How AI will break Brooks' longstanding "no silver bullet" law, yet drive more software jobs - eventually.
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Around 500 BC Heraclitus declared, "you can never stand in the same river twice". Then everything changed.
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CCA-F facts and falacies.
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The utterly confusing, confounding, and compounding cost of AI.
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Agents are hyper-communicative polyglot polymaths with photographic memories - not faster horses.
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Over reliance on AI destroys new skill formation - who knew?
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Opus 4.6 and Codex 5.3 are more than coding engines, they are fabric for hyper-lean operating models.
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Steve Jobs' design genius wasn't form follows function - it was monetisation follows form. Could Anthropic unleash an AI Store for the singularity?
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When the tide goes out, you see who's got their trunks on - Warren Buffett [paraphrased], 1995 AGM .
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The end of SaaS? Not likely. Time to buy? Most likely.
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What happens when your side project turns into a cross between SkyNet and the Life of Brian?
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Near-term "10x" gains from coding agents won't come from ten times more code or a tenth of the cost. They come from crashing the value stream.
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The hardest problems in technology aren't inside systems - they're between them.
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If an MRI scan revealed your innermost thoughts, would you share the results? Looks like the Cursor crew wouldn't either...
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What's the risk - in the scheme of things?
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A philosophical critique of AI predictions, futurology and AI-2027.
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Serendipity and the law of obliquity
Read on SubstackLab · New release
A live point cloud that reveals the logistic bifurcation diagram as the real slice of the Mandelbrot set.
Five verified stages · About 4.5 hours
A single one-shot prompt produced the initial build. Under Autometta, Claude Fable 5 and GPT-5.6 Sol delivered the mathematical kernel, WebGL point cloud, camera, marker and animated cascade, with each stage independently checked by another model before it landed.
The core run took about 4.5 hours, including a 50-minute model-availability pause. Two short follow-up stages fixed point-cloud clipping and added period colouring that same evening.
Click to rotate, zoom and explore →
Research · Sweeps
Financial press, frontier labs, academia, VC and independent analysis - cross-read in a single view.
2026-08-27 · deep
Information density, entropy and semantic determinacy in formal versus natural languages, and what it implies for specifying computation
2026-08-25 · deep
Observability for LLM agent orchestration, pipelines and multi-agent swarms between August 2025 and August 2026: emerging standards (OpenTelemetry GenAI semantic conventions, MCP tool tracing), the tooling landscape (LangSmith, Langfuse, Arize Phoenix, Braintrust, W&B Weave, Comet Opik, Datadog LLM Observability), the two-layer pattern of durable orchestration plus agent tracing (Temporal, LangGraph), and how production operators such as Stripe, Shopify, Airbnb and Anthropic instrument, evaluate and debug agent fleets at scale
2026-08-14 · deep
Evidence of AI-related labour-market change from November 2022–August 2026: employment, vacancies, wages and productivity; occupational and demographic distribution; job creation, displacement and work redesign; and competing economic interpretations using ONS, BLS, Eurostat, OECD, ILO and IMF evidence.
2026-08-04 · deep
Whether Apple Silicon unified memory and the Neural Engine are becoming a credible substrate for how AI is run commercially and on consumer devices between January 2025 and August 2026: shipped capacity and bandwidth (M3 Ultra at 512GB, M4 and M5 Max) versus the rumoured 1.5TB M7 Ultra, comparison against NVIDIA H200, GB200 NVL72 and AMD MI355X on memory-bound serving, the orchestration and scheduling gap (MLX distributed, EXO, Thunderbolt 5 fabrics, Kubernetes and MDM on macOS, Metal kernel maturity versus CUDA), the on-device stack (Neural Engine, Core ML, the Foundation Models framework, Private Cloud Compute), fleet monitoring and data-centre economics, and how Apple is positioned to capture value from the AI race as compute substrate rather than as a frontier model lab.
2026-08-03 · deep
Independent security research into Chinese open-weight models (DeepSeek R1 and V3, Alibaba Qwen, Moonshot Kimi K2, Zhipu GLM, MiniMax, Baidu Ernie) from February 2025 to August 2026: who is testing them, what red-teaming and provenance methods they use, which results survive independent replication, and how regulated, defence and military buyers are assuring models whose training data and training objectives are never disclosed
2026-07-28 · deep
AI 2027 scenario tracking from the report's April 2025 publication through to late July 2026: whether the OpenAI sandbox escape and Hugging Face compromise, the US export-control suspension of Claude Fable 5 and Mythos 5, state and criminal use of AI in cyber operations, and Chinese open-weight model capability reproduce the scenario's predicted sequence of loss of control, cyber capability and state intervention; whether the capability curve has plateaued or merely re-based; and which of the scenario's remaining failure conditions have or have not materialised
Research · Explainers
Long-form research explainers - context, implications, and the parts most coverage leaves out.
West (2026)
Eight free candidates, five local open-weight models and three cloud free-tier routes, were retro-graded against frontier verdicts on ten real stages of agent-written code. The best caught 77% of genuine failures. Five caught almost none, while agreeing with passing work up to 98% of the time.
Flyvbjerg et al. (2026)
Across 5,360 IT projects, 59% finished on or below budget. But the severe-overrun group averaged 453% above budget, creating a fat tail: rare outcomes so large that ordinary averages become dangerously reassuring.
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