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Edition of Tuesday, March 31, 2026

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Two New Papers Sharply Lower Quantum Resource Estimates for Breaking Bitcoin Keys

Justin Drake reports two papers improving Shor's algorithm, one from Google Quantum AI and one from startup Oratomic. Google estimates about 1,000 logical qubits could break secp256k1 signatures, while Oratomic estimates about 26,000 atomic qubits could do so, both pending expert vetting.

Original post · 6 min read
Today is a monumentous day for quantum computing and cryptography. Two breakthrough papers just landed (links in next tweet). Both papers improve Shor's algorithm, infamous for cracking RSA and elliptic curve cryptography. The two results compound, optimising separate layers of the quantum stack. The results are shocking. I expect a narrative shift and a further R&D boost toward post-quantum cryptography.

The first paper is by Google Quantum AI. They tackle the (logical) Shor algorithm, tailoring it to crack Bitcoin and Ethereum signatures. The algorithm runs on ~1K logical qubits for the 256-bit elliptic curve secp256k1. Due to the low circuit depth, a fast superconducting computer would recover private keys in minutes. I'm grateful to have joined as a late paper co-author, in large part for the chance to interact with experts and the alpha gleaned from internal discussions.

The second paper is by a stealthy startup called Oratomic, with ex-Google and prominent Caltech faculty. Their starting point is Google's improvements to the logical quantum circuit. They then apply improvements at the physical layer, with tricks specific to neutral atom quantum computers. The result estimates that 26,000 atomic qubits are sufficient to break 256-bit elliptic curve signatures. This would be roughly a 40x improvement in physical qubit count over previous state-of-the-art. On the flip side, a single Shor run would take ~10 days due to the relatively slow speed of neutral atoms.

Below are my key takeaways. As a disclaimer, I am not a quantum expert. Time is needed for the results to be properly vetted. Based on my interactions with the team, I have faith the Google Quantum AI results are conservative. The Oratomic paper is much harder for me to assess, especially because of the use of more exotic qLDPC codes. I will take it with a grain of salt until the dust settles.

→ q-day: My confidence in q-day by 2032 has shot up significantly. IMO there's at least a 10% chance that by 2032 a quantum computer recovers a secp256k1 ECDSA private key from an exposed public key. While a cryptographically-relevant quantum computer (CRQC) before 2030 still feels unlikely, now is undoubtedly the time to start preparing.
→ censorship: The Google paper uses a zero-knowledge (ZK) proof to demonstrate the algorithm's existence without leaking actual optimisations. From now on, assume state-of-the-art algorithms will be censored. There may be self-censorship for moral or commercial reasons, or because of government pressure. A blackout in academic publications would be a tell-tale sign.
→ cracking time: A superconducting quantum computer, the type Google is building, could crack keys in minutes. This is because the optimised quantum circuit is just 100M Toffoli gates, which is surprisingly shallow. (Toffoli gates are hard because they require production of so-called "magic states".) Toffoli gates would consume ~10 microseconds on a superconducting platform, totalling ~1,000 sec of Shor runtime.
→ latency optimisations: Two latency optimisations bring key cracking time to single-digit minutes. The first parallelises computation across quantum devices. The second involves feeding the pubkey to the quantum computer mid-flight, after a generic setup phase.
→ fast- and slow-clock: At first approximation there are two families of quantum computers. The fast-clock flavour, which includes superconducting and photonic architectures, runs at roughly 100 kHz. The slow-clock flavour, which includes trapped ion and neutral atom architectures, runs roughly 1,000x slower (~100 Hz, or ~1 week to crack a single key).
→ qubit count: The size-optimised variant of the algorithm runs on 1,200 logical qubits. On a superconducting computer with surface code error correction that's roughly 500K physical qubits, a 400:1 physical-to-logical ratio. The surface code is conservative, assuming only four-way nearest-neighbour grid connectivity. It was demonstrated last year by Google on a real quantum computer.
→ future gains: Low-hanging fruit is still being picked, with at least one of the Google optimisations resulting from a surprisingly simple observation. Interestingly, AI was not (yet!) tasked to find optimisations. This was also the first time authors such as Craig Gidney attacked elliptic curves (as opposed to RSA). Shor logical qubit count could plausibly go under 1K soonish.
→ error correction: The physical-to-logical ratio for superconducting computers could go under 100:1. For superconducting computers that would be mean ~100K physical qubits for a CRQC, two orders of magnitude away from state of the art. Neutral atoms quantum computers are amenable to error correcting codes other than the surface code. While much slower to run, they can bring down the physical to logical qubit ratio closer to 10:1.
→ Bitcoin PoW: Commercially-viable Bitcoin PoW via Grover's algorithm is not happening any time soon. We're talking decades, possibly centuries away. This observation should help focus the discussion on ECDSA and Schnorr. (Side note: as unofficial Bitcoin security researcher, I still believe Bitcoin PoW is cooked due to the dwindling security budget.)
→ team quality: The folks at Google Quantum AI are the real deal. Craig Gidney (@CraigGidney) is arguably the world's top quantum circuit optimisooor. Just last year he squeezed 10x out of Shor for RSA, bringing the physical qubit count down from 10M to 1M. Special thanks to the Google team for patiently answering all my newb questions with detailed, fact-based answers. I was expecting some hype, but found none.
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Jack Dorsey and Sequoia Essay Traces Org Design From Roman Army to AI

