Sri Ramamurthy

Tech junkie interested in Product Strategy, Business Operations, Sports, Travel and Food. Syracuse Grad. Orchestrator of Business and Technology Realms.

[Xiaoyin Qu @quxiaoyin]

My bet: @thinkymachines will soon make more money than @AnthropicAI. Not by winning the race to build one standardized frontier model. By becoming the Palantir FDE for enterprise custom models.

The playbook:
1. Release the best American open-weight model.
2. Drive widespread enterprise adoption.
3. Charge the largest companies 7–9 figures to post-train and run custom models behind their own firewall.

The model rests on three bets:
1. Large enterprises will increasingly demand their own models with their own data, and this is how they differentiate and win.
2. Enterprises won’t need just one model. They’ll continuously need new models for different workflows, departments, and proprietary datasets. That creates extremely sticky, recurring revenue.
3. Autoresearch will make custom model development increasingly scalable. Tinker can become the interface enterprises use to post-train their own models—with @thinkymachines providing the expertise and infrastructure behind it. FDE, infra, everything, huge contracts.
4. Eventually, maybe everyone wants their OWN model, and autoresearch and training inside tinker on top of @thinkymachines's base model will make it happen.

Meanwhile, Henry-ford-styled, standardized models will makes no margins. OpenAI and Anthropic will have their API margins squeezed by Deepseek/GLM/Grok/Meta etc, and their consumer subscriptions are loss centers.

The fat margin will move to customization: proprietary data, post-training, evals, deployment, and infrastructure.

If this thesis is right, @thinkymachines isn’t building just another frontier lab. It’s building the highest-value layer between frontier research and enterprise model ownership.

Turns out, the best business model for enterprise is NOT to sell commodity API access. Sell them their own models.

I’m extremely bullish on this approach.
@miramurati may be the most commercially savvy frontier-lab leader. I have to admit it.

[Satya Nadella @satyanadella]

https://t.co/xv6csf1SbV

[codila @0xCodila]

Anthropic's Andrej Karpathy to Andrew Ng:

"Most people uses AI by importing a library, don't - write the whole thing by hand

that's what I've been thinking about a lot at OpenAI "

how he actually learned it:

→ wrote his own neural net library by hand - just to understand backprop
→ never abstracted a single layer he didn't build
→ only then touched a framework

the payoff: "if you don't build it, you can't debug it, and you can't improve it"

Ng backed him up - people who can debug ML are 10-100x faster than the rest

two AI legends in one room - watch it, then read the loop guide below ↓

[The Techno Optimist @technooptimist1]

Chamath Palihapitiya @chamath "Bro, there is no failure."

"That is what other people think of you to keep you down. There's no failure. If you were left on an island and it happened, you would just brush it off and move on. It's everybody else that you think is judging you. But then, here's the secret. They don't give a fuck about you. They are living their own lives. So it's your perception of what they think of you."

"There is no failure. There's do and learn. Do and learn. Do and learn. Do and learn."

@StanfordAILab

[Nick Dorsey @Midnight_Captl]

Wow… this is amazing

This latest episode of the @theallinpod is a complete referendum of closed source AI (ie. Anthropic/OAI) in a way I haven’t yet heard from industry leaders

Open source AI is about not just have a moment. It’s about to be a monsoon. A flood of demand. Watch the clip here from @friedberg.
There’s a huge vibe shift underway. The move away from proprietary AI that captures companies IP (Ant/OAI), to Open source AI that is sovereign and owned by enterprises

This is the future that Satya, Karp, and Jensen have been speaking about. I expect a tidal wave of demand for open source AI to be on our doorstep after the 4th of July festivities are over. GPU rental rates are about to soar once again

Incredibly bullish for $NVDA $PLTR and $MSFT : all key partners in enabling this future

[Eminent_minds @minds_eminent]

SIGNS THAT A MAN IS GROWING IN SILENCE AND NO ONE KNOWS IT YET:

1. He stopped explaining his plans to everyone

[Gokul Rajaram @gokulr]

