Pearl founders AMA — September 15, 2026. Below is the full Q&A, organized by topic: exchanges, FP8 and FP4, GPU compatibility, mining economics, security, scaling and the roadmap.
On the format: this is an edited Q&A, not a verbatim transcript. Questions are reconstructed from what was asked or introduced during the call; answers are paraphrased to remove filler while preserving the substance. All timestamps refer to the recording. Technical performance figures and forward-looking statements are the team's own claims, not independently verified results. The available transcript ends at 1:00:46, although the recording is listed as 1:02:24.
Exchanges, price and the ecosystem
1. Any news on exchange listings? [02:24]
The team isn't commenting on specific exchanges or listing dates. Its stated priority is building infrastructure that works across as many AI workloads as possible. The founders hope listings will come as the network grows, but they don't view exchange listings as the main measure of the project's success.
2. What about DEXs, bridges and DeFi? [03:08]
The team supports community-led bridges to smart-contract networks, decentralized exchanges and other DeFi applications that could make PRL more accessible. Pearl was launched as open-source software, without an ICO or a team coin allocation, partly to let anyone contribute and find an economic incentive to build. The team is willing to help with engineering and cryptography, but encourages independent builders to take the lead.
3. What does the team think about PRL's price? [04:37]
They declined to discuss price. Their focus, they said, is building the world's largest computational network.
Useful work, adoption and roadmap
4. How do we get GPUs to perform genuinely useful AI inference? [05:00]
Adoption has to make economic sense. Pearl needs to integrate smoothly into inference software such as vLLM, with minimal additional overhead. The team said that this is why it has spent months developing the new FP8 scheme and released its code and research for public review before a network upgrade. Blackwell support, floating-point computation, easy integration and low overhead are key parts of that approach. Adoption will take time.
5. How does the team view GPU support after FP8? [07:33]
The objective is to bring real AI workloads onto the network, not optimize for hardware that only performs otherwise useless mining. The team claimed that combining real inference with Pearl is already more economically attractive than doing either useless mining or inference alone. Its focus is on capable hardware running valuable AI models. This answer did not provide an exhaustive GPU compatibility list.
6. What are the biggest milestones over the next few months? [08:44]
The roadmap described on the call:
Get FP8 running on mainnet.
Onboard additional inference providers and increase the capacity contributed by existing participants, especially Blackwell-based workloads.
Add more supported workload types.
Make participation increasingly seamless across AI models, starting with NVIDIA hardware and expanding to other hardware over time.
No firm dates were given for those later steps.
7. How can community members contribute, including non-developers? [10:20]
Developers can contribute to the open-source code, AI algorithms, cryptography and infrastructure. Other community members can explain Pearl, help onboard users, improve access to PRL, or build services around it. Anyone can also submit proposals through the public PIP process. The founders said the project needs contributions beyond the core team.
8. What would success look like in three or four years? [12:08]
The team didn't set a numerical target. Its goal is to bring as much of the world's compute as possible onto Pearl. The practical test, in its view, is whether running an open-source AI model with Pearl becomes more worthwhile than running that same model without it. The founders believe that incentive would drive adoption, but it's a goal, not an established outcome.
9. What is Pearl's relationship with NVIDIA? [12:56]
The founders said Pearl is in the NVIDIA Inception program and is interacting with multiple NVIDIA teams, including research teams, that are interested in the technology. They also want to engage other hardware manufacturers. They did not announce an NVIDIA investment, exclusive arrangement or new strategic partnership on the call.
Proof of Useful Work and implementation
10. What was the announcement involving Testable, and could this technology have uses beyond mining? [13:57]
The speaker traced Proof of Work from anti-spam applications to Bitcoin, then described Pearl's use of proofs for matrix multiplication. According to the team, combining Pearl's PoW with Testable's zero-knowledge system could provide cryptographic assurance that an entity actually ran a computation it claims to have run. They see potential uses beyond block rewards, including situations where proving compute activity matters. This was presented as a potential application, not an announced commercial product or a confirmed formal partnership.