From Hierarchy to Intelligence

Jack Dorsey's X article, published with Sequoia, traces the history of organizational hierarchy from the Roman legion through the Prussian General Staff. It argues AI can fundamentally rethink how companies coordinate, with speed as a compounding advantage.

Original post · 12 min read
X ArticleFrom Hierarchy to Intelligence
At Sequoia, we see that speed is the best predictor of start-up success. Most companies are focused on AI as a productivity enhancer. Few are focused on the potential of AI to change how we work together. Block is showing what it looks like to fundamentally rethink organization design, ultimately harnessing AI to increase speed as a compounding competitive advantage.
Two thousand years before the first corporate org chart, the Roman Army solved a problem that every large organization still faces: how do you coordinate thousands of people across vast distances with limited communication?
Their answer was a nested hierarchy with a consistent span of control at every level. The smallest unit was the contubernium, eight soldiers who shared a tent, equipment, and a mule, led by a decanus. Ten contubernia formed a century of eighty men under a centurion. Six centuries made a cohort. Ten cohorts made a legion of roughly 5,000. At each layer, a named commander held defined authority, aggregated information from below, and relayed decisions from above. The structure (8 → 80 → 480 → 5,000) was an information routing protocol built around a simple human limitation: a leader can effectively manage somewhere between three and eight people. The Romans discovered this through centuries of warfare. Even today, the US Army's hierarchical chain follows a similar pattern. We now call it "span of control," and it remains the governing constraint of every large organization on earth.
The next big change came from Prussia. After Napoleon's army destroyed the Prussian forces at the Battle of Jena in 1806, a group of reformers led by Scharnhorst and Gneisenau rebuilt the military around an uncomfortable truth: you cannot depend on individual genius at the top. You need a system. They created the General Staff, a dedicated class of trained officers whose job was not to fight but to plan operations, process information, and coordinate across units. Scharnhorst intended these staff officers to "support incompetent Generals, providing the talents that might otherwise be wanting among leaders and commanders." This was middle management before the term existed. Professionals whose purpose was to route information, pre-compute decisions, and maintain alignment across a complex organization. The military also formalized the distinction between "line" and "staff" functions. Line advances the core mission. Staff provides specialized support. Every corporation still uses this vocabulary today.
Military hierarchy entered the business world through the American railroads in the 1840s and 1850s. The U.S. Army lent West Point-trained engineers to private railroad companies, and these officers brought military organizational thinking with them. Staff and line hierarchies, divisional structure, bureaucratic systems of reporting and control: all of it was developed in the military before the railroads adopted it. In the mid-1850s, Daniel McCallum of the New York and Erie Railroad created the world's first organizational chart to manage a system stretching over 500 miles with thousands of workers. The informal management styles that worked for smaller railroads were failing. Train collisions were killing people. McCallum's chart formalized the same hierarchical logic the Romans had used: layers of authority, defined reporting lines, structured information flow. It became the blueprint for the modern corporation.
Frederick Taylor (1856-1915), often called the "Father of Scientific Management," optimized what happened within that hierarchy. Taylor broke work into specialized tasks, assigned them to trained experts, and managed through measurement rather than intuition. This produced the functional pyramid organization - a structure optimized for efficiency within the information routing system that the military had pioneered and the railroads had commercialized.
The first real stress test of functional hierarchy came during World War II. The Manhattan Project required physicists, chemists, engineers, metallurgists, and military officers to work across disciplinary boundaries toward a single objective under extreme secrecy and time pressure. Robert Oppenheimer organized Los Alamos into functional divisions but insisted on open collaboration across them, resisting the military's instinct to compartmentalize. When the implosion problem became critical in 1944, he reorganized the lab around it, creating cross-functional teams unlike anything in corporate America at the time. It worked, but it was a wartime exception led by a singular figure. The question the postwar business world faced was whether that kind of cross-functional coordination could be made routine.
With the growth and globalization of companies after World War II, the scale limitations of functional design became acute. In 1959, McKinsey's Gilbert Clee and Alfred di Scipio published "Creating a World Enterprise" in the Harvard Business Review, providing an intellectual framework for a matri… continue on X ↗
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Marik Hazan Team Rebuilds Y Combinator Demo Day Startups With Agentic Founders

Tim Draper reposts a claim from Marik Hazan that an agentic AI team rebuilt every startup in Y Combinator's latest demo day batch, with working products shared in a thread. The claim is presented without independent verification.