MEMORY IS THE MOAT

@nikesharora, Chairman & CEO of @PaloAltoNtwks , interviewed by @HarryStebbings (@20vcFund )

Summary: Nikesh Arora took Palo Alto Networks from an $18 billion company to one worth $225 billion, and his read on enterprise AI is blunt: most companies are doing it wrong, and most of the products are not ready. His core claim is that consumers forgive AI's mistakes while enterprises cannot, so the money will flow to whoever builds the depth (the context, the memory, and the edge-case training) that lets an agent act without a human catching its errors. The companies that win will redesign themselves around AI instead of adding it to yesterday's workflow, and the lasting advantage will be the memory a system builds up about you. He expects token prices to fall 90%, half of G&A roles to disappear in 3 years, and more engineers and salespeople, not fewer.

1. Context Stickiness. The lasting advantage in AI is the context a system holds about you, not the model itself. Arora says the frontier labs are racing to remember what you asked over the last 30, 60, 90 days so each new answer gets easier and you stop wanting to leave. The more a model knows about a user, the higher the cost of switching, and that stickiness is the moat. For enterprises the same logic holds: the company that owns its context wins, not the one renting the smartest model.

2. Breadth Versus Depth. The frontier model problem is a breadth versus depth problem. Consumers tolerate false positives and enterprises have none to spare. Arora had Gemini write a passable investment memo in 4 minutes, and a wrong line or two did not matter because a person was sitting in the middle to catch it. An agent acting on its own has no person in the middle, so a false positive becomes a live failure. Consumer AI wins on breadth and brand, while real enterprise revenue comes from depth.

3. The Waymo Standard. Waymo is the biggest agentic product in the world, and it shows what depth actually costs. Replacing one human, the driver, took tens of billions of dollars of edge-case training and data that exists nowhere on the internet. You cannot drop the next Anthropic model into your Mercedes and tell it to drive you home. Every enterprise agent that truly replaces a person needs that same depth, which is why most agentic enterprise products are not ready.

4. Rethink The Workflow. Most enterprises are losing because they add a little AI to an old workflow instead of redesigning the workflow around AI. Arora's example: scanning an invoice 20% faster is the trap, while the real win is letting AI do 80% of the thinking, like reading every CV and telling you which 20 people to interview and what to ask each one. That means giving up human control, which is exactly what companies resist. The winners over the next 3 years rethink the company with AI, not the task.

5. Software With Opinions. The next wave of enterprise software will have opinions, and that is the real change Arora is pointing at. Coded SaaS gives you the output you defined for the input you fed it. An AI marketing assistant reads your copy, tells you it is off-brand, and says how to fix it. That opinion makes an average employee smarter, which is why Arora expects half the people in G&A functions like marketing, finance, and HR to be gone within 3 years.

6. More Engineers, Not Fewer. The fear that AI shrinks headcount is half wrong. Process-heavy G&A roles compress, but Arora wants more technical and more sales people. His teams keep asking for resources to rework marketing and HR, and for people who can prompt frontier models, build harnesses, and bring in data nobody else has. A good product also needs more sellers: he met 20 customers in Europe last week and half did not know what his 20-year-old company already ships.

7. Tokens At One-Tenth. Long-term token pricing should be a tenth of what it is today. Compute costs 2 to 4 times what it did 2 years ago because more than half of it feeds loss-making consumer AI, which forces the pricing pressure onto enterprise and coding workloads that have to pay. As compute gets more efficient and consumer usage gets capped, prices fall hard over the next 3 to 5 years. The model from 2 years ago was already good enough for 90% of tasks; the problem was it cost too much to run.

8. The Token Allocation Trap. Capping token spend punishes your best people. Arora runs a "use judiciously" model, not a free-for-all, because the smartest AI-savvy employee can burn 20 times the tokens of an average one. Playing whack-a-mole with cost hurts the high performers most and slows the learning you need. The better move is to track usage, leave the power users alone, and cap only the genuine outliers.

