 70% of the fees. The article said it remains unclear how much of the eye-catching volume reflects real demand.
It then compared the pattern with other ecosystems. Pump.fun dominated Solana for two years before facing pressure from BONK.fun and LaunchLab. On Robinhood Chain, Pons had barely established itself for two months before Pools.trade arrived with zero-fee competition. In that framing, token issuance rights are turning into free public infrastructure.
At the token level, the article listed several examples: Pons has burned 30% of its token supply; Zora’s token rose 800% in July; Virtuals continues to buy back and burn tokens with protocol revenue, though its 18,000 AI agents rely on inference engines running on its own servers; and Clanker uses 60% of revenue for buybacks. The report argued that the value of these platform tokens is tied directly to a single question: how long a platform can keep producing fees.
Jay Yu on benchmark pricing for compute
The second featured item was an English-language thread by Jay Yu on who could become the “CME” of compute markets. His argument started from an analogy with U.S. power markets.
Power, like compute, is heterogeneous, time-sensitive, and shaped by hubs. Since the 1990s, the U.S. power system has gradually been privatized, with generation, transmission, and distribution separated from the old vertically integrated structure. Independent grid operators came to serve different regions, and major interconnections such as PJM-West, ERCOT North, and CAISO SP15 eventually became tradable commodities and market benchmarks on ICE and CME.
At the physical layer, Yu described the power market as a “grid-operator-node” structure. Regional transmission organizations and independent system operators manage sets of local substations, and prices at each node are set dynamically through locational marginal pricing, or LMP, based on generation dispatch, user demand, transmission costs, and power flows. The most liquid and largest hub prices become the reference points for the asset class.
He suggested that compute could develop along a similar path. In physical settlement terms, the closest equivalent to “grid-operator-node” may be “hardware-provider-cluster.” Different chip classes, including H100, H200, B200, and B300, could develop into separate but linked pricing benchmarks. At the provider layer, AWS, Nebius, CoreWeave, SF Compute, and Ornn could offer distinct prices for GPU clusters across time, geography, and SKU.
Yu wrote that the markets most likely to supply the benchmark index for compute may resemble power and oil in one key respect: they will be the venues with the deepest physical delivery infrastructure. At the same time, actual compute consumers, including new cloud providers and AI companies, may not hedge directly on exchanges. Instead, they may trade specific GPU SKUs through brokers and OTC desks, leaving intermediaries to manage inventory and basis risk between standardized compute products and real hardware.
He also broke the inference compute stack into three layers. First is the neocloud layer, including companies such as Nebius and CoreWeave that operate physical data centers and act as sellers in the GPU market. Second is the on-tap layer, which includes developer platforms such as Fireworks and Baseten. These firms turn bare-metal GPU environments into full compute environments where developers can run tasks directly or obtain inference tokens, making them buyers of GPUs. Third is the application layer, including products such as Cursor, Perplexity, and Rime. These companies buy tokens from inference platforms in order to provide final services to users and enterprises, while the tokens themselves ultimately depend on GPUs.
As a rule of thumb for margin distribution, Yu wrote that when an AI application at the top of the stack spends $100 on tokens, about $45 flows to the on-tap layer, about $50 flows to the neocloud or GPU layer, and the remaining $5 flows to routing layers such as OpenRouter.
a16z introduces Lattice Jolt
The third piece covered Andreessen Horowitz, or a16z, and its release of Lattice Jolt, a new version of the open-source zkVM Jolt. The architecture remains unchanged, but its cryptographic foundation has moved from elliptic curves to lattice cryptography.
a16z said the switch brings three changes at once. Jolt now has post-quantum security. Prover and verifier performance is 2x to 3x faster. And proof size is now the smallest among existing post-quantum zkVM systems, below 100 KB, with room for more compression later. Because proofs must be published onchain and transmitted across networks, smaller proofs reduce verification costs across use cases.
The firm said the same proving system can process billions of CPU cycles on GPUs and prove millions of cycles on smartphones. Developers do not need to hand-write specialist circuits and can work from ordinary programs instead.
The main reason for the speed gains is straightforward. The earlier elliptic-curve setup required Jolt to operate over a 256-bit field, while lattice cryptography can deliver similar security over a 128-bit field. Since a large share of the prover’s work consists of multiplying field elements, cutting the size of those numbers in half makes each multiplication several times faster.
The post gave several performance points. A Dory-based version of Jolt had already reached roughly 700,000 RISC-V cycles per second on a laptop, later improving to more than 1 million cycles per second. Lattice Jolt now proves more than 2 million cycles per second on the same machine. With Apple Metal GPU acceleration, it can exceed 10 million RV64IMAC cycles per second on a MacBook. In a16z’s telling, one version upgrade took Jolt on a MacBook from about 1 million cycles per second to more than 10 million.
The article also placed Lattice Jolt within a broader post-quantum research path. Hash-based SNARKs have long been treated as the main route to post-quantum security, and production deployments have mostly followed that line. But research on lattice SNARKs and lattice commitments has continued across projects including LaBRADOR, Greyhound, LatticeFold, SuperNeo, and Hachi. Lattice Jolt builds on that body of work by bringing a lattice-commitment layer into a high-performance zkVM architecture.
a16z compared the shift with what has happened in digital signatures and key exchange. Hash-based signatures are often seen as the more conservative option because the assumptions are simpler and older, yet real-world adoption has tilted toward lattice-based signatures because they are faster and shorter. The trend is even clearer in encryption and key exchange. The firm noted that NIST’s main key-establishment standard, ML-KEM, finalized in 2024, is already deployed by default in mainstream browsers, communication apps, and a large number of TLS connections across the internet.
