Last week Google Quantum AI dropped a 57-page whitepaper that should be keeping every blockchain developer awake at night. The headline finding: Shor’s algorithm can break the 256-bit elliptic curve cryptography underpinning Bitcoin, Ethereum, and most of the crypto ecosystem using fewer than half a million physical qubits on a superconducting architecture. Their circuits could execute in about nine minutes–within Bitcoin’s average block time.
… Basically: Google withholds the specific quantum circuit they discovered in the name of responsible disclosure, yet the paper itself constrains the search space so tightly that reproducing comparable circuits is well within reach for any serious quantum algorithms group. Including, I would say, our team at SingularityNET, even though quantum is not our main shtick.
Another point I made to the journalists who asked me about this is: The qubit counts that make these cryptographic attacks feasible are roughly the same qubit counts that make quantum-enhanced AI feasible. So regarding quantum computing, the threat and the capability will arrive on roughly the same time-scale, and if you’re only looking at the threat side, you’re missing half the picture–arguably the more important half. — Read more
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Inside Meta’s Home Grown AI Analytics Agent
The hypothesis was simple: can an AI agent perform routine data analysis tasks autonomously? Data scientists tend to get asked similar questions over and over, working within a familiar set of tables. An agent seeded with context about which tables a person queries, and how they use them, might be able to handle much of this work on its own.
To test the idea, a data scientist on the team used Meta’s internal coding agent to hack together a prototype on their devserver: an agent that could execute SQL against the internal data warehouse, with access to a few colleague’s query history for context.
The first real-world trial started simply enough: after sharing the prototype with a colleague, they asked it to diagnose a drop in a health monitoring metric. The agent identified the right tables, ran several diagnostic queries on its own, and ultimately traced the root cause to a recent code change.
That was an ah-ha moment that shifted the conversation. — Read More
Cutting the Middle Management Layer
Block, the company behind Square, Cash App and Afterpay, recently cut its staff by 40%, over 4000 employees. Block is questioning the underlying assumption: that organizations have to be hierarchically organized with humans as the coordination mechanism. Instead, Block intends to replace what the hierarchy does. Most companies using AI today are giving everyone a copilot, which makes the existing structure work slightly better without changing it. They’re after something different: a company built as an intelligence (or mini-AGI).
Block CEO Jack Dorsey just co-authored a post arguing the position, believing most companies will follow suit in the near future. — Read More
The Economics of Generative AI: Two Years Later
I am excited to update my original analysis from 2024: The Economics of Generative AI. This is the analysis I come back to more than anything else I’ve written because it’s a reminder of the “physics” of the AI industry.
Two years ago, I found that the Gen AI value chain was inverted: the compute layer captured ~83% of all revenue and ~87% of all gross profit. The application layer, despite being closest to end customers, earned almost nothing. I predicted this would flip over time, following the pattern of every prior platform shift.
Two years since, the AI ecosystem has grown roughly 5x, from ~$90B to ~$435B in annualized revenue. But what’s remarkable is how little the shape of economics has changed.
The bottom line upfront: Semi is a one-player game. Apps is a two-player game. Infra is the only competitive layer. The most profitable strategy in AI is still selling the shovels. 🙂 — Read More
Meta-Harness: End-to-End Optimization of Model Harnesses
The performance of large language model (LLM) systems depends not only on model weights, but also on their harness: the code that determines what information to store, retrieve, and present to the model. Yet harnesses are still designed largely by hand, and existing text optimizers are poorly matched to this setting because they compress feedback too aggressively. We introduce Meta-Harness, an outer-loop system that searches over harness code for LLM applications. It uses an agentic proposer that accesses the source code, scores, and execution traces of all prior candidates through a filesystem. On online text classification, Meta-Harness improves over a state-of-the-art context management system by 7.7 points while using 4x fewer context tokens. On retrieval-augmented math reasoning, a single discovered harness improves accuracy on 200 IMO-level problems by 4.7 points on average across five held-out models. On agentic coding, discovered harnesses surpass the best hand-engineered baselines on TerminalBench-2. Together, these results show that richer access to prior experience can enable automated harness engineering. — Read More
Backpropagation is simpler than you think (once you see this)
Backpropagation is one of those terms that gets thrown around so much in AI that people assume everyone already understands it.
