Aspiring Polymath, Good Fight Instigator, Humane Capitalist, Dad Joke Connoisseur; Cofounder JioSaavn; Partner 212MEDIA
NYC|MTV|BOM|DEL|BLR and Zoom
Joined March 2007
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Vinodh Bhat retweeted
Lonely and Right
Keynes said picking stocks is like a newspaper beauty contest: you don't win by picking the prettiest face, you win by picking the face everyone else picks.
Ninety years on, that's still the most honest description of my job which is sad because I think I know beauty.
Now the tortoise and the hare. Everyone tells it as "slow and steady wins." That's not the story. The story is that for 90% of the race the tortoise is losing. Badly. In public. The hare is at the cocktail party. The hare's LPs are thrilled. The hare is on the list. Then the tortoise wins at the end, and we tell it to children, and we leave out that the tortoise spent the whole race feeling like a fool.
So be honest. Would you rather win 90% of the race and lose at the end, or lose 90% of the race and win? Counted in years of your one life, the hare has the better deal. That is venture capital right now. The young partner in a venture firm would rather be the hare and jump to the next firm than be lonely as the tortoise.
The consensus deal is the hare. Expensive, crowded, fun. Everyone at dinner knows the company. The marks go up, the capital piles in, and for years you feel like you're winning, right up to the moment the music stops, which is the only moment that counts. The contrarian deal is the tortoise. It pays only if you're contrarian and right, and you find out which at the end. Until then you're on no list, nobody at dinner has heard of your company, and you look slow. Early and wrong feel identical from the inside. That's the whole difficulty.
So the real trade was never consensus versus contrarian. It's finding the thing that's contrarian today and consensus in eighteen months.
Buy the tortoise, then watch it grow legs. Cheap entry, then the multiple expands when the crowd arrives, and then you're back at the cocktail party being congratulated on your foresight by the same people who thought you were lonely.
Lonely and right, then crowded and rich.
That's the business. Finding the ugly duckling that's going to become a swan - that's where the alpha is.
What's contrarian now that I think flips? Held loosely:
1. Outcomes, not tokens. Everyone is selling the enterprise intelligence by the pound. The enterprise doesn't want intelligence. It wants the claim processed, the invoice reconciled, the ticket closed. One in five companies has AI in production; one in fourteen has rebuilt the factory. Whoever sells the other thirteen a finished outcome, priced like a result and not like a utility bill, wins the decade. It looks like a services business on the way in. So did Amazon.
2. Security that gets paid for the breach that didn't happen. The attackers have agents now, so every company on earth is about to be robbed at machine speed, and the defender's product is still a dashboard. The bet is the company that stops selling the dashboard and starts selling the result: we handle it, we're on the hook. Security and insurance were always the same business. Someone is about to notice.
3. The specialist labs. Not the frontier, which is consensus at a trillion, but the labs that do one thing for one industry at a hundredth the price.
4. The boring half of the physical world. Robotics is loud. Inspection, maintenance and warehouses are quiet, and the revenue lives in the quiet part.
I may be wrong about all four. That's rather the point. If I were sure, so would everyone, and then it wouldn't be a trade. It'd be a cocktail party.
Vinodh Bhat retweeted
Elon Musk unveils new Tesla Roadster by launching it off a ramp flipping the car mid-air and safely landing.
Vinodh Bhat retweeted
Cavemen Issues Dire Warning That The Wheel Is Going To Eliminate Countless Jobs
Vinodh Bhat retweeted
I think @evanspiegel is actually making sick progress on SPECS and Snap is going to crush it when these get to < $400
SPECS bring useful digital experiences into the world around you while keeping you present in the moment. From work and navigation to entertainment, creativity, and connection.
SPECS.com
Vinodh Bhat retweeted
Great video by Andrew Schulz (via IG) on what it means to be a New Yorker and what it meant when the Knicks won. Certainly resonates with me. How we feeling?
Vinodh Bhat retweeted
WELCOME TO THE AI BOWL
And if you've seen this movie before, you already know how it ends.
Tonight, while 130 million Americans watch the Seahawks and Patriots, they'll also witness something that should make every serious investor deeply uncomfortable:
The single largest concentration of AI advertising in television history.
16 tech companies bought Super Bowl ads this year.
OpenAI, Google, Amazon, Meta, Anthropic, Genspark, Base44, Rippling, Ramp - and more.