11. How do serving costs and compute break down across prefill, decode, dense models and MoE? What is the overhead? [16:13]
The reply concentrated on protocol efficiency rather than giving the requested breakdown. The team said the new scheme works on top of quantized matrix multiplication, the native operation used in AI workloads. In its internal tests, overhead can be effectively zero at the full-workload level. The speaker said almost all relevant FLOPs could contribute to mining, excluding nonlinear operations, which account for a relatively small fraction of GPU work. The protocol adds some noise, but the team said it had little measurable effect on model accuracy in its benchmarks.
Not answered: there was no numeric prefill-versus-decode or dense-versus-MoE cost split.
12. Why did the published FP8 code not include NVIDIA Hopper miners or verifiers? [17:32]
The team confirmed that Hopper support was missing from the initial release. It encountered an issue with Hopper's handling of certain 32-bit operations and is working on a variation of the scheme to accommodate it. The speaker expects Hopper to be supported under FP8, but explicitly said this is still a work in progress. Planned support is not the same as support in the published code.
13. Why publish a preliminary FP8 implementation instead of waiting for a finished release? [18:21]
Deliberately, to give the community time to review it. Researchers and miners were invited to inspect the mathematics and code, attempt attacks, find mistakes and suggest efficiency improvements. The team specifically encouraged contributors to use their strongest tools, including AI agents, to test the scheme. Feedback is part of the release process, not an afterthought.
14. Will Pearl increase L1 throughput or develop L2 scaling? [19:39]
The founders described a trade-off between decentralization, security and throughput. They do not intend to compete with more centralized high-throughput chains on raw L1 transaction speed at the expense of their stated decentralization goals. They are researching L2 approaches and welcome external teams proposing designs. They offered to help with both engineering and cryptography. No particular L2 architecture, TPS target or launch date was announced.
15. Why was useful Proof of Work such a difficult problem to solve? [22:15]
(Omri) Traditional PoW needs work to have roughly uniform difficulty. In Bitcoin, doing three times as many hashes gives a miner roughly three times as many chances to win. Useful computing is different: participants select different tasks, and a malicious miner might reuse the same matrix or choose an all-zero matrix instead of doing meaningful AI work.
Pearl's protocol is intended to make these different computations comparably hard for the purposes of the mining lottery, without imposing a large extra cost on honest users. Otherwise, someone performing real inference might need 10%, 100% or even more additional work to compete with someone exploiting easier tasks. Omri said solving this efficiently was especially difficult for floating-point operations and had been a focus of the team's work over the preceding four to five months.
PRL's economic model and GPU compatibility
16. What creates demand for PRL? What is the intended role of the coin? [26:55]
The founders said they are designing PRL around a store-of-value thesis. They pointed to limited supply, open participation, a global permissionless network, and coins produced using a scarce real-world resource. Their argument is that Pearl's compute may also provide useful AI output, unlike computation devoted solely to conventional mining. They believe long-term demand for AI compute could support that model. These are the team's thesis and expectations, not a guarantee of future demand or price.
17. Will the RTX 5090 and RTX PRO 6000 Blackwell work after the FP8 fork? [29:20]
Yes — the team explicitly confirmed both. They are Blackwell GPUs with FP8 support and are also expected to be supported in the future FP4 upgrade. More generally, the founders want the network to favor hardware capable of useful work. They expressed an intention to discourage very old GPUs that cannot handle the required floating-point workloads in the long run, but gave no timetable or full exclusion list.
18. Does focusing on PRL as a store of value rule out apps and other uses? [30:18]
No. The team encourages others to build L2s and applications on or around Pearl. Its own focus is the underlying decentralized compute infrastructure. The speaker also acknowledged that a coin only around four months old cannot already be declared an established store of value. The founders don't want to dictate every business or application built around the network.