Original post · 1 min read
Draper Associates (@DraperVC) company just replicated every YC startup in the latest batch using agentic founders. Incredible.
Marik Hazan @MarikHazan
We just rebuilt every startup in @ycombinator's latest demo day batch.

Here's what our agentic "founders" pulled off and what it means for the future of startups.

Fully useable products at the bottom of the thread below 🤖🧨
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Roelof Botha Says Block Is Pioneering AI-Driven Alternative to Hierarchical Management

Sequoia partner Roelof Botha promotes an essay with Jack Dorsey arguing that Block is building the first real alternative to hierarchical coordination. The thesis is that AI enables an information architecture built around a world model rather than reporting lines.

Original post · 1 min read
.@blocks is building what we think is the first real alternative to hierarchical coordination. For 2,000 years, humans have organized themselves in roughly the same way.

AI changes what’s possible: Not a flatter org chart, but a fundamentally different information architecture, organized around a world model rather than a reporting structure.

@jack and I wrote about what this looks like in practice, why it's different from past experiments, and why it may reshape how companies of all kinds organize in the coming decade.
jack @jack
From Hierarchy to Intelligence — At Sequoia, we see that speed is the best predictor of start-up success. Most companies are focused on AI as a productivity enhancer. Few are focused on the potential of AI to change how we work
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Job Search Coach Pitches Referral-First Strategy and $49 AI Job Search System

Job Search Coach Pitches Referral-First Strategy and $49 AI Job Search System

Aakash Gupta, who says he has placed candidates at OpenAI, Anthropic, Google and others, argues that referrals and tailored prototypes beat cold applications. He promotes a $49 system of 18 Claude Code skills for resumes, interview prep and networking.

Original post · 2 min read
I've placed people at OpenAI, Anthropic, Google, Meta AI, Databricks, and Stripe in the last year. They all did the same thing that 90% of job searchers skip.

They built referral paths before submitting a single application.

The average cold application callback rate in 2026 is around 2-4%. With a warm intro, it's 5x that. Every candidate I coached who got an offer at a top company had a referral on file before the resume went in. Every single one.

But here's what most people get wrong about networking for jobs. They send "I'd love to pick your brain" to strangers. One message, no follow-up, silence forever. The people who land offers send 25 personalized connection requests per week, rotate across target companies, follow up on day 3, 7, and 14, and ask for the referral only after building context.

The resume game broke too. I tested every paid AI resume tool on the market ($20-40/mo). They all do one of two things: invent experience you don't have (which gets you blacklisted when the interviewer checks) or swap keywords on a generic template (which recruiters now spot in a 6-second scan).

The move almost nobody makes: a 1-pager analyzing the company's product + a working prototype of your recommendation. 90 minutes. I've seen this land interviews where cold applications failed completely. The catch is specificity. If it could've been written for any company, a hiring manager told me it's actually a negative signal.

I spent 6 months building a system that automates all of this. 18 Claude Code skills. Resume tailoring from your real experience only. Interview prep with insider data from 250 companies. Mock interviews that compound (after 5-6 real interviews, the system knows your weakest question types). Networking sequences. Negotiation.

$49 once. 45-minute setup. Then 20 minutes a day.

Honest caveat: the system is only as good as your experience library. If you skip the 10-minute setup where you load your real career history, every output will be generic. The input is the bottleneck, not the tool.

Full deep dive: news.aakashg.com/p/job-search-os
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Google Stitch Adds Prompt Enhancer and Design Tips for Better Results

Google Stitch Adds Prompt Enhancer and Design Tips for Better Results▶

Stitch by Google thanks users after its launch and shares guidance on prompting, including a new prompt enhancer under the plus menu. The post outlines tips on intent, design language and color hierarchy, with a video walkthrough by David East.

Original post · 1 min read
We are completely humbled by the amazing response to our launch last week! 🫶 Now, we want to help you get the absolute best results from Stitch.

In this new video, David East walks you through how to consistently get premium results.

We also launched a new prompt enhancer (located under ‘+’ menu) to help you quickly collaborate on your vision before you submit your first prompt.

Stitch doesn't replace the design process—it is a tool for fast exploration and refinement, which is most effective when you step into the role of Creative Director.

Here are David's top strategies for taking your designs from generic to amazing:

🧠 Start with Intent: Define exactly who the design is for and how you want them to feel before you start building.

🎨 Enhance your prompt: You can use the new prompt enhancer (under the ‘+’ button’) to teach you design language and swap abstract words like "sporty" for tangible aesthetic descriptions like "high-end stationery" or "architectural limestone".

📐 Master Color Hierarchy: Treat colors as visual weight—Neutral for the canvas, Primary for ink, and Tertiary for your loudest accents.

Watch the full breakdown and see the transformation here👇images in 🧵
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