The cheap part got cheaper
AWS cut S3 Vectors query pricing by up to 80% at scale. It is a real win for your bill and says nothing about your moat - because the price cut lands on the one slice of retrieval where quality barely lives.
AI search
12 articles tagged “AI search”.
Code mode is batched retrieval
Hornet held the retriever, model and corpus fixed and swapped the harness from one tool call per turn to one Python program per turn. Recall went up 65%, tokens down 51%. Strip the new vocabulary and the win isn't Python - it's that retrieval was finally allowed to fan out.
Near both, on neither: why single-vector search fails high-intent queries
A small, self-contained experiment on why single-vector retrieval breaks on compound, high-intent queries - and how late interaction keeps two facets intact where a dense embedding averages them away.
Why I’m betting on AI Engine Optimisation
The closing piece of the series, and the substantive one. Why the AEO problem is structurally interesting, what fifteen years of search infrastructure transfers and what doesn’t, and why I think the timing is right.
Optimising for AI search
If being in the AI answer is the new being on page one, how do you get there - and how do you know if you have? A look at what optimising for AI search actually means, and why the measurement problem is the hard part.
Where did all the traffic go?
A lot of sites are watching their search traffic fall, and it isn’t because their content got worse. The funnel changed shape. This is the post in the series where the cultural shift turns into a commercial problem.
Citations are the new ranking
The previous post in this series argued the engineering inside an AI answer is recognisable IR. This one looks at the visible end of the pipeline: the citation slot. When the surface is a paragraph with three sources cited, the question "did we get cited?" becomes the new "did we rank #1?".
Retrieval inside an LLM is still retrieval
Open Perplexity, run a query, look at the Sources panel. The architecture is recognisable: retrieve, rerank, summarise, cite. The system has moved; the engineering hasn’t. A bridge piece between the observational posts and the next phase of this series.
The disappearing search engine result page
The previous post in this series was about my own habits shifting. This one is about the page itself. The shape of the Google SERP in 2026 is structurally different from the one we built businesses around - and the difference is mostly that you don’t have to leave it any more.
How I actually use the web in 2026
I spent fifteen years building search engines - and somewhere in the last year I quietly stopped using one. An honest look at how my web habits have shifted, what it costs, and why I don’t think it’s just me.
The harness is mostly retrieval
Laurie Voss says applied-AI iteration has moved off the model and into "the harness". He’s right - and once you strip the new vocabulary, the harness is mostly a retrieval system.
Everything has changed in search. Nothing has changed in search.
Agentic search looks like a clean break from classical IR. Look closer and the architectural instincts are the ones backend engineers have used for decades - the components are new, the rules are not.