Data & Decision Science

37 stories · publishing
Updated Sep 2, 2026
AIU research department

PublishingThis department publishes coverage today — every story links out to its own source.

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The lead

Sep 2, 2026

AI-Assisted Software DevelopmentGoogle DeepMindnotableSep 2, 2026

Google's Gemini 3.8 Flash holds the old price and adds a cybersecurity-only sibling

Google DeepMind released Gemini 3.8 Flash and 3.8 Flash Cyber on September 2 — one foundational model split by safeguards rather than by size.

What it means for your work

Third Flash release in six weeks at the same rate card — the cheap tier is where most production traffic actually runs, and working harder per task is a cost change even when the price is not.

✓ verified · deepmind.google · added todayRead it at deepmind.google

What today means

Our read on the items that move something. The reporting is everyone's; this part is ours.

  1. Enterprise model spend is compounding faster than the seat-based software it displaces — the number to hold up next to any internal business case that still treats model cost as an experiment budget.Anthropic’s annualized revenue run rate passes $65B, ahead of OpenAI’s reported $40BThe AI Product & Business Strategy beat · TechCrunch

  2. Check the licence before you plan on it: the code is Apache-2.0 but the weights are non-commercial and non-production, which rules out most business forecasting.Google’s TimesFM-3 forecasts several related series at once, with no fine-tuningThe Data & Decision Science beat · Google Research

  3. If agent loops rather than chat are driving a three-and-a-half-fold jump in six months, capacity planning is an agent-architecture question before it is a model one.China’s daily AI token calls passed 500 trillion, up from 140 trillion in MarchThe Data & Decision Science beat · TechNode

2 new findings here overnight. 36 here in all.

What this department watches

The charge

This department watches the machinery underneath decisions: the data stack and the pipelines that feed it, the dashboards people actually steer on, and the statistical practice that separates a defensible result from a confident one. Where a claim rests on a number, this department is interested in how that number was produced.

  • The data stack, pipelines, and where they break
  • Dashboards and metric definitions people actually use
  • Statistical and experimental practice, including its misuse
  • Data quality, lineage, and reconciliation
  • AI-assisted analysis, and where it quietly guesses
  • The business metrics companies steer and report on

The questions it keeps open

6 standing questions

These are the open questions this department works. They are questions, not conclusions — what comes back gets published with its sources attached.

  • When a copilot writes the query, where does it break — and which of those failures are silent rather than loud?
  • What is the current honest state of plain-language-to-query on a real, imperfect warehouse schema, as opposed to a clean demo one?
  • Which parts of data cleaning and quality management can be handed to a model, and which still require someone who knows the business?
  • How are teams defining and governing metrics now that anyone can generate a chart in seconds — who owns the definition?
  • What is AI actually changing about experimental design and how results get read, versus what only sounds like it changed?
  • What does a data role look like when the query-writing is automated — what are teams hiring for instead?

How the research runs

The process
1 · Read the real sources

AIU's research agents work from a published register of trusted sources rather than from memory — the register itself is open, so you can see what is being read and how often it is checked.

The source register →

2 · Carry the method and the source

A finding is only useful if you can check it. Every item carries the source it came from and the date that source published, and links straight out to the original — so a claim can be verified rather than taken on trust.

The research archive →

3 · Feed the coursework

What this department reads is what keeps the coursework current — and the coursework decides what is worth watching next. The two are meant to inform each other, which is why the curriculum below is on this page rather than somewhere else.

The curriculum behind it

3 courses on aiuni.tech
The major

AI + Data & Decision Science

Turn data into decisions with AI — the practical alternative to a statistics degree, built around real dashboards and real pipelines.

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Two doors

This department is publishing now — read the edition, or put your name down to be told when its research goes further.

Request early access to this department's research Read the Data & Decision Science edition

Behind this department

Everything below is Intel's one record, filtered to this department — the same pages Research opens, showing only this slice. Each one says so on arrival and links back to everything.
Sources for this departmentOnly the sources it draws onResearch behind this departmentEvery finding it selected, in fullThis department on the mapIts slice of the knowledge graph
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