9. The Attacker's New Edge. Powerful coding models cut both ways. Trained to write good code, they are just as good at finding bad code. Pointed at his own systems, a model found in 6 weeks what would have taken his team 5 to 6 years. It cannot safely auto-patch, because it would "fix" 30% of things that are not broken, so it arms attackers faster than defenders. The result is urgency: every enterprise has to fix its systems faster, which is good for security companies.

10. The FTE Tell. If a startup needs forward-deployed engineers to sell into the enterprise, the product is not finished. Arora's read: enterprise AI is barely 12 months old, agents keep changing what the product even is, so vendors send engineers to build the product inside the customer while the technology keeps moving. A real forward-deployed engineer brings code back and folds it into the product; many are just adoption consultants. Expect customers to churn from one tool to the next, the way coding went from Windsurf and Devin to Codex, Claude, and Factory.

11. Three Missed Tricks. Miss one trick and you survive, miss two and you are partly impaled, miss three and you could be obsolete. This is why Arora spends more time than ever learning, pinging founders building things he does not yet understand. He buys early and cheap on conviction, treating an acquisition as a 10x or 100x bet where paying 1 or 2 times more does not matter, rather than waiting to buy the proven winner for a billion. He runs a twice-weekly "AI EIO" meeting so his top 15 leaders compete to show what they shipped.

12. The Sunk Cost Walk. A board member taught Arora to separate effort from wanting the outcome. After months grinding through a near-billion-dollar acquisition, he was told to take a long walk and ask one question: if this deal walked in the door right now with zero effort, would I still write the check? You have not spent a dollar yet, so the only thing that counts is whether it stands on its own merits. The same trap catches investors who confuse beating 8 VCs to a term sheet with the deal being good.

[Xiaoyin Qu @quxiaoyin]

American and European enterprises will ditch OpenAI and anthropic and adopt Chinese models. Here’s why:
1. They can host Chinese models under their own GPUs so it’s still compliant and they would argue they have more control.
2. they will post train with their own data on top of Chinese models. That’s how they build data moat.
3. They will not trust anthropic who will retain their data at any time for “safety” concerns like how they did with Fable and then try to build the same thing like how anthropic did with healthcare and legal.
4. They need to justify their AI spend and ROI.
The cure is a reliable America open source model but there is none. After all, if giving away all your data and AI control at the mercy of anthropic and OpenAI means you care about safety and compliance, you are outright stupid.

[Ihtesham Ali @ihteshamali]

Marc Andreessen says Alex Karp almost never talks about Palantir in interviews. He calls it the single best marketing strategy he has ever seen and then revealed the number that proves it works better than anything else in the history of investor communications.

Every founder makes the same mistake. They think inside out. My company, my product, my story, out into the world. It feels natural. It is also why most founder content is indistinguishable from every other founder's content.

Karp does the opposite.

He talks about the future of the US military. He talks about superintelligence. He talks about whatever is genuinely interesting to him about the world right now. And because he is the CEO of Palantir, the company just sits there attached to all of it.

Then Marc dropped the number.

What percentage of Palantir investors have read the S1? Practically zero. What percentage have seen Karp on YouTube? Close to 100.

A Edelman B2B study found that thought leadership content drives purchasing consideration more than product marketing does — by a factor of nearly three to one among enterprise buyers. Karp did not read that report. He just built the playbook it describes.

Palantir's lawyers spent thousands of hours on the S1. It explains everything the company does with full precision. Nobody read it.

Karp spent those hours talking about things that interested him. Everybody watched.

The most effective investor communication Palantir ever produced was never filed with the SEC.

Watch the full video on @a16z YouTube channel

[Nikesh Arora @nikesharora]

The AI Business model trap: LLMs want cash flow to fund the race to AGI or the next model. Enter free consumer AI - they are losing a lot of money on the breadth of models to serve consumers for free! They are caught in the post training data trap, free consumer usage feeds post training needs, it can't be right to stop serving customers for free?

But they need money for the compute:

The monetization challenge is being pointed to Enterprises.