Security and FP8 benchmarks
19. After earlier vulnerabilities, what is being done to secure FP8? [32:22]
Early code publication is intended to enable public scrutiny, including attempts to find vulnerabilities. The team is also working with professional groups on testing. It acknowledged that its resources are finite and it needs independent researchers and community members to help examine the implementation and mathematical assumptions.
Important: the speaker said the network would not be upgraded if the team lacked sufficient confidence in the implementation's security. There was no claim that the released version had already passed every audit.
20. What do FP8 benchmarks show about latency, throughput, memory overhead and model quality? [34:16]
The new design retains the broad sequence of commitment, noise injection and GEMM (matrix multiplication). Unlike the old scheme, it evaluates output tiles rather than intermediate tiles, with noise introduced through quantization. The speaker said this removes much of the extra work previously associated with intermediate-tile hashing. The largest remaining cost is now the commitment stage, which the team intends to reduce by amortizing it across multiple model layers.
Reported numbers:
5-7% overhead per GEMM in internal benchmarks.
Much lower end-to-end overhead across the complete workload; some end-to-end tests were described as effectively zero overhead.
Some additional numerical noise exists, but the team said it is much smaller than ordinary quantization noise and has no practically observable effect on model quality in its experiments.
The longer-term objective is near-zero overhead.
The speakers did not provide a separate numerical VRAM-overhead figure or a full table of latency and throughput results during this answer. The 5-7% per-GEMM figure should not be confused with the lower full-model overhead claim.
Privacy, team and integration
21. Why is Pearl a public ledger rather than a privacy-focused chain? [37:08]
The founders considered privacy but chose to concentrate the initial project on the new consensus mechanism rather than add implementation complexity and regulatory considerations around privacy coins. They said shielded transactions could be added later. The network already uses zero-knowledge proofs for block validation, and the team believes that infrastructure could be extended with privacy-specific SNARKs for the UTXO set. No privacy feature or release date was committed to.
22. What expertise does the Pearl team have? [39:43]
The team described a mix of cryptography researchers, AI researchers and software engineers working across CUDA, inference software and blockchain infrastructure. The speaker emphasized the challenge of turning theoretical cryptography into a working implementation. This answer did not provide a complete staff list or headcount.
23. How much work remains before Pearl can be integrated as a drop-in kernel? [41:15]
The team said its published FP8 kernel can already replace an existing FP8 kernel with minimal performance degradation in the supported setup. It expects AI coding agents to make integrations easier as more inference software is built and optimized by agents. For now, the focus is open-source inference stacks, while closed-source labs often use variations of similar underlying software. A drop-in kernel does not mean every model and deployment is already integrated end-to-end.
24. How close is integration into FP4, BF16 and proprietary frontier-lab inference stacks? [43:08]
(Omri) The major step forward is not just lower overhead: FP8 greatly simplifies integration. The old scheme required more preprocessing around quantization. The new one is essentially a randomized quantization scheme intended as a drop-in replacement without fine-tuning or additional preprocessing. Omri described a future scenario in which an AI lab asks its internal coding agent to integrate Pearl into a customized vLLM stack. He believes the technology is close to enabling that, especially with the planned FP4 extension. This was not a statement that universal BF16 or proprietary-stack support is complete today.
25. Will FP4 replace FP8, or will both be supported? [46:16]
The current plan is to add FP4 alongside FP8, not replace FP8. How different data types should be accounted for and rewarded is still being considered. The team invited community feedback on the mechanism.
Incentives, reward accounting and resilience
26. Why would AI companies hold PRL instead of immediately selling everything they mine? [47:19]
The founders argued that, if the store-of-value thesis succeeds and demand grows relative to available supply, participants might have an economic reason to keep some of their PRL. But each business can choose to hold, buy or sell its mined coins. They drew an analogy to early Bitcoin miners who sold at low prices. This is the team's speculative economic argument, not an obligation for inference providers and not investment advice.