Pump.fun opens custom quote assets for token launches
The fourth item focused on Pump.fun’s new custom-pairing feature. According to TechFlow, the platform formally opened the quote-asset field in its token launch form on the afternoon of Sept. 9. Users can now pair new tokens not only with standard crypto assets, but also with tokenized equities, wrapped BTC, wrapped ETH, silver, and gold.
Pump.fun named 20 U.S. stock quote assets in the first batch, including Boeing, Alibaba, Dell, Nvidia, and Tesla, with the tickers BA, BABA, DELL, NVDA, and TSLA. The platform supports 93 quote assets in total.
The article said the change does more than refresh the user interface. It alters the pricing anchor and trading rhythm of meme coins. A newly launched token is no longer driven only by community appetite; it can also inherit volatility from the underlying quote asset during U.S. market hours.
On the plumbing side, the report said the 20 stock tokens are issued by Backpack Securities through a compliant brokerage route. Investors receive assets backed by real equity ownership, dividend rights, and DTCC transfer processing. With those underlying assets in place, Sunrise uses Wormhole’s native token transfer, or NTT, framework to bring the stock tokens onto Solana and supply initial liquidity on day one. Pump.fun itself does not issue the stocks or conduct compliance review. Its role is to provide traffic and the bonding-curve launch mechanism.
The platform also adjusted incentives. Token creators can set a trading cut between 0.05% and 1%, and that fee is paid in the selected quote asset. The article gave a simple example: if a meme token is paired against NVDA, the creator receives tokenized Nvidia stock as trading fees when activity occurs in the pool. At the protocol level, 50% of revenue generated by custom pairs is routed into a programmatic buyback-and-burn contract for PUMP.
The report stressed that coin-stock pairing is not entirely new. Back in July, Robinhood Chain was already producing nearly $30 million in average daily DEX volume through meme trading based on tokenized stocks, briefly outperforming Solana’s on-platform activity. Even before that, Raydium had multiple unofficial meme pools linked to Nvidia. Pump.fun is now trying to consolidate that scattered coin-stock liquidity by using its own distribution advantage.
Still, the first day’s order book showed thinner liquidity than the platform’s core quote assets. Wrapped ETH had the deepest displayed liquidity at about $3.07 million. Nvidia showed about $1.13 million and Tesla about $830,000. Within 20 minutes of launch, the earliest custom pairs still avoided stocks and chose wrapped bitcoin, or wBTC, instead, with only a little more than $3,000 in reserves in a single pool.
The article ended on an open question. Backpack handles custody, redemption, and dividends for the stock tokens, while Pump.fun focuses on traffic and curves. What remains unclear is whether speculative traders will actually lock meme-coin liquidity into these stock-linked assets.
Ethereum’s August EIP pipeline
The fifth featured read came from Four Pillars and reviewed Ethereum’s EIP activity in August. Its main point was that the number of new proposals fell back to roughly the level seen two months earlier, while existing EIPs, especially core EIPs, continued to move forward.
In August, 10 new EIPs were proposed, down by 14 from the prior month. At the same time, 25 existing EIPs advanced to the next stage, and none were withdrawn.
Among the new core proposals, EIP-8321 would decouple RANDAO from BLS signatures by introducing a hash chain. EIP-8347 proposes migrating the current Merkle Patricia Trie, or MPT, state offline into PBT and switching to the new structure at a scheduled hard fork. EIP-8371 spreads blob data recovery work across the network. EIP-8390 proposes replacing sync committees with zero-knowledge finality proofs for future uses such as light clients. Four Pillars said these proposals point in the same direction: reducing Ethereum’s dependence on specific cryptographic schemes, high-performance nodes, and small groups of validators. At the application layer, ERC proposals were described as an effort to turn complex execution flows previously implemented by individual applications into standard interfaces reusable across contracts and services.
With organizational priorities becoming clearer, the Ethereum Foundation began pushing these ideas into testing and deployment preparation during August. On Aug. 17, the EF Protocol DevOps team launched the Platåberget testnet as a public environment for major Glamsterdam changes. The testnet integrates ePBS, block-level access lists, and gas repricing, while allowing solo stakers, DVT projects, and large operators to register their own validators and builders in order to test the new block-production flow.
That shift, the article said, marks Glamsterdam’s move away from a short-term, client-focused devnet toward an open validation stage involving the node operators and application developers who would actually be affected by the upgrade.
The Ethereum Foundation also started evaluating how the new scaling architecture could affect existing applications. On Aug. 24, EF Protocol Research, EthPandaOps, and the Specifications team applied the gas repricing plans in EIP-8037 and EIP-8038 to historical mainnet transactions. The result: most contracts were unaffected, but some applications that rely on current gas-limit assumptions could suffer failed execution or weaker performance.
The piece ended by discussing what Robinhood Chain says about the changing yardstick for Layer 2 success. Four Pillars argued that Robinhood moved existing users and financial products onto a proprietary chain, connected tokenized stocks to DeFi, and captured sequencer revenue. The chain itself has clearly succeeded in attracting assets and volume quickly after launch. Whether that success benefits Robinhood, Arbitrum, and Ethereum to the same degree is a separate question.
Its reasoning for launching a dedicated Layer 2 was described as simple. Robinhood can use Arbitrum and Ethereum technology while still securing its own blockspace and shaping transaction processing, fees, and regulatory controls around business needs. Because the chain publishes transaction data to Ethereum blobs and uses ETH as the gas asset, it can tap Ethereum’s data availability and finality without taking on the cost and operational burden of building a new Layer 1 from scratch. For enterprises, the article said, Layer 2s increasingly look less like a destination defined only by decentralization and more like customizable onchain infrastructure built around business functions, compliance requirements, and control.
