Simplifying complexity
NewThe 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 "form follows monetisation". 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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How AI will break Brooks' longstanding "no silver bullet" law, yet drive more software jobs - eventually.
Read on SubstackResearch · Sweeps
Financial press, frontier labs, academia, VC and independent analysis — cross-read in a single view.
2026-06-03 · deep
Tools and emerging approaches for code intelligence and code-graph indexing for AI coding agents from June 2025 through early June 2026, spanning local/embedded indexers (CodeGraph/Caveman-style repo maps, tree-sitter, SQLite and embedded graph stores), enterprise-scale code understanding (SCIP, code knowledge graphs, embeddings+retrieval), LSP-to-MCP bridges such as Serena, and the semantic-vs-syntactic-vs-embedding trade-off.
2026-06-01 · standard
Engineering patterns for large, fast-changing corpora from 2024 to 2026: prompt and prefix caching, the shift from prompt engineering to context engineering, embedding staleness and freshness strategies, multi-strategy retrieval beyond pure vector search, and the inference-cost economics now reshaping infrastructure decisions.
2026-05-19 · deep
The trajectory of AI regulation across the EU AI Act, the UK's pro-innovation and contextual approach, and the financial-services regulatory regime (FCA, PRA, Bank of England) from January 2023 to May 2026, including the FCA Mills Review, GPAI obligations, model-risk and accountability rules, and what they demand of technology leadership in regulated firms
2026-05-15 · deep
The science of emergent behaviour and self-organisation from 2015–May 2026, connecting reaction-diffusion and cellular-automata models to commercial markets, organisational behaviour and culture, ecology, biology, physics and chaos theory, including foundational algorithms and their cross-domain explanatory power.
2026-05-11 · deep
The compounding economic logic of three successive technology waves from January 1995 to May 2026 — internet disintermediation of distribution, software-defined platforms and cloud infrastructure, and the current AI/agentic systems wave — examining the technical, economic and human-skills dependencies that make each wave a precondition for the next, the new categories of work each wave created, and whether the relationship is best understood as cumulative compounding or as externalised costs harvested by later layers.
2026-05-10 · deep
Agentic RAG between November 2025 and May 2026: how retrieval-augmented generation is shifting toward agent-driven architectures, the operational problems (token burn, context management, latency, reliability), information-organisation patterns such as context catalogues and semantic categorisation, parallels with traditional data warehousing (dimensions, measures, star schemas), the evolving RAG tooling landscape, and decision criteria for switching to pure agentic workflows.
Research · Explainers
Long-form research explainers — context, implications, and the parts most coverage leaves out.
Hu et al. (2025)
Position-Independent Caching lets language models reuse document KV vectors regardless of what comes before them. EPIC's LegoLink algorithm fixes the resulting attention sink with O(kN) work instead of O(N²).
Liu et al. (2026)
A source-level reading of Anthropic's coding agent finds that about 1.6% of the code is AI decision logic. The other 98.4% is permission gates, context compaction, extensibility plumbing, and recovery.
Mertens et al. (2026)
Across 17,000 worker evaluations of more than 3,000 real labor-market tasks, frontier models improve broadly across task lengths, not in sudden bursts. By 2029 most text-based tasks could hit 80 - 95% success rates.
Cambridge CCAF (2026)
A 628-organisation, 151-jurisdiction survey finds 81% of financial firms now using AI, while regulators trail on adoption, data collection and the supervisory tools needed to keep up.
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