Sogol Malek

Builder | @Ethereum fellow cohort 4| @Harvard blockchain 024🏆 | @ForbesWomen

[Sogol Malek @sogol_malek]

I’m super excited and honored to showcase and launch our EP-x , efficient #stateless #lightclient that consumes #zkp
Super thrilled for possibilities it can provide for #flashbots to be protective against Front-running & 🥪attacks!🧵

https://t.co/EjaBOXNHaY

[Paul @PaulBonnet]

In 1852 aluminium cost twice as much as gold.
Then the price fell over 99.9%. Yet it built one of the largest materials markets on earth.
What it says about AI tokens: https://t.co/5Y04wrz8mj

[Sogol Malek @sogol_malek]

@Hacker0x01 @PayPal @Hacker0x01 My report has been open for several days with new evidence indicating potential fund-drain impact. I was warned not to follow up with the protocol team directly, so I’m keeping everything within HackerOne — could someone please review it?

[Sogol Malek @sogol_malek]

@miamislice @Consensys Thanks for your attention 🙏🙋‍♀️

[Sogol Malek @sogol_malek]

@miamislice @Consensys Thanks @miamislice that’s great. It’s utmost important to get this done as important bug. Happy to share report submitted via hackerone and more in encrypted format with security team

[Amjad Masad @amasad]

https://t.co/9ujCmLs3lQ

[Startup Archive @StartupArchive_]

Naval Ravikant: “The future will be almost all startups”

“I firmly believe that the efficient size of a company is shrinking very rapidly, and so the future will be almost all startups.”

In the clip below from a 2012 interview, Naval speculates that information technology will reverse the centralizing force of economies of scale following the Industrial Revolution.

“I think the contract work trend is going to increase, and I think the size of your average company is going to decrease. I think we’re going to see more and more billion dollar businesses built by four or five people, and it’ll stay at that.”

He doesn’t think we’ll see many more companies like Facebook or Google with tens of thousands of employees:

“I think any entrepreneur worth their salt could today build Facebook with a few hundred people… Facebook and Google are in the situation that large companies end up in where the founders know that 80% of the people are not really needed, they just don’t know which 80%.”

[Startup Archive @StartupArchive_]

Brian Armstrong’s #1 piece of advice for founders: “Think bigger”

Goldman Sachs CEO David Soloman asks Coinbase founder Brian Armstrong for one piece of advice for entrepreneurs in the audience.

Brian replies:

“When I was first trying to start companies, I was looking for ideas that only required some tech innovation. They were smaller ideas, and none of them ended up really mattering. My advice would be to go for more ambitious, long-term projects. Anything worth doing is going to have a tech component, but you’re also probably going to have to bump up against legislation and the government. Don’t shy away from those problems. Solving these hard problems is what creates value.”

He continues:

“It’s probably going to take 10 years to even make a dent in it, but by 20 years you’ll really start having an impact. I think people generally don’t attack ambitious enough problems, but that’s where the value is created and there’s fewer people who play in that space. I just keep thinking: How do we think bigger?”

Source: @GoldmanSachs (Dec 2025)

[Chamath Palihapitiya @chamath]

Enterprises still measure an executive's importance by the size of the team they manage. But this is an old proxy for a world where more people was the only way to do more work.

This isn’t true anymore.

The leaders who will be most impactful over the next five years will be the ones whose people spend all their time on only the work that actually moves the business, because they use systems and factories to do more of the rest.

[Startup Archive @StartupArchive_]

Marc Andreessen: “The best entrepreneurs of the future will be quite skilled at 6-8 things”

Marc is asked how being a founder changes in the age of AI, to which he responds:

“I think there are two ways to have a differentiated edge in general — go deep or go broad.”

Going deep means becoming a specialized expert in your domain.

“There are domains where that really matters,” Marc explains. “In biotech and working on AI foundation models, the deeper you are the better.”

But as AI gets more powerful, Marc would bet that “going broad” will be the winning strategy for most fields. He recommends knowing a lot about many different fields and how the world works — then use AI tools to go deep whenever you need to.

“If you talk to any of the great CEOs, you see this.” Mark explains. “The really great CEOs are great at product, sales, and marketing people, they’re great legal thinkers, and they’re great at finance and with investors and the press. It’s a multidisciplinary kind of approach.”

