Mo El-Bibany

venturing. founder @pageonevc.

[Mo El-Bibany @mobibany]

A historical lens on #VentureCapital by @_TheFamily. One of my favorite pieces. Ecosystem is more than a buzzword. Technology will be the backbone of the brave new world. #vc
https://t.co/823oBPORBd

[Rob Maurer @robmaurer]

The metric I keep coming back to for SpaceX is $/Mbps to orbit

Starlink exists because Falcon 9 dropped bandwidth deployment costs ~10x to ~$6.55/Mbps. That’s about to drop again to just $0.30/Mbps because of Starship.

A business that is doubling users annually with a 63% adjusted EBITDA margin is about to cut their biggest cost by 95%… It really seems like people don't understand the implications of this.

The math assumes a reusable Falcon 9 launch is 17 tonnes at $1,000/kg and 2,600 Gbps per launch. Starship is targeting 100 tonnes at under $185/kg and 61,000 Gbps per launch. That's $17M for 2,600 Gbps ($6.55/Mbps) verse $18.5M for 61,000 Gbps ($0.30/Mbps).

Starship's additional volume allows for larger satellites, enabling simultaneous gains on multiple cost curves. The math suggests V3 satellites are ~600 Mbps/kg vs ~150 Mbps/kg from V2 mini.

Combining the 4x improvement on satellite bandwidth density with a 5x improvement in launch gets you the 20x improvement to 30 cents per Mbps to orbit.

These are fairly conservative assumptions because launch probably comes in even lower as Starship ramps, and satellite improvements probably keep coming. At $0.10 / Mbps, $1 billion spend on launch represents 10,000 Tbps or about 15x the bandwidth of Starlink's constellation today.

$1B is 90 days of operating income for Starlink... at it's current scale...

Yeah, I really don't think people are getting this. Starlink is the internet now.

[Antonio Linares @alc2022]

biology will deliver most of the returns in the stock market over the next 20 years

[P. E. Sottas @pesottas]

With age reversal leading this

[sarah guo @saranormous]

https://t.co/Hw02laH9yp

[Jarren Feldman @jarrenfeldman]

Everyone asks where’s the productivity from AI yet any biolab you talk to says they’ve increased discovery rates and timelines by 2-5x or more in certain workflows.

[Rohan Paul @rohanpaul_ai]

Great news from new nature published research. "historic" indeed.

A person has received the first gene therapy designed to make damaged eye neurons act young again.

The damaged eye cells being targeted are not ordinary eye cells, but brain-like central nervous system neurons that normally do not regrow once injured.

Means success here would be an early sign that medicine may be able to repair brain-like nerve tissue once thought permanently damaged.

[Bruce Booth @LifeSciVC]

With these huge IPOs for SpaceX, Anthropic, and OpenAI… I'm thinking about a couple possible implications for biotech investing:

1/ Many of the large AUM long-only asset management firms (Fidelity, Wellington, T Rowe, Cap Re, etc) are likely participating in a big way in these IPOs.  Given the size of these offerings, these large funds will likely be investing billions into each of these. Every large investor only has so much capital (from a risk management perspective) allocatable to primary offerings of IPOs in a given period… so will these three suck all the oxygen out of the room for long-only firms' ability to play in biotech IPOs in 2H 2026?  Will the sector be even more dependent on specialist healthcare investors for IPOs for the next few quarters?  Seems likely to me.

2/ Right now these three positions are very large private marks on many big investor’s books.  Most of these firms have limits as to what percentage of their AUM can be invested in private deals… when these three move over to the public side, it immediately changes the “ratio”  in a big way… creating significant “space” for private investing in their portfolios.  Will that bode well for their participation in late stage private deals in biotech?  Maybe... hopefully.

So for the next few quarters... while biotech IPOs may be more reliant on specialists, we might see renewed interest from long-only firms in later stage private biotech deals - helping companies stay private for longer.

[Daphne Zohar @daphnezohar]

More biotech M&A this morning w/ GSK acquiring Nuvalent for $10.6B. Yesterday Incyte/Vega ($1.25B upfront), J&J/Firefly($1B cash). A couple of these get leaked to Financial Times day before. Always wonder about who leaks, motivation and any consequences. Banks sometimes do this to force the hand but a night before doesn’t really benefit anyone other than PR firms

[Techmeme @Techmeme]

Source: Cursor, which is prepping for an expected acquisition by SpaceX, passed $4B in annualized revenue in the last week, up from $3B in April and $2B in Feb. (@richardjnieva / Forbes)

(Visit Techmeme dot com for the link and full context!)

[Anthropic @AnthropicAI]

New Science Blog: Why has AI advanced faster in coding than in biology?

To agents, bio databases are like cities built before cars—maddening to drive in because they're designed for different traffic.

How do we build infrastructure agents can use?
https://t.co/PQaNQ4GRJZ

[Matt Schwartz @matt_is_nice]

This is why I'm so bullish on domain-specific foundation models for biomedicine.

