🇸🇬 SINGAPORE | 11th June 🇸🇬
We hosted one of the most hardcore Whitepaper Reading Club sessions in Singapore last Thursday, where we were deciphering:
🧠 Cryptography’s XLA Moment: Why ZK may need a compiler, not another proving system 🧠
This session was led by
@jumanzi_dev from Fractalyze, the team building “the PyTorch for ZK” — a Python-first compiler stack for Zero-Knowledge.
Big shoutout to our:
- Discussion: Jooman & Batzorig from Fractalyze for working on this amazing project. They are deeply passionate about what they do and I feel I've learned so much.
- Thank you to my partner in crime
@BrianSeong who made this session possible.
- Great to see
@ulandaoon @kowei1995 @9oelM and welcome
@tomespel (welcome back to ZK)
🙏 Appreciate
@kimmi from
@SuperteamSG, for being there for us :)
📝 For more information:
- Fractalyze:
fractalyze.io
- Event summary:
docs.google.com/document/d/1…
📆 Upcoming events:
luma.com/whitepaper
‼️ THE EVENT NOTES > TL;DR ‼️
(1) ZK’s Problem: zkVM/proving teams like
@SuccinctLabs,
@RiscZero,
@ziskvm, OpenVM, Plonky3 (
@0xPolygon), Jolt from
@a16zcrypto, and Binius from
@IrreducibleHW are all fighting the same hard problem: turning Rust/CUDA, memory layout, GPU execution, and field arithmetic into production-level proving performance.
(2) Fractalyze's Solution: Build ZK proving systems in high-level Python, then let a cryptography-native compiler handle optimization underneath.
(3) Zorch: Fractalyze’s stack aims to become the “PyTorch for ZK” — making proving systems easier to write, test, optimize, package, and deploy.
(4) The XLA Analogy: ML scaled as researchers stopped hand-writing kernels.
@OpenXLA / MLIR helped high-level models compile into efficient hardware execution.
(5) Cryptography Needs the Same Layer: ZK, FHE, MPC, post-quantum crypto, and formal verification all need better compiler infrastructure if they are going to become real products they are going to become real products — see
@zama,
@TACEO_IO,
@rv_inc, and
@NethermindSec.
(6) Not Just Developer UX: The goal is not only abstraction. The deeper claim is that compiler automation can beat hand-written optimization by reasoning across the whole computation graph.
(7) Hardware Portability: Today’s ZK systems are often tied to specific
@nvidia GPU assumptions. A compiler layer could make cryptographic workloads easier to move across CPU, GPU, FPGA, and future crypto-specific hardware — a direction also explored by teams like
@Ingo_zk / ICICLE and
@cysic_xyz.
(8) Ethereum: This matters for real-time block proving, lighter validation, larger block capacity, and the long-term vision of
@ethereumfndn becoming easier to verify.
(9) Bigger Than ZK: The real question is whether advanced cryptography can move from research papers and hand-built systems into reusable infrastructure — the same way AI did.
🧠QUESTIONS:
(i) Is the next major ZK breakthrough a new proving system — or the compiler layer underneath all of them?
(ii) If Ethereum itself becomes ZK-powered, what is the future role of ZK rollups?
(iii) Can cryptography become a profitable product category, or is it closer to public-good infrastructure?
(iv) Will AI-generated code make formal verification and verifiable compute much more important?
(v) What happens when cryptography gets 100x or 1000x cheaper to run?
#ZK #Cryptography #Ethereum #MLIR #XLA #VerifiableCompute #Fractalyze #WhitepaperReadingClub