Sashi Shanmugavelu

Building Scout-verified human judgement infrastructure for the AI era. Prior: @GoldmanSachs, @UMNComputerSci

[Sashi Shanmugavelu @Sashi_Shan]

The JD/resume system is fundamentally broken for AI-adjacent technical roles.
Not because recruiters are lazy.
Because the skills are too new to have names.
A thread on what I think is one of the most underrated problems in the AI talent market right now 🧵

[Satya Nadella @satyanadella]

https://t.co/xv6csf1SbV

[Sashi Shanmugavelu @Sashi_Shan]

@ankurnagpal @AngelList Access

[Sashi Shanmugavelu @Sashi_Shan]

Every major AI deployment venture will hit the same wall within 12 months.
OpenAI just launched a $4B Deployment Company. Anthropic launched a $1.5B enterprise JV with Blackstone and Goldman Sachs. Both are backed by PE firms with thousands of portfolio companies to deploy into.
The capital is there. The models are there. The enterprise demand is there.
But none of that solves the real bottleneck.
Not a model problem. Not a platform problem.
A human judgment problem.
What they cannot do is reliably find, verify, and match the humans whose judgment makes those deployments actually work in production.
The humans I mean are specific:
The forward-deployed engineer who knows when a RAG pipeline is confidently wrong. The domain expert who can map the informal handoffs that no requirements doc ever captured. The practitioner who knows exactly where to put a human in the loop.
Those people exist. They are invisible to every hiring system that currently exists.
That is the wall. And nobody is building the infrastructure to solve it.
That is Scout.

[Sashi Shanmugavelu @Sashi_Shan]

Exactly.
The principal/agent framing is the right one — and it has an infrastructure implication most builders are missing.
If humans are end-to-end and AI is middle-to-middle, then the bottleneck isn't model capability. It's the reliability of human judgment at those endpoints. Right now every enterprise solves this ad hoc — informal escalation, whoever's available, personal networks. That breaks completely at production scale.
The missing layer isn't better agents. It's callable, verified human judgment as infrastructure — a skill representation and invocation layer that every agent stack can call when it hits the judgment boundary. Not a marketplace. Not a staffing tool. Infrastructure.
That's what I'm building. Would value your reaction @balajis.

[Khairallah AL-Awady @eng_khairallah1]

ANTHROPIC JUST RELEASED THE OFFICIAL PLAYBOOK FOR BUILDING A COMPANY WITH CLAUDE CODE.

30 minutes. free. from the engineers who built it.

Bookmark this before you forget.

CEO: 1 human. Employees: AI agents. Operations: fully automatic.

The zero-headcount company is no longer a joke.

[Ihtesham Ali @ihteshamali]

An MIT professor taught the same math course for 62 years, and the day he retired, students from every country on earth showed up online to watch him give his final lecture.

I opened the playlist at 2am and ended up watching three of them back to back.

His name is Gilbert Strang. The course is MIT 18.06 Linear Algebra.

Every machine learning engineer, every data scientist, every quant, every self-taught programmer who actually understands how AI works learned the math from this one man. Most of them never set foot on MIT's campus. They just opened a free playlist on YouTube and let him teach.

Here's the story almost nobody tells you.

Strang joined the MIT math faculty in 1962. He retired in 2023. That is 61 years of standing at the same chalkboard teaching the same subject to 18-year-olds.

The interesting part is what he did when MIT launched OpenCourseWare in 2002. Most professors were skeptical. They worried that putting their lectures online would make their classrooms irrelevant. Strang did not hesitate. He said his life's mission was to open mathematics to students everywhere. He filmed every lecture and gave it away.

The decision quietly changed how the world learns math.

For decades linear algebra was taught the wrong way. Professors started with abstract vector spaces and proofs about field axioms. Students drowned in the abstraction. Most never recovered. They walked out believing they were bad at math when they had simply been taught in an order that nobody's brain is built to absorb.

Strang inverted the entire curriculum.

He started with matrix multiplication. Something you can write down on paper. Something you can compute by hand. Something you can see. Then he showed his students that everything else in linear algebra eigenvectors, singular value decomposition, orthogonality, the four fundamental subspaces was just a different lens for understanding what the matrix was actually doing under the hood.

His rule was strict. If a student could not explain a concept using a concrete 3 by 3 example, that student did not actually understand the concept yet. The abstraction was supposed to come last, not first. The intuition was the foundation. The proofs were just confirmation that the intuition was correct.

