Eric D. Brown, D.Sc.

I help leadership teams evaluate technology and AI decisions before they get expensive. Doctorate in AI/ML. CTO. Fortune 500 to Series A.

[Eric D. Brown, D.Sc. @EricDBrownDsC]

I help leadership teams make technology and AI decisions before they get expensive and diagnose the ones that already have.

Fortune 500 to Series A. Doctorate in AI/ML/Data Science

Writing at https://t.co/8WVEH195SX

[Julien Simon @julsimon]

Trust for AI agents will not be one global market. I made that case yesterday on the investor panel at the AI Assurance & Governance Summit, Stanford Faculty Club, in a room full of US VCs and founders.

Europe has already run this test in payments. Before the Brexit transition ended, the European Banking Authority announced that the certificates of UK open-banking providers would be revoked. Certificates follow jurisdiction.

So before an agent touches production, a European bank will ask where the keys are, which government can order them handed over or switched off, and which court to go to when something breaks.

Founders: build for two roots of trust from the start, one American and one European.

https://t.co/w4soqS7Bq7

[Arize AI @arizeai]

How do you benchmark an agent when two versions both get the answer right?

Run them on the same task, in the same environment, then inspect how they got there.

@ehutt_ from the Phoenix team used Harbor + Phoenix to benchmark agents across PXI, Claude Code, Codex, and different tools and interfaces.

Full readout here: https://t.co/WmrTGmYCxQ

[Jane Zhang @jjanezhang]

🎉 Introducing Databricks AI Decide: make fast decisions on your governed data

Following the TypeSafe AI Jev launch, we’ve seen increasing demand for a fast, low-cost API that turns raw text into structured decisions. However, many of our enterprise customers are unable to use Jev due to data privacy and access concerns.

AI Decide is our enterprise-grade function for fast decisions on customer’s governed data. It is supported for batch use cases like processing millions of documents with SQL and for realtime applications like model routing through our REST API. Starting early next week, customers will also be able to govern permissions on the ai_decide function through Databricks Unity Gateway.

A big shoutout to @mattydtweetz @ivanzhouyq @nihit_desai @hanlintang for getting this new function launched in less than a week!

[Andrew Drozdov @mrdrozdov]

LLM as a judge? Reranker? Reward model? Classifier? Make up your mind! Glad we’ve decisively converged to decision models.

[Daniel Han @danielhanchen]

Unsloth Desktop can serve local Jev type decision models!

We made a real time packing demo powered by local Laya through Unsloth’s Decision API - suitcase items update as you type and change your travel plans!

More optims coming soon to make it even faster for local hardware!

[Eric D. Brown, D.Sc. @EricDBrownDsC]

Almost every AI plan I read says a human will review the output. The plan should also say what that human is allowed to do, because if they can't say no and fix what the model got wrong, the review is there for show.

https://t.co/YYWPohbtt7

[Eric D. Brown, D.Sc. @EricDBrownDsC]

The FTC opens an AI product-risk probe, Google, OpenAI and Anthropic ship new models, AMD picks up World Labs, and Micron says the memory squeeze runs through 2028.

https://t.co/OHxAfShzxK

#AI #AIStrategy #WeeklyIntel #AIRegulation #AISecurity

[Eric D. Brown, D.Sc. @EricDBrownDsC]

The FTC opened an investigation into OpenAI, Anthropic and other AI companies over product risks, and Nvidia released an agent safety platform after several labs disclosed models escaping their sandboxes. Google, OpenAI and Anthropic all shipped new models, AMD is bringing in World Labs, and Micron says the memory shortage will get tighter in 2027 and 2028.

https://t.co/HAPopx529Q

#AI #AIStrategy #WeeklyIntel #AIRegulation #AISecurity

[Eric D. Brown, D.Sc. @EricDBrownDsC]

Great read and reminder that 'change' is constant.

https://t.co/45XXDeb3ZY

[Eric D. Brown, D.Sc. @EricDBrownDsC]

Every planning meeting I've sat in was full of forecasts. Almost none carried a probability, and nobody went back to check how they did.

Tetlock's Superforecasting follows the people who actually keep score, and what happens when the book's core habit (start with the base rate) meets questions the past can't answer, like how fast AI would move.