But most explanations stop at “the network adjusts its weights using gradients” and leave you nodding along without actually knowing what is being computed or why.
In this blog, I’m going to fix that.
We’ll start from scratch and work all the way to a complete, clean idea of every gradient you need. — Read More
Quantum computers need vastly fewer resources than thought to break vital encryption
Building a utility-scale quantum computer that can crack one of the most vital cryptosystems—elliptic curves—doesn’t require nearly the resources anticipated just a year or two ago, two independently written whitepapers have concluded. In one, researchers demonstrated the use of neutral atoms as reconfigurable qubits that have free access to each other. They went on to show this approach could allow a quantum computer to break 256-bit elliptic-curve cryptography (ECC) in 10 days while using 100 times less overhead than previously estimated. In a second paper, Google researchers demonstrated how to break ECC-securing blockchains for bitcoin and other cryptocurrencies in less than nine minutes while achieving a 20-fold resource reduction.
Taken together, the papers are the latest sign that cryptographically relevant quantum computing (CRQC) at utility-scale is making meaningful progress. — Read More
Architectural Governance at AI Speed
GenAI has slashed the effort required to produce code, and rapid prototyping is increasingly common. As a result, the software development lifecycle is now constrained by an organization’s ability to bring ideas into alignment and maintain cohesion across the system.
Historically, organizations have relied on manual processes and human oversight to achieve architectural cohesion. Startups rely on key individuals to catch misalignment between architectural intent and implementation. Enterprise-level organizations attempt to maintain cohesion through change boards and proliferating ADRs and documentation. In both contexts, identifying misalignment is slow because it requires synchronous dependence on a central authority. In the startup case, development teams are stuck waiting for busy experts. In the enterprise case, they have to wait on review boards and sift through documented guidance with the hope that what they find has not become obsolete. GenAI exacerbates this by accelerating the production of work that’s subject to review. Where previously only developers were producing code over days or weeks, executives and product managers can now vibe-code functional prototypes in minutes or hours. As a result, development teams are left with an impossible choice: be beholden to the pace of manual oversight at the cost of velocity, or push forward without knowing whether they are aligned.
Over time, these small pushes compound into architectural fragmentation, which the organization responds to with more process and stricter guidelines, which further increase the difficulty of releasing software in alignment. This is a vicious cycle that slows delivery and blunts innovation. — Read More
AI Applications and Vertical Integration
At a high level, you can think about an AI product that achieves outcomes as having three layers:
1. At the bottom, the model
2. In the middle, the application or agent which includes the data/context, etc
3. At the top, the human or service layer needed to review/prompt/do the last mile to actually get to an outcome
… Traditional application layer companies would sit just in the middle layer. But these companies are increasingly beginning to (or starting off) vertically integrate in one of two directions. Some move down into the model layer. Others start or move up into the human or service layer. Both end up looking “full-stack1,” just in very different ways. — Read More
AI Infrastructure Roadmap: Five frontiers for 2026
The first generation of AI was built for a world where the model was the product, and progress meant bigger weights, more data, and stellar benchmarks. AI infrastructure mirrored this reality, fueling the rise of giants in foundation models, compute capacity, training techniques, and data ops. This was the focus of our 2024 AI Infrastructure Roadmap, which drove our investments in companies such as Anthropic, Fal AI, Supermaven (acquired by Cursor), and VAPI as the AI infrastructure revolution unfolded.
But the landscape has changed. Big labs are moving beyond chasing benchmark gains to designing AI that interfaces with the real world, and enterprises are graduating from POCs to production. The infrastructure that got us here — which was optimized for scale and efficiency — won’t get us to the next phase. What’s needed now is infrastructure for grounding AI in operational contexts, real-world experience, and continuous learning.
The stage is being set for a new wave of AI infrastructure tools to enable AI to operate in the real world. — Read More
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