jovin

quant that likes philosophy :)
@ucberkeley x @georgiatech

[jovin @jovinxthomas]

philosophically, we're in a time where the Marshall McLuhan maxim, "the medium is the message," has never been more poignant. technology does not just facilitate but it often dictates the overall cultural rhythm, molds behaviors, and perhaps even our ethics. honestly, the question of culture now isn't just about what we do but about what we are becoming. sometimes i wonder if we are evolving into a species that is more connected but less present, more informed but less wise?

[jovin @jovinxthomas]

@Kalshi what do the time horizons for these deals generally look like? surely this isn't month-to-month right?

[jovin @jovinxthomas]

@EmoryExplorer i usually just watch. especially with tech stocks there's just a lot of volitility

[GEOFF @geoffreywoo]

how to read the room in venture right now:

if a partner keeps saying founder quality matters more than ever, ask what they actually mean.

often it just means their old product heuristics stopped working and theyre retreating into vibes, references, and charisma theater.

agents are making this painfully obvious.

the firms that win this cycle wont be the ones with the prettiest taste.
theyll be the ones willing to underwrite businesses that look weird to human pattern matching but survive base model progress.

most vcs were never underwriting the future.
they were underwriting familiarity with better branding.

that game is breaking.

founders should notice who still asks the same legacy questions after the workflow changed.

those are not your investors.
those are museum curators for the last software cycle.

[jovin @jovinxthomas]

@StockSavvyShay On top of that $NVDA owns ~4% to 4.5% of $INTC too

[jovin @jovinxthomas]

OpenAI vs Anthropic profitability in 2026 is the cleanest unit-economics case study in tech.

OpenAI has 900 million weekly active users. Massive distribution moat but only ~50 million are paying (about 5%). The free-tier inference load is a lot financially, which is exactly why they’re rolling out ads on free and Go tiers right now. Frankly, ads on ChatGPT are lucrative for so many other reasons especially given that they allow advertisers to move away from 'inferred Intent" to "stated intent."

On the other hand, Anthropic sits at ~19 million MAU yet just crossed $30B ARR run-rate, ahead of OpenAI’s $25B. Over 80% from enterprise + API. ARPU 7–8x higher. Cleaner margins with almost zero free-rider drag.

Consumer scale wins adoption, but enterprise purity wins profitability, at least in the short term.

[jovin @jovinxthomas]

@OpenAI vs @AnthropicAI profitability in 2026 is turning into one of the cleanest unit-economics case studies in tech right now.

OpenAI has 900 million weekly active users. Massive distribution moat but only ~50 million are paying (about 5%). The free-tier inference load is a lot to deal with financially, which is exactly why they’re rolling out ads on free and Go tiers right now.

Anthropic sits at ~19 million MAU yet just crossed $30B ARR run-rate, ahead of OpenAI’s $25B. Over 80% from enterprise + API. ARPU 7–8x higher. Cleaner margins, almost zero free-rider drag.

Consumer scale clearly wins the adoption race, but enterprise purity, at least in the short term, wins the profitability race.

[jovin @jovinxthomas]

the @cursor_ai deal is more of a clean call option.

@SpaceX paid $10B for the collaboration + a $60B strike to own cursor outright (or walk). colossus 1 was always the prototype cluster, and now fully monetized by leasing it to @AnthropicAI for cash.

@SpaceX still has Colossus 2+ and they'll probably use @cursor_ai as the option on coding distribution.

seems like just smart infrastructure arbitrage.

[jovin @jovinxthomas]

@StockSavvyShay Such a smart decision to work together! Compute has been a huge constraint for Anthropic's growth. They're at ~$44B ARR in May '26 -- would it really be that unrealistic for their ARR to hit ~$70B by July '26?

[jovin @jovinxthomas]

@iScienceLuvr XWS -- X Web Services

[jovin @jovinxthomas]

@claudeai @SpaceX makes a lot of sense -- frankly it seems like Colossus is going to be fully utilized with customers like Anthropic, Cursor, xAI, and Tesla all under the same umbrella.

[jovin @jovinxthomas]

staying strong on the $MU thesis 🚀

[jovin @jovinxthomas]

The Future 500: Day 1 - Micron Technology $MU - Pioneering the Memory of AI ⤵️

Micron Technology isn't just about chips; it's about powering the AI revolution and shaping the future of computing. With their strategic investments, cutting-edge tech, and crucial partnerships, Micron stands at the forefront of the AI and data center evolution, offering a prime investment opportunity for those with an eye on the future!

Why Micron is Crucial for AI and Data Centers:
🔸High-Bandwidth Memory (HBM): Micron's HBM3 and the forthcoming HBM3e are indispensable for AI, allowing data centers to process vast data streams with unprecedented speed.
🔸AI Memory Needs: As AI models grow, so does their need for memory. Micron's solutions ensure these models aren't constrained by memory limitations.
🔸Data Center Expansion: Thanks to the CHIPS and Science Act, Micron is expanding U.S. manufacturing, enhancing supply chains, and supporting tech sovereignty.