The second thing Strang changed was the classroom itself. He said please and thank you to his students. Every single lecture. He paused mid-derivation to ask "am I OK?" to check if anyone was lost. He never used the word "obviously" or "trivially" because he knew exactly what those words do to a student who is one step behind. He treated 19-year-olds learning math for the first time the way he treated his own colleagues. With patience. With respect. With the assumption that they belonged in the room.

For 62 years.

The result is something that has never happened in the history of education. A single math professor became the default teacher of his subject for the entire planet.

Universities in India, China, Brazil, Nigeria, every country with a computer science department, started telling their own students to just watch Strang's lectures. The University of Illinois revised its linear algebra course to do almost no in-person lecturing. The reason was honest. The professor said they could not compete with the videos.

His final lecture was in May 2023.

The auditorium was packed with students who had never met him before. He walked to the chalkboard, taught for an hour, and at the end the entire room stood and applauded. He looked confused for a moment, like he genuinely did not understand why they were cheering. Then he smiled and waved them off and walked out.

His written comment under the YouTube video of that final lecture was four sentences long. He said teaching had been a wonderful life. He said he was grateful to everyone who saw the importance of linear algebra. He said the movement of teaching it well would continue because it was right.

That was it. No book promotion. No farewell speech. No legacy management.

The man whose teaching is the foundation of modern AI just thanked the audience and went home.

20 million views. Zero ego. The entire engine of the AI revolution sits on top of math that millions of people learned for free from one quiet professor in Cambridge.

The course is still on MIT OpenCourseWare. Every lecture, every problem set, every exam, every solution. Free.

The most important math course of the 21st century is sitting one click away from you. Most people will never open it.

[Sashi Shanmugavelu @Sashi_Shan]

@jn_jackk List

[Naval @naval]

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—

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This problem has become farcical in the last decade. Startups are reaching trillion dollar valuations in the private markets while ordinary investors have their noses up to the glass, wondering when they’ll be let in.

Investing in private markets isn’t easy. You need feet on the ground. You need judgment built over years. Most people don’t have the patience to wait ten or twenty years for an investment to come to fruition.

But there is no more productive, harder-working way to deploy a dollar than in true venture capital.

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[Sashi Shanmugavelu @Sashi_Shan]

@ParagArora Parag — curious when hiring for early-stage teams, how do you assess what someone can actually do vs their resume or title?
Feels like this breaks down especially for AI roles where the work doesn’t map cleanly to titles.

[Sashi Shanmugavelu @Sashi_Shan]

@Jason I'm in

[Sashi Shanmugavelu @Sashi_Shan]

@agupta Building https://t.co/J6GT1Ri0xM — infrastructure for human skills in the AI era.
Most broken hiring problem in AI: skills too new for titles, work too fluid for JDs.
Think what Stripe did for money — but for human judgment.
India-based founder. Happy to share more.

[Deedy @deedydas]

Every single book that influenced Demis Hassabis and DeepMind (and how it inspired them), from “Infinity Machine”.

Classics like GEB, Hitchhikers, Foundation series, Culture series and modern ones like the Altman influences.

Bookmark this. Required reading all technologists.

[Jared Friedman @snowmaker]

We had room for 2,000 people at Startup School India.

More than 25,000 applied.

No Startup School anywhere in the world has ever had this many people apply. Not SF, not NYC, not London. India blew them all away.

[Garry Tan @garrytan]

Founders who are the realest actually do this

[Sashi Shanmugavelu @Sashi_Shan]

@AlfieJCarter CLAUDE

[Garry Tan @garrytan]

I'm telling most everyone I know that they should build a personal OpenClaw

[Sashi Shanmugavelu @Sashi_Shan]

Even 2–3 conversations would be incredibly valuable — especially with people whose work doesn’t fit their title.

[Sashi Shanmugavelu @Sashi_Shan]

The JD/resume system is fundamentally broken for AI-adjacent technical roles.
Not because recruiters are lazy.
Because the skills are too new to have names.
A thread on what I think is one of the most underrated problems in the AI talent market right now 🧵

[Sashi Shanmugavelu @Sashi_Shan]

Before I build, I want to understand.
If you’re working in AI and your role doesn’t fit a job title —
let’s talk for 60 mins.
Not recruiting. I’ll map your capability more precisely than any resume.
Reply or DM 👇

[Sashi Shanmugavelu @Sashi_Shan]

To be specific — I’m looking for people making judgment calls no model can replicate.
Who know when evals lie.
Who can spot a wrong architecture before metrics show it.
Who’ve figured things out not documented yet.
That person.