The practices still hold: keep score and update in small steps. They're a floor, and it's worth knowing where that floor ends.

https://t.co/GW7aoOYwjH

[Eric D. Brown, D.Sc. @EricDBrownDsC]

How a Milky Way visibility rating works: it checks core altitude, astronomical darkness and the moon for your exact spot, so you can plan months ahead.

https://t.co/1xTqGVCcGq

#MilkyWay #Astrophotography

[Eric D. Brown, D.Sc. @EricDBrownDsC]

A Milky Way visibility rating checks three things you can compute years ahead: whether the galactic core is high enough, whether the sky reaches astronomical darkness, and whether the moon is out of the way during your core hours. Weather and light pollution are left out on purpose, so you can pick next June's nights in October and check the forecast three days out.

When a July night rates badly, the row tells you why. A bright moon means try another night that week, and "No twilight" in the core column means your latitude and a different month.

https://t.co/abAN4JYLU0

[Eric D. Brown, D.Sc. @EricDBrownDsC]

Two AI data readiness studies both land on 7%, and they measure different things. The silo problem AI trips over is twenty years old, and readiness starts with one workflow.

https://t.co/F9BWeUeZIO

#AI #AIStrategy #DataReadiness #Judgment #CriticalThinking

[Eric D. Brown, D.Sc. @EricDBrownDsC]

Two AI data readiness studies came out this year and both landed on 7%. Accenture's number is its analysts' grade of 2,000 companies. Cloudera and Harvard Business Review Analytic Services asked 231 people whether their data was completely ready, and 7% said yes. Same number, and they aren't measuring the same thing.

The top obstacle in the Cloudera study was siloed data and trouble integrating sources, at 56%. I called that the data disconnect back in 2012, when teams were buying their own tools and the data ended up on laptops and in vendor clouds, and AI inherits all of that.

Accenture's own report says its 7% concentrated on a few strategic bets and built readiness in an iterative loop. I'd start with one workflow that moves money or customers, and the data that workflow actually needs.

https://t.co/vOIEzHRteO

#AI #AIStrategy #DataReadiness #Judgment #CriticalThinking

[Eric D. Brown, D.Sc. @EricDBrownDsC]

A lot of AI proposals get sold on how new they sound. The deck says nobody in the industry is doing this yet, a model spits out forty angles in under a minute, and the room leans in.

Two Stanford studies say that's the wrong funding gate. Experts rated AI research ideas more novel than human ones on paper. Then researchers spent roughly a hundred hours building them. The AI ideas lost most of that lead. How an idea scored before anyone touched it told you very little about what it was worth once it existed.

So don't fund the roadmap off the pitch score. Fund a slice scoped to find what breaks: fixed budget, named builder, stop condition written first. Ask the team what fails in the first hundred hours of building this. If they can't name it, they haven't thought about building it yet.

https://t.co/IWTHy8rtPk

[Eric D. Brown, D.Sc. @EricDBrownDsC]

OpenAI shipped GPT-6 Sol and Luna, Anthropic shipped Claude Opus 5.5, which costs 40% less than Opus 5, an appeals court upheld the Pentagon's blacklisting of Anthropic, and ASML sold nothing in Europe in 2026. https://t.co/lJZ7UhooSm

#AI #AIStrategy #WeeklyIntel #AIRegulation #AIModels

[Eric D. Brown, D.Sc. @EricDBrownDsC]

Grand Canyon, one of my first tries at the Milky Way. Started at sunset, way too early, and the clouds covered most of the sky. The pine and the rock ended up carrying the whole frame instead.

https://t.co/yD64BIq9pA

[Eric D. Brown, D.Sc. @EricDBrownDsC]

Newly unredacted filings in the New York Times case quote a senior Microsoft executive describing the companies' AI training practices as theft, and as the largest theft of labor in human history. OpenAI leadership called its own models an existential threat to the publishers whose work trained them. The filings also allege the companies bypassed paywalls undetected and stripped copyright notices from training data. . https://t.co/k2k2OcVhgv

#AI #AIStrategy #WeeklyIntel #AIRegulation #AISecurity

[Magnus Stjernstrom @magnus72]

Asked Jev one question:
Is the Answer to the Ultimate Question of Life, the Universe, and Everything equal to 42?
Noul came back 0.98.
System One is Team Deep Thought.

[Eric D. Brown, D.Sc. @EricDBrownDsC]

One of my first attempts at the Milky Way. Grand Canyon, 2019.

https://t.co/WGajgQS2E9

#GrandCanyon #MilkyWay #NightPhotography #LandscapePhotography