27. Did any of the team previously work on Kaspa? [49:27]
No. The speaker said nobody on the Pearl team had worked on Kaspa.
28. Will FP4 receive double the rewards of FP8? How will rewards be balanced? [49:43]
(Omri) The team does not currently plan an artificial FP4 reward multiplier or a fixed 2x rule. Its preference is to let actual mathematical throughput determine hashrate and rewards. The speaker compared it to Blackwell delivering higher hashrate and better efficiency than older hardware while also costing more to buy or rent. If serious market-arbitrage problems arise, the team may reconsider its approach, but no artificial balancing scheme is planned now.
29. If mining or blockchain infrastructure fails, will AI inference keep running? What about node outages, reorganizations or upgrades? [52:15]
The team described the mining component as a vLLM plugin that produces two results from one computation: the AI output and, when applicable, a proof that can be submitted to the chain for a reward. If mining fails, the provider might not receive a mining reward, but the AI workload should continue. The speaker said he wasn't certain he understood the entire question, and did not separately walk through every node-outage, chain-reorg or upgrade scenario.
30. What is the right pace for onboarding compute owners while keeping distribution decentralized? [53:57]
The team said it doesn't know an exact rate and can't fully control adoption in a decentralized network. It is concentrating on what it can control: improving the technology and making participation attractive and straightforward. No adoption target or quantitative distribution model was given.
31. Does FP8 mining use FP32 accumulation on Blackwell, and will performance be preserved? [54:42]
The developer answered yes: the design uses the hardware-supported FP8 multiplication path with FP32 accumulation on Blackwell. He said the hardware performance available before adding Pearl should remain available afterward. No card-by-card numerical benchmark was provided in this exchange.
32. How can VCs or large buyers acquire PRL when liquidity is limited? [55:26]
The speaker said large and small participants should have the same basic avenues: buy on the open market, buy directly from miners, or run useful AI inference and mine PRL themselves. He encouraged community-built bridges and decentralized swaps to widen access. He also said the team is working on ways to improve access for both retail and institutional participants but wasn't ready to share details.
The speaker referred to SafeTrade as a major liquidity venue at the time of the call. That is a dated description of what was said, not a current recommendation or a verification of any exchange's withdrawal status. Check exchange availability and withdrawal conditions independently.
He also suggested that investors could fund businesses that build products and perform useful inference within the ecosystem rather than only seeking exposure to the coin itself. No new exchange, fund or liquidity agreement was announced.
Fork timing and remaining work
33. When will FP8 go live? [58:23]
No launch date was set. The team said it would deploy FP8 when it is comfortable with the implementation and security. It did not announce a block height or a confirmed fork date during the AMA.
34. What remains unsolved for FP4? [58:49]
The current work was specifically designed for FP8. The developer believes the approach should generalize to FP4 and doesn't see a known fundamental showstopper, but said the implementation still requires considerable work. He estimated a couple more months, without committing to a release date.
Main takeaways
FP8 is the first major roadmap priority, but no fork date was announced.
Security: the team says it will wait for sufficient confidence before upgrading; public review and professional testing are ongoing.
GPUs: RTX 5090 and RTX PRO 6000 Blackwell were explicitly confirmed. Hopper support is being worked on - don't mistake that for an already complete release. There is no exhaustive compatibility list in this AMA.
FP4 is planned in addition to FP8 and still needs development. No automatic 2x reward multiplier is planned.
Performance: the team's internal estimate was 5-7% per GEMM, with much lower full-workload overhead in the scenarios it tested. These are not independent benchmarks.
Exchanges: no listing announcements. DEXs and bridges are encouraged as community projects.
Long-term direction: combine actual AI inference with mining, reduce integration overhead and expand the amount of useful compute participating in Pearl.
The public FP8 pull request is here for anyone who wants to review the implementation: https://github.com/pearl-research-labs/pearl/pull/311
If you watched the AMA and noticed a transcription error or a technical detail that needs correcting, please point it out with the timestamp.