In a space like endoscopy where we have a massive proprietary dataset that just doesn't exist in the LLM training corpus, we can standardize powerful endoscopy representations and pass to LLMs.

[Lifespan @JoinLifespan]

Two FDA-approved drugs just produced one of the largest lifespan extensions in mice
• Trametinib: +7.2% (f), +10.2% (m)
 • Rapa: +17.4% (f), +16.6% (m)
 • Both: +29% (f), +27% (m)
The drugs target different aspects of/insulin/TOR to mimic fasting 🚀 
https://t.co/f1XBDc4Yr4

[John Felix @johnfelix123]

I’ve talked to so many LPs who say they want to see a strong track record before backing an emerging manager.

The problem is that by the time the track record becomes truly exceptional, getting into the fund as a new LP is often impossible.

The best managers are usually hardest to access at exactly the moment they become easiest to underwrite.

[Oyvind Bjerke @BjerkeOy]

Databricks CEO interview insights:

Significant acceleration beyond 65% YoY growth in Q1.

Over 81% of databases launched on Databricks are now from agents.

Cybersecurity market revolution - existing cybersecurity vendors can't keep up with malicious agents. Therefore, Databricks entered this market. Additionally, as AI makes software development much faster, it is much easier to enter this market.

Reaffirmed no 2026 IPO plans - bad year for IPO due to big dislocations: election year, energy uncertainty and mega IPOs.

"Chinese open-source models are absolutely dominating" given their significant cost advantage.

Source: Bloomberg

[Naval Podcast @navalpodcast]

Executive Brief of our latest episode with special guests @rauchg, @bscholl and @maxhodak_.

The AI Industrial Revolution

1. The engineer’s job has changed from shipping output to building the factory that ships it. We used to argue whether 10x engineers exist; now it’s 100x and 1,000x and the world hasn’t caught up yet.

2. Waste tokens to save time. Don’t look at the tokens either as inputs or outputs—just look at your time and the final output.

3. Enterprise software dies when the customer can generate their exact workflow internally. Even spreadsheets are cooked—they were the closest thing to custom software before everybody could build their own.

4. When models speak natural language and source code, pure software gets harder to defend. The moat shifts toward factories, hardware, network effects, regulatory barriers, and other things AI can’t generate on demand.

5. Two engineers can now vibe code a jet engine. Instead of passing spreadsheets around like it’s the ’90s, software engineers build the architecture, hardware engineers vibe code the parts, and the aerodynamics update in real time.

6. China’s open-source AI push is industrial policy. If users can generate software on demand, China’s hardware advantage compounds and Silicon Valley loses one of its biggest edges.

7. Intelligence is an unalloyed good, so you always want the smartest model. The moment one is even a little smarter, you stop trusting the dumber one’s answers.

8. Humans are becoming verifiers. The job isn’t to read every line of a pull request—it’s to sign off on the consequences and be willing to stand behind it when something breaks.

9. When AI can finish 200 pages of compliance paperwork in hours, hardware teams can iterate on an airplane design without months of regulatory rework after every iteration, shortening cycle times.

10. Healthcare is a small communist society running inside a larger capitalist one: there is no price list because patients don’t pay directly—you get care, and the bill goes to an insurer. The fix isn’t single-payer; it’s making care cheap enough to put on a credit card, which China is already doing.

11. Your job is no longer to do the work—it’s to train the agent that does it.

12. In the end it’s not humans vs. AI, but humans with AI vs. humans without AI. What’s left to us is creativity, taste, and judgment—a bicycle for the mind, accelerated.

[Tren Griffin @trengriffin]

Multiple charts would help identify factors.
A moat is a must but also giant expanding markets. Some moats are strong but the markets can be limited in size/growth.

1. Supply-Side Economies of Scale and Scope; 2. Demand-side Economies of Scale (Network Effects); 3. Brand; 4. Regulation; 5. Culture and 6. Intellectual Property https://t.co/wmPz2XEUGB

[News from Science @NewsfromScience]

Researchers show that a type of #AI known as a large language model often outperformed physicians at diagnosing complex and potentially life-threatening conditions, including decreased blood flow to the heart, even in the fast-moving stages of real ER care when information is limited.

In early ER cases, the model identified the correct or a very close diagnosis in about 67% of cases, compared with roughly 50% to 55% for physicians. And the technology is only getting better.

Learn more: https://t.co/ke7CsHcdUq

[Loofy @Loofyb0i]

SpaceX is filing for an IPO at $2T. Anthropic and a dozen AI companies are right behind it.

Thomas Laffont at Coatue explains why this is not 1999. The "10x Paradox": models get 10x better, prices drop 10x, demand explodes 100x.

33-min and you'll understand the power law that determines who wins in AI

bookmark - it's the most important investment framework for the next 3 years

[Tren Griffin @trengriffin]

Coatue's Thomas Laffont on a "Power Law Paradox": a business valued between $100B and $1T (a "Centacorn") has a higher statistical likelihood (31%) of multiplying its value by 10x compared to smaller, earlier-stage unicorns (8%).

[Chamath Palihapitiya @chamath]

I still believe this.