Phase 1 - seemed easy, value capture in coding, the most bottom up motion in enterprise - with low customization per customer. Developers continue to train coding, tasks and eventually will train flawless skills.

Phase 2 is where the challenge lies, showing true enterprise value. The promise of efficiency, accuracy, elimination of resources - that requires a different approach, build depth with harnesses, context, memory, solving for edge cases with deterministic guardrails! Build skill libraries - enter FDEs. Yes,FDEs will train the enterprise Waymos of the world.

The risk - high token pricing for enterprises while consumers for free! Yes for consumer distribution businesses (aka Google, Meta, Apple, etc) it makes sense to hold on the distribution with free AI.

If you want to win enterprise, you should be forward pricing tokens. The cheaper the tokens for enterprises it will allow for experimentation, workflow reimagination - instead CIOs are busy restricting AI use and working on making the use more efficient!

Paradox: They still haven't fully understood and embraced the value of AI in the enterprise.

If I were them:
1. Cut token pricing now, else send enterprises to secure opensource and end up with friction filled routing layers.
2. Show me how enterprises can use their context, training and data as their competitive advantage.
3. Build tools for rapid edge case learning and reducing false positives.

@HarryStebbings
@sama @DarioAmodei @demishassabis

[Seven Screen Studio @7screenstudio]

Some men belong to an era.
Some men become one.

To the man who mastered both
and became a phenomenon of his own ♥️

Wishing our dearest Hon'ble Chief Minister,
Vijay sir, a very Happy Birthday ♥️

@actorvijay @TVKVijayHQ #HBDCMJosephVijay

[Aesthetic Gentleman - Fitness | Fashion | Health @BAestheticMan]

Most expensive color combinations

[Codie Sanchez @Codie_Sanchez]

Ex-Amazon VP shares 3 lessons he learned working with Jeff Bezos:

[Priyanshhh🤫 @heyprabhshere]

Therapist @actorvijay ♥️🫂

[sanjay/alterego @trust7heprocess]

Vijay song without vijay🔥🔥🔥🧨

[Gokul Rajaram @gokulr]

THE TOKEN HANGOVER

@matanSF (Matan Grinberg), CEO and co-founder of @FactoryAI , interviewed by @HarryStebbings (@20vcFund )

This is a special for me since I've been an investor in @FactoryAI since their seed round, and think Matan is a very very special founder.

Summary: Grinberg argues the next 24 months in enterprise AI are a resource-allocation problem: tokens, dollars, and people. Most CIOs are now waking up to bills they cannot justify. The fix is to spend frontier tokens only on the 10-20% of work that requires planning intelligence, run the other 80-90% on open models, and rebuild teams around load-bearing polymaths who own business outcomes. The single-frontier-monopoly fear is fading: four roughly-equivalent labs is the emerging reality, which puts pricing power back in the application layer.

1. The Token Hangover. Enterprise AI adoption ran through three phases this year: boards yelling at CEOs about AI strategy, "token maxing" with AI usage written into perf reviews, and now the morning-after bill. One CIO Grinberg spoke to was spending hundreds of thousands of dollars a month on engineers asking Opus 4.8 things like "how's it going" and "what are my macros from lunch." The frontier model became the default surface for every question, no matter how trivial. Phase 3 is the moment routing matters: every call to a frontier model needs to earn its price.

2. Resource Allocation Is the Job. For the next 24 months every C-suite is solving the same problem: how to allocate dollars, tokens, and headcount against business outcomes. Engineering teams used to be judged by features shipped per quarter, a metric with no link to revenue, market share, or retention. A logistics company adding more engineers to ship more features was always solving the wrong problem; AI made the misallocation visible. Tie every person's work to the metric that actually moves the business, then re-allocate.

3. Load-Bearing Individuals. The "10x engineer" frame measures lines of code, the wrong unit. Grinberg's unit is the load-bearing individual: the person whose absence breaks something. With AI the load-bearing few compound roughly 10,000%; the others get close to nothing, so any org enforcing one token-spend-per-engineer number is painting with too wide a brush. Average token spend per engineer will land on the same order of magnitude as their salary within three years, with a wildly bimodal distribution.