Tech ad spending is DOUBLE what it was during the 2022 "Crypto Bowl," the last time NBC broadcast the game.
At $8-10 million per 30 second spot, plus production costs pushing all-in budgets north of $20 million per ad, hundreds of millions of dollars are being spent tonight by companies trying to convince you their AI product will change your life.
I've been in markets for 45 years.
When an entire sector floods the most expensive advertising real estate on the planet, it's not a signal to buy. It's a signal to think VERY carefully about what comes next.
My friend @DougKass has been beating this drum for years with his "Stock Market Super Bowl Indicator".
The more intensely a single sector dominates Super Bowl advertising, the more likely those stocks underperform in the year ahead.
Doug's been right far more often than he's been wrong on this one.
Because we've seen this exact playbook twice before...
2000: The Dot-Com Bowl. 14 internet startups bought Super Bowl ads at $2.2M per spot. Pets .com spent $1.2M on its famous sock puppet commercial.
Within 10 months it was dead - stock went from $11 to zero.
8 of 11 startups that advertised were bankrupt or sold in fire sales within a year.
2022: The Crypto Bowl. FTX, Coinbase, Crypto .com, and eToro collectively spent $54 million on Super Bowl ads.
Larry David told 100M viewers "Don't miss out on crypto."
9 months later, FTX was bankrupt.
Bankman-Fried is now in prison.
Coinbase shares fell 70% within a year.
By the next Super Bowl, crypto had "zero representation."
Now 2026: The AI Bowl.
And the parallels are impossible to ignore.
The Duke CFO Survey tells us the vast majority of CFOs report "no change" in productivity, decision-making, or customer satisfaction from AI investments over the past 12 months.
US tech giants spent $380 BILLION on AI infrastructure in 2025. Goldman Sachs says measurable GDP impact doesn't start until 2027.
The companies SELLING AI tools are getting rich. The companies BUYING them can't measure the returns.
And tonight, the sellers are spending record sums to convince even more buyers to pile in.
The data on Super Bowl advertisers as investments is damning.
Bridgewise found that after 6 months, companies running Super Bowl ads trailed the S&P 500 by an average of 9.2%.
Only 25% outperformed the index at 6 months.
Now look at who's advertising tonight:
OpenAI and Anthropic are pre-IPO companies burning billions in cash, racing to prove they can monetize before capital markets demand accountability.
Genspark and Base44 are startups most Americans have never heard of, spending $8-10M per spot to introduce themselves.
The real rivalry tonight isn't Seattle vs. New England.
It's Anthropic vs. OpenAI, fighting for "mindshare and legitimacy as both prepare for massive IPOs."
That's a NARRATIVE CAMPAIGN to justify valuations before public markets scrutinize the books.
Every bubble has a moment where the hype sector dominates the Super Bowl.
Dot-coms in 2000. Crypto in 2022. AI in 2026.
Peak advertising spend correlates with peak euphoria, not peak fundamentals.
Dot-com companies told you to click.
Crypto companies told you not to miss out.
AI companies are telling you the future is here.
The future may well arrive. But the stocks are priced like it already delivered and exceeded expectations.
It hasn't.
Enjoy the game tonight.
But the best contrarian indicator in markets isn't some fancy algorithm.
It's a $10 million Super Bowl ad.
Vinodh Bhat retweeted
"I only use the Brave browser and Brave Search for this reason. I don't want my search history saved somewhere. F--k that." - Jason Calacanis on the latest episode of @theallinpod
Thanks for the shoutout, @jason! Proud to be your choice for private browsing and search!🫡
Vinodh Bhat retweeted
1. As the third and final electoral verdict on Donald Trump - and a potential generational shift to Kamala Harris - this election will do much to determine the future of US foreign policy and how the world looks at us.
Vinodh Bhat retweeted
After his second year at Michigan, Tom Brady wanted to transfer.
He wasn't playing in games, and he was so low on the depth chart that he only got 2 reps in practice.
Brady met with his coach to express his frustration, “The other quarterbacks get all the reps.”
Coach replied,
“Brady, I want you to stop worrying about what all the other players on our team are doing. All you do is worry about what the starter is doing, what the second guy is doing, what everyone else is doing. You don't worry about what you're doing.”
Coach reminded him, “You came here to be the best. If you're going to be the best, you have to beat out the best.”