Micron's Competitive Edge
🔸Market Position: As the ONLY MAJOR U.S.-BASED MEMORY MANUFACTURER, Micron offers a strategic advantage in a market largely controlled by Asian competitors.
🔸Technological Leadership: Micron's innovations, like the world's first 176-layer 3D NAND and HBM3, set the industry standard.
🔸Partnerships: The collaboration with Nvidia $NVDA on HBM for AI chips underscores Micron's central role in AI development.

Competition
🔸Samsung: The world leader in memory, Samsung's sheer scale and vertical integration make it a formidable competitor, although it faces similar geopolitical challenges in the U.S. market.
🔸SK Hynix: Known for its innovation in HBM memory, SK Hynix has a strong presence in AI applications, making the competition intense.
🔸Kioxia: A significant player in NAND flash, but with a focus more on storage solutions rather than the broad memory portfolio Micron offers.

Stock Performance & Outlook
🔸Recent Performance: Micron's stock has seen significant volatility, with a notable drop followed by analyst optimism. It's currently viewed as a potential "golden buying opportunity" due to its oversold conditions and solid fundamentals.
🔸Future Growth: Micron is poised for growth with AI's expansion. The demand for AI-specific memory solutions is expected to skyrocket, with Micron's HBM potentially accounting for a significant portion of its DRAM revenue.
🔸Analyst Sentiment: Analysts are BULLISH, with price targets suggesting substantial upside potential, driven by Micron's AI and data center memory prospects.

Micron Technology isn't just shaping the future; it's engineering it. Whether you're an investor or simply fascinated by tech, $MU represents a key piece of the puzzle in the AI landscape. #TheFuture500 #Micron #Investment #AI #STARGATE #StargateAI

[jovin @jovinxthomas]

https://t.co/rrMOXH2sLO

[jovin @jovinxthomas]

the Center for AI Standards and Innovation (CAISI) just published voluntary agreements with the frontier labs (DeepMind, Microsoft, and xAI). this is a real shift in the “policy-as-a-service” layer: a move away from abstract governance toward concrete measurements as baselines.

the voluntary nature is the real signal here bc it shows the industry is converging on a standard go-forward path for these models.

they aren’t just looking at the final product. they’re running evals on unreleased models with the safeguards stripped off, essentially load-testing them to find the actual capability ceiling. from a model-risk standpoint, it’s the only way to get a high signal-to-noise ratio. you move from a more or less “trust me” phase to an empirical baseline where risks become measurable deltas instead of “what-if” scenarios.

by setting these redlines pre-deployment, you flip the energy from reactive regulation to proactive measurement. if you can’t accurately measure what a model can do when the guardrails are removed, you don’t actually know the safety margin of the system you’re about to deploy.

this feels like the right pragmatic first step toward grounding the entire safety debate in compute and data.

[jovin @jovinxthomas]

@agarwl_ really cool -- thanks for sharing it

[jovin @jovinxthomas]

@StockSavvyShay i get it but getting people off the $APPL ecosystem will be a herculean task.

[NeilXbt @neil_xbt]

Andrej Karpathy could have charged $10,000 for this course.

He put it on YouTube.

The man who built Tesla Autopilot from scratch.
Co-founded OpenAI.
Understands AI at a level most engineers at Google and Meta never reach.

Sat down. Recorded 2 hours. No frameworks. No libraries. No shortcuts.

Then dropped it for free.

The gap between people who watch it this week and those who save it for later is not 2 hours.

It is everything those 2 hours quietly unlock for the rest of your career.

[jovin @jovinxthomas]

@StockSavvyShay @LeverageShares underrated way to think about fair value in tech/semiconductors.

[jovin @jovinxthomas]

this is so impressive!

[Yinghao Xu @YinghaoXu1]

🎉 After one year of teamwork, we are excited to release our 3D foundation model — LingBot-Map!

Unlike DA3/VGGT, LingBot-Map is a purely autoregressive model for streaming 3D reconstruction ⚡
It achieves ~20 FPS on 518×378 resolution over sequences exceeding 10,000 frames — and beyond 🚀

Two key insights behind LingBot-Map:
🔑 Keep SLAM's structural wisdom: build Geometric Context Attention with long-context modeling while maintaining a compact streaming state
🔑 Make everything end-to-end learnable — no optimization, no post-processing

Let's check out our demos 👇

[jovin @jovinxthomas]

the US railroad buildout is a really great benchmark.

private capital poured in over decades even with peak annual spending hitting something like 20% of GDP in the big 19th century surges. without the rails, US productivity in 1890 would have been ~25% lower, and the social rate of return on that ~$8B (1890 dollars) of capital was around 43% a year.

only a small slice went to the railroad companies themselves -- most of the gains came from opening up new markets, shifting manufacturing, and connecting the country.

there were a bunch of bankruptcies but the long-term rewiring of the economy was massive. the most interesting question is what the true multipliers look like when you measure these things right.