4. Frontier for Decisions Only. 80-90% of software development tasks can run on open models; the remaining 10-20% is planning, where the frontier still wins. This mirrors how human orgs work: leadership is a tiny share of total hours but decides the company's fate. The ego trap is engineers assuming their work is too important for an open model. The router decides better than the engineer, and the cost curve falls only if you wire the routing.

5. The Kirkland Mistake. Kirkland & Ellis announced a $500M, five-year internal AI build, which Grinberg reads as validation for Harvey rather than a threat. Building AI is not a law firm's core competency, and Kirkland's spend will teach them how hard it is. The general rule: just because you can build it does not mean you should, and the discipline is naming the few things you and your team own end-to-end. Outsource everything else, even when you technically know how to do it yourself.

6. Model-App Separation. When the model provider also sells the app, the incentives split: an API business wants you to spend more tokens. A healthy market keeps the application layer independent, so model providers compete on price, speed, and quality every week. Enterprises do not want to vendor-lock again; every CIO carries scars from the cloud era's three-year discount-then-jack-the-price trap. The application layer survives precisely because it forces that competition.

7. Sales as Product. Name a legendary company with a weak sales or marketing team. You can't. The Silicon Valley fallacy that research sits at the top and sales is "dirty work" produces companies that win the gold rush and then collapse when gravity returns. At Factory, engineers and salespeople sit intermixed; when sales closes, engineering says "we closed"; when engineering ships, sales says "we shipped." Atrophied sales muscles will not regrow once enterprise buyers stop saying yes to everything.

8. Polymath Era. Da Vinci, Newton, Euler could be polymaths because their fields were shallow. By the 2010s a theoretical physicist needed 50 years to reach the frontier before contributing anything new. AI collapses that catch-up time, so one person can push forward developer marketing, token-caching infrastructure, and solution engineering at once. The engineer of the future is a GM who owns marketing copy, product metrics, and sales enablement.

9. Build the Factory. Factory's name is literal: engineers in the next era design the assembly line that produces software. The DevX investments that used to scale linearly with headcount (good docs, CI/CD, linters, pre-commit hooks) now scale with the number of agents you run, which is 10x or 100x larger. Every dollar spent making agents production-ready compounds against thousands of PRs a week. Humans move up the stack, from writing code to designing the system that writes code.

10. Seal Team Six. Mandating beds in the office is a hiring failure dressed up as commitment. Grinberg's image: a basketball game judged by who sweat the most, when the scoreboard is what counts. Factory bought eight sleeps for all 30 team members at the time, because recovery is where the gains come from when work requires every ounce of brain power. If your load-bearing engineer can do their best work on two hours of sleep, they were not doing load-bearing work in the first place.

11. Four Frontier Labs. Grinberg's biggest mind-change this year: a single dominant model is unlikely, and four roughly-equivalent frontier providers is the more probable steady state. That outcome is the win for humanity. A one-lab monopoly was the dangerous scenario, and four equivalent labs is also the structural bull case for the application layer because it forces real ongoing price competition. Every CIO Grinberg meets has already decided not to throw their lot in with a single provider.

12. Dario's Self-Serving Doom. "AI will take your jobs" was the pitch that helped raise hundreds of billions, and Grinberg thinks it damaged public psychology and fed the slow-AI lobby. Watch the rhetoric flip at IPO: humans will suddenly become important again, because humans are the ones buying the stock. Founders who never needed to raise that money, like Zuckerberg and Hassabis, never made that argument. Incentives drive the labor-displacement rhetoric more than philosophy does.

[EPIC MASCULINE @EpicMasculine7]

Stop asking her childish questions…

These questions will expose who she really is 🧵

[ɪꜱᴀᴀᴄ🤍 @isaac_editz]

Yeppo paathalum intha video Cute thaaan 🤍🤌🏻
@TVKVijayHQ 🎀