And then he recommended that Brady start meeting with Greg Harden, a sports psychologist who worked in Michigan's athletic department.
Brady went to Harden's office and whined, “I'm never going to get my chance. They're only giving me 2 reps.”
Harden simply replied, “Just go out there and focus on doing the best you can with those 2 reps. Make them as perfect as you possibly can.”
“So that's what I did,” Brady said. “They'd put me in for those 2 reps, man, I'd sprint out there like it was Super Bowl 39. 'Let's go boys! Here we go! What play we got?'”
“And I started to do really well with those 2 reps. Because I brought enthusiasm, I brought energy.”
Soon, it went from getting 2 reps to getting 4 reps. Then from 4 to 10, “and before you knew it,” Brady said, with this new mindset that Greg instilled in me—to focus on what you can control, to focus on what you're getting, not what anyone else is getting, to treat every rep like it's the Super Bowl—eventually, I became the starter.”
Takeaway 1:
Greg Harden telling Brady to just focus on being great during his 2 reps reminded me of a piece of advice from the entrepreneur Mark Cuban.
“People come to me all the time and tell me they're stuck,” Cuban explained. “They're stuck in a job they don't like. They're stuck working for a boss they don't like. They're stuck on a team they don't like.”
“I just tell them, 'Be great.'”
“The reality of life is that you can't just always quit your job. You can't just always go to your boss and say, 'Give me the promotion, or I'm out of here.'” You can't just always go to your coach and say, 'Give me more reps, or I'm transferring.'
“So when you're stuck, you've gotta find it within yourself to say, 'Ok, this is where I am. And if I'm going to be here, I'm going to be great.'
Because if you're great at your job, typically other people and companies find out, so it creates opportunities.”
Takeaway 2:
I've written before about “lead measures”—the actions and behaviors that predictably drive success.
The core characteristic of a lead measure, the authors of The 4 Disciplines of Execution (4DX) write, is that “a lead measure is influenceable; it can be directly influenced by you.”
To achieve your goals, they recommend (echoing what the Michigan Coach told Brady), apply a disproportionate energy to the things that are in your control.
Starting at Michigan and for the rest of his career, that’s what Brady did, that’s what drove his success.
In his first media call after he was selected by the New England Patriots with the 199th pick in the 2000 draft, Brady was asked: “Are you aware that [along with starting quarterback, Drew Bledsoe] there’s another quarterback here that they drafted last year?”
Brady said he was aware of that. “And I know he’s a heck of a player,” Brady said. “But I’ve always really concerned myself just with the things I can control. I don’t put a lot of thinking into the other guys because I know I’m not at my best when I’m not just thinking about playing as well as I possibly can.”
- - -
“I never once in my life ever said I wanted to be the best of all time. Ever. I wanted to be the best I could be, period. I learned that in college. It didn’t matter what the other guys were doing. It mattered what I was doing.” — Tom Brady
Follow @bpoppenheimer for more content like this!
Vinodh Bhat retweeted
Wow. As we exit the Age of ZIRP Excess, great to see a poster child for the Age of Efficiency. Just $15M of VC to get to $600 M in revs and billions in market cap! Instead of celebrating billions raised, perhaps we can celebrate how scarcity drives innovation. @abialecki 🚀
Klaviyo built their business the old fashioned way - with capital efficient profits! Awesome to see a software company hit GAAP profitability before going public. They're rewarded with a top 10 software multiple today. Altimeter is excited to participate in the IPO $KVYO
Vinodh Bhat retweeted
Tesla's FSD v12 is a REALLY big deal.
Last night @elonmusk livestreamed a ~45-minute video of a Model S driving itself around using Tesla's latest self-driving software, FSD v12.
Self-driving is something the company has been trying to solve since Tesla Autopilot was released in 2015. Since then, Tesla has made steady progress improving the code to handle all kinds of road situations, has added cameras around the car, and removed sensors, all the while rewriting the code multiple times to solve for things the car couldn’t handle.
And even though Tesla cars can drive themselves in many situations, the biggest challenge with self-driving cars is the thousands (or millions) of situations drivers face on a daily basis that are completely unexpected or difficult to solve for, like other drivers acting irrationally, inclement weather, debris on the road, weird (or lack of) lane markings, etc.
