For most of the AI conversation in 2024 and 2025, the binding constraints on the build-out were chips and capital. By 2026 the conversation has shifted, and the constraint that gets discussed most seriously inside the hyperscalers is electricity. Not the cost of electricity. The actual physical availability of electrons - at gigawatt scale, in the places where the data centres need to be, on the schedule the model labs need them to be. The story does not have a single villain or a single number, but it has a shape, and the shape is becoming the story of the second half of the decade. ...

For most of the last decade, talking about AI hardware meant talking about Nvidia. In 2026 that has stopped being true at the inference layer. Three companies - Cerebras, Groq, and SambaNova - have built genuinely different chips around the same insight: that the workload economics of running models in production are not the same as the workload economics of training them, and that the chip architecture should follow the workload. The bet has been right enough that Nvidia has now licensed pieces of it. ...

If you spend enough time in data engineering, you will eventually encounter the conviction that batch processing is dying and streaming is the future. This is the third or fourth time the industry has had this conversation in my career, and the answer has been the same every time. Streaming is not the future. Batch is not the past. They are different tools with different operational profiles, and the systems that age well use both, with discipline about which is the right choice for which problem. ...

TL;DR Document understanding is the unglamorous killer application - invoices, contracts, and scanned PDFs that were painful to extract data from are now tractable without dedicated pipelines Vision models still under-deliver on precise spatial reasoning, object counting, and subtle medical or scientific imagery - these remain jobs for specialist models Audio is the modality with the most upside: beyond transcription, it carries tone, pace, and hesitation that text loses, enabling fault detection, emotional analysis, and richer inputs The teams getting real value treat multimodal as an invisible enabling capability within a workflow, not a feature to demo - and they verify high-stakes outputs just as they would text The right question when evaluating multimodal is not “can we use this” but “what specific user problem becomes tractable that previously was not” When the first multimodal frontier models shipped, the demos were genuinely impressive. A photo of a fridge interior with the model suggesting a recipe. A handwritten napkin sketch becoming working code. A short audio clip of a meeting being transcribed, summarised, and structured. It looked, briefly, like the boundary between modalities had collapsed and we were entering a new regime in which models could reason fluidly across text, images, and sound. ...

Two years ago the way to make a model better was to train a bigger one. By the start of 2026 that recipe has stopped being the most interesting answer. The frontier has moved to a different lever - letting the model think for longer at inference time, generating intermediate reasoning, and only then producing the final answer. The category has a name now (reasoning models) and a family of products built around it. The interesting questions are no longer whether the trick works, because it clearly does, but when to reach for one, where it lands in production, and what the costs actually look like once the demo glow wears off. ...

The word “lakehouse” has been doing a lot of work for the last five years. It has been used to describe everything from a thin SQL layer over object storage to a fully integrated platform with governance, lineage, ML training, and BI built on top. Like most umbrella terms, this elasticity has been useful for marketers and confusing for engineers. This post is the version of the conversation I would have with a senior engineer who has been asked to “build out our lakehouse” and wants to know which pieces are load-bearing and which are noise. It draws on what I have actually seen ship and survive in production data platforms in 2026, and it tries to be specific about why each layer is in the stack rather than just describing the picture as a fait accompli. ...

Scott Galloway is the kind of commentator the AI conversation rarely produces: not a researcher, not a founder, not a doomer, not a booster. He is a marketing professor and a serial entrepreneur with a record of correctly reading the corporate stories of the last two decades, and he has spent the last two years pointing at the AI story with increasing concern. The headline of his pitch - that AI was not built for ordinary people and that the rich no longer need them - is provocative on purpose. The argument underneath is more careful, and worth pulling apart on its own terms. ...

The Yamaha Montage M and the Roland MC-707 are both, on paper, complete instruments. The Montage is a flagship synth workstation with three distinct sound engines and the kind of polyphony and DSP headroom that makes most studio plugins look slow. The MC-707 is a compact groovebox with eight tracks, an internal sequencer, sample playback, and the kind of immediate hands-on workflow that makes laptop production feel laborious by comparison. ...

A six-month review is a different beast from a release-day one. The honeymoon is over. The early enthusiasm has cooled. The features that demoed well in the showroom have either earned their place in your daily workflow or quietly been abandoned, and the features you initially overlooked have either continued to be irrelevant or become indispensable. This is a six-month review of the Yamaha Montage M, the M8X variant specifically, from the perspective of someone using it as the centrepiece of a hybrid hardware rig rather than a stage instrument or a sound-design lab. The conclusions are mine, the use case is specific, and your mileage will genuinely vary, but the patterns I have noticed are likely to repeat across other working setups. ...

What is ETL? ETL is a foundational data engineering process that powers modern analytics: Extract - Retrieve data from various sources (databases, APIs, files, cloud services, streaming platforms) Transform - Clean, validate, deduplicate, and reshape data into required data models Load - Move processed data into data warehouses, data lakes, or analytical systems ETL ensures data quality, consistency, and accessibility for analytics and reporting. In 2026 the dominant pattern is ELT (Extract-Load-Transform), which leverages cloud data warehouse compute for transformation, and increasingly EtLT (adding lightweight pre-load transforms for streaming and schema drift). See the Fundamentals of Data Engineering book for a deeper framing. ...