Zeer MLCommons / intent / mlcommons

MLCommons

1-10 employees·San Francisco, California, United States·mlcommons.org

Observed, not inferred · updated weekly

Buying intent

35 tracked signals | Top 15 topics are below | Engineering and Product are carrying most of it.

29signals · 30 days
21topics tracked
65%attributed to a team

Attention by team

LinkedIn activity, by team

Where MLCommons's own people are actually spending their attention, by team, by topic. Bands run Low to High against the busiest pairing on this page, and each cell also shows how much of that team's own activity it represents.

Artificial Intelligence
Digital Innovation
Executive Education
European Tech Startups
Financial Services
Data Center Networking
Engineering
High30% of team Engineering to Artificial Intelligence: High, 30% of this team's signals
High30% of team Engineering to Digital Innovation: High, 30% of this team's signals
Medium20% of team Engineering to Executive Education: Medium, 20% of this team's signals
Engineering to European Tech Startups: no signal
Medium20% of team Engineering to Financial Services: Medium, 20% of this team's signals
Engineering to Data Center Networking: no signal
Product
High100% of team Product to Artificial Intelligence: High, 100% of this team's signals
Product to Digital Innovation: no signal
Product to Executive Education: no signal
Product to European Tech Startups: no signal
Product to Financial Services: no signal
Product to Data Center Networking: no signal
Others
High57% of team Others to Artificial Intelligence: High, 57% of this team's signals
Others to Digital Innovation: no signal
Others to Executive Education: no signal
Medium29% of team Others to European Tech Startups: Medium, 29% of this team's signals
Others to Financial Services: no signal
Low14% of team Others to Data Center Networking: Low, 14% of this team's signals
LowMediumHigh·  banded against the busiest pairing on this page

Topics being researched

30-day window

Every tracked topic, ranked by volume, not by our guess at what matters. Confidence is the classifier's own certainty that a signal belongs where we've filed it.

Artificial Intelligence
LinkedIn
High volume
96%
last
10d ago
Digital Innovation
LinkedIn
Low volume
97%
last
12d ago
Executive Education
LinkedIn
Low volume
98%
last
20d ago
European Tech Startups
LinkedIn
Low volume
95%
last
11d ago
Financial Services
LinkedIn
Low volume
98%
last
20d ago
Data Center Networking
LinkedIn
Low volume
96%
last
10d ago
Storage Networking
LinkedIn
Low volume
98%
last
10d ago
Founder
LinkedIn
Low volume
78%
last
13d ago
Negotiation
LinkedIn
Low volume
98%
last
22d ago
Software Development
LinkedIn
Low volume
96%
last
17d ago
Hiring
LinkedIn
Low volume
92%
last
11d ago
Marketing Campaign
LinkedIn
Low volume
98%
last
20d ago
Customer Needs
LinkedIn
Low volume
98%
last
17d ago
Vibe Coding
LinkedIn
Low volume
98%
last
13d ago
Open Source
LinkedIn
Low volume
98%
last
20d ago

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Who's active at MLCommons

verified title on file

Titles, seniority and topic straight from each person's own activity, with a LinkedIn link so you can check any of them yourself.

Others3 active
Executiveresearching Artificial Intelligence
in
researching Customer Needs
in
Product Manager and Partnership Strategy Lead for AILuminate
researching Vibe Coding
in
Engineering1 active
Senior ICresearching Artificial Intelligence
in
Product1 active
Senior ICresearching Artificial Intelligence
in

Primary products / business lines

LinkedIn company profile

MLCommons is an Artificial Intelligence engineering consortium, built on a philosophy of open collaboration to improve AI systems. Through our collective engineering efforts across industry and academia we continually measure and improve the accuracy, safety, speed and efficiency of AI technologies–helping organizations around the world build better AI systems that will benefit society.

Top accounts researching MLCommons

names withheld on the public page

These are companies whose own people brought up MLCommons unprompted, not accounts we guessed might be interested. We can't yet tell an implementation partner from a genuine buyer here, names unlock along with the buyer profile below.

151,426 companies · 839,012 people are researching Artificial Intelligence

MLCommons's own team shows 10 signals on this topic. No one outside MLCommons has been seen researching the company by name yet — so this is the market it sits in, not a list of its buyers.

  • Digital Innovation1,748 cos · 5,275 people
  • Executive Education11,167 cos · 45,778 people
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Buyer profile

company size · seniority

Company size and how senior the people involved are, the two things that decide whether this is a real deal. Competitor overlap isn't computed yet for this account.

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Company-size breakdown and buyer seniority mix for accounts researching MLCommons.

Company size breakdown
Buyer seniority mix

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Buying committee functions

Employee job titles (LinkedIn)

Leadership — 1 person; Product — 1 person

What's been said

public posts by MLCommons's team

No public post naming MLCommons has surfaced in the past year, so this is what MLCommons's own team is posting about publicly — their topics, in their words.

LinkedInArtificial Intelligence

We don't have good patterns for the #UX design of #AI systems that develop theories of mind of us, and of each other, and which directly and autonomously test hypotheses about the world by acting around us and on us. Devices like that are increasingly around us, and we're only going to see more of them. I wrote a blog post discussing this in more detail: https://lnkd.in/gw9St3cs The blog post contains my theory of how technology development swings from the cloud to the edge (with us in midswing from cloud to edge right now), which is something I observed in my work on #iot systems, and because I've always kept one toe in the hardware world. I also rant about terminology: specifically that when we use the term "embodied AI" to refer to connected edge devices that have semi-autonomous local AI capabilities, we imply that AI otherwise doesn't have a body. But it does: it has giant, sprawling bodies all over the place. We call them data centers, and by pretending they don't exist when we use "embodied AI", we make a serious categorization error.

May 2026
LinkedIn

I'm pulling together a small group of regulatory experts on AI in financial services/ life sciences (deliberately broad scope) to contribute to a project. If you have worked on this either in a senior risk, compliance, or model governance role inside a bank or pharmaceutical/biotech company or previously at US/EU/APAC regulator - DM me! (UNRELATED but also sharing relevant chart from Stanford HAI Index Report 2026 - link in comments)

May 2026

Leadership changes

Web / press

After 8 years co-founding and leading the development of and MLCommons, the time has come for me to hand the reins of MLPerf to a new leader.; Announcement! Dave Graham is the Head of Marketing at MLCommons, a leading AI engineering consortium, built on the philosophy of open collaboration to improve AI systems. | Alastair Cameron

M&A / partnership appetite

Web / press

If AI is going to transform healthcare, retail, and business, trust has to transform with it. That's the core idea behind PACT AI - and why MLCommons is joining as a founding … | MLCommons; Notice Pursuant to the National Cooperative Research and Production Act of 1993-MLCommons Association | 91 FR 58708

Regulatory exposure

Web / press

Federal Register :: Notice Pursuant to the National Cooperative Research and Production Act of 1993-MLCommons Association; Notice Pursuant to the National Cooperative Research and Production Act of 1993-MLCommons Association | 91 FR 46160

About this data. MLCommons (mlcommons.org). Department attribution is 65%; remaining activity is grouped under Others. Updated 2026-10-03T14:00:03.490Z.

Not yet computedPer-team narrative summaries