Up to this point, Tesla and other companies have to spend a large amount of time running through simulated scenarios to generate code that would teach the system how to handle these situations. This code is oftentimes written by a human and needs to account for every variation of something happening on the road. And even then, there are thousands (or millions) of situations that a simulation won’t come with, since real life is so damn complicated and complex.
The approach many have taken to try and solve this problem is by overfitting their self-driving cars with a ton of different sensors like LIDAR, radar, ultrasonics, cameras, and other sensors in addition to generating High-Definition maps of the areas where the cars are meant to be driven in. They’ve done this in hopes that there’s a combination of sensors and map data that would allow them to solve for the most unexpected situations. Here’s a picture of a GM Cruise vehicle that uses this approach.
However, yesterday’s video has demonstrated a breakthrough in how self-driving cars can operate.
Instead of using many sensors and hardware on their cars to process the world, Tesla is using 8 cameras and a computer that’s specifically built to process video data. That’s 9 total parts vs everyone else’s 30, 40, 50+ parts to process the world.
Using only vision to process the world and not needing things like LIDAR, radar, and other sensors to interpret the physical objects around the car is impressive enough, but HOW the car learns to do this is what the real breakthrough is.
With FSD v12, Tesla takes the video data that is collected by its fleet of ~4 million cars and runs it through an AI that is built using Neural-Nets (like ChatGPT but for the real world), and then the AI figures out what the car should do based on what it sees.
What’s important to highlight here is that Tesla has done 0 work in telling the car how it should interpret the world. That means that the AI doesn’t explicitly know what a lane is, what a traffic light is, what a stop sign is, what a cone is, what a pothole is, what rain is, etc.
What Tesla does is it shows the AI a metric-ton of video of a car driving around using the 8 cameras that are outfitted around the car, and the AI learns how to do the same. The more video Tesla feeds it, the better the system gets. The more unique situations that are collected from the fleet of Teslas, the better the system gets at accounting for those situations.
This “AI code” will then be beamed to every Tesla in the world, and the on-board computer will be able to process its surroundings without the need to connect to Tesla’s AI server. It’s no different than you or I getting into a car and driving it. Instead of our 2 eyes collecting video around us and using our brain to process the info, the 8 camera system will collect the video and Tesla’s on-board computer will process it using the “AI code”.
In other words, Tesla has moved away from humans figuring out how to write code that tells the car what to do, and instead feeds video to an AI, and the AI figures out the best way to account for every situation.
The only thing Tesla needs moving forward is more video and more compute power (chips) to process videos.
That’s it.
This is profound. Everything that is captured on video with the 8 camera system is something the AI will be able to figure out how to navigate through. Snow. Potholes. Deer. Cyclists. Aliens. You name it. And this “code” that is generated by the AI will get better and better as Tesla’s compute capabilities grow with their purchases of NVIDIA’s H100 chips and the build-out of their in-house DOJO compute system. Not to mention the growing fleet of Teslas that will capture more and more video data around the world. Every Model S, 3, X, and Y that is sold today is a video-capturing robot that feeds the AI. And every Cybertruck and Compact Car will be the same.
And believe it or not, it gets even crazier.
Now that Tesla has come up with a real-world data collection and processing system with its cameras and on-board computer, this same system can be used on other physical products that can learn how to move around its surroundings.
This is where Tesla’s Optimus Bot comes into play. Tesla will be able to use its 8 camera system on the Tesla Bot to collect video of its surroundings, beam it back to the AI, the AI figures out how to best do that thing that it’s collecting video for, and then beam back the “AI code” for the Bot to process with its on-board compute.
We are not far away from a world where a humanoid robot watches you do the dishes, sends back the data to the mothership to process it, and then the next morning the Bot has learned how to do the dishes - not only using the video it gathered from you doing it, but from every other person in the world washing dishes.
Do this process with literally anything.
ChatGPT showed the masses what the potential of AI can be. NVIDIA showed the masses just how much demand there is for hardware that is used by AI systems.
And now, Tesla just showed the masses what AI means for real-world physical applications.
The world has changed - again.
Vinodh Bhat retweeted
In 2002 - Amazon's stock was down 95%, employees were jumping ship, and investors had completely given up on the company - Bezos stood up at our all-hands with an inspiring message: "Everyone hates our biz because it's hard, but that's exactly why I love it. Hard is our moat!"
Vinodh Bhat retweeted
This shot it just RIDICULOUS from Tsitsipas 😱
(via @rolandgarros)