He continues:

“The best entrepreneurs of the future will probably be quite skilled at 6 or 8 things and then will be able to cross-pollinate and combine them.”

Source: @tbpn (May 2025)

[The All-In Podcast @theallinpod]

J-Cal: Nvidia is taking the gloves off with its open source model. They will own the whole stack.

“You will not be able to tell the difference between Jensen Huang's open source LLM and Claude for 95% of your searches, I guarantee you.

Now, why has Nvidia and Jensen downplayed their open source model until this moment?

Why would he do that? Why would he never bring it up in the All-In interview, never bring it up?

Because his top customers (OpenAI, Anthropic, etc.) were very concerned, from what I understand, about the fact that Nvidia had made so much progress on their open source model.

But suddenly, after OpenAI announced their Jalapeno chips, after Anthropic started making chips, after AMD did successful projects with both of these companies, after Elon said he's going to do his own fab, Nvidia's taking the gloves off.

They are going to own the whole stack. You get the hardware from them, and you're going to get a model that's competitive with OpenAI's for free.”

[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.

[Chamath Palihapitiya @chamath]

On 8090's go to market:

We could have sold into easier, forgiving customers and posted a nice logo wall. Instead, we did the opposite.

We went straight at the hardest, most regulated buyers we could find: healthcare, insurance, life sciences, aerospace, energy, manufacturing, financial services, and the United States government.

The logic was simple. If AI can be trusted to do mission-critical work inside the industries with the least tolerance for error and the most oversight, it can be trusted with serious work anywhere.

Some recent 8090 wins:

- A publicly traded insurer ran a workflow its existing systems could never accommodate and took years of cost out of their system.
- A life sciences customer cut its authoring-to-approval cycle by more than half, so the team spends its time on the science instead of the paperwork.
- A manufacturer brought tens of thousands of parts under automated handling, and the people who used to do manual intake now work the exceptions that actually need a human.

Overwhelmingly all of our customers create negative churn for our business: a small customer quickly becomes medium, a medium customer becomes large. Our large customers are becoming extra-large.

And every engagement makes the platform smarter, because of how have engineered our Software Factory. A network effect in Enterprise Software is quite novel and we may be onto one.

[Peter H. Diamandis, MD @PeterDiamandis]

Want to become a billionaire? Help a billion people. The size of the fortune is just a receipt for the size of the problem you were willing to take on nothing else.

[The All-In Podcast @theallinpod]

Chamath’s Liquidity takeaways and reflections:

The state of private and public markets is changing. In recent decades, companies have stayed private longer while competitive advantage deepens with scale. Now, some of the most valuable private companies in the world are preparing to go public at unprecedented valuations just a few years after being founded.

A historic liquidity wave is arriving as SpaceX, OpenAI, and Anthropic prepare to go public and lockups expire. These three companies alone are projected to exceed the entire prior decade of venture exits combined.

That capital will go somewhere, and right now the focus is on the physical substrate of AI. Power, memory, copper, and specialized silicon are the current key components. The AI model layer itself is competitive, and the frontier labs are searching for profit pools in application layers. Six themes carried across the room.

1/ The liquidity wave & the barbell
2/ AI is a power problem
3/ Models commoditize, compute doesn't
4/ Public vs. private trends are shifting
5/ Can the US build a moat China can't cross?
6/ One scarce edge is human judgment

Full breakdown of each theme, along with Chamath's takeaways from each speaker, here: https://t.co/GXeh9MmiIa

[8090 @8090_Factory]

Every platform raced to be the AI agent control plane this week: Snowflake CoCo, Microsoft Agent 365, NVIDIA's faster models.

Faster agents don't move the real blind spot: Your AI writes one repo at a time.

Your system is 100 repos, and the risk lives in the seams.

Try our new multi-repo indexing: https://t.co/YnLbBjlNE5

[Naval @naval]

Software platforms are going to be rebuilt for agent-first.

[Chamath Palihapitiya @chamath]

I still believe this.

[Bill Ackman @BillAckman]

A must watch from Jamie Dimon.

[Elon Musk @elonmusk]

Tesla IPO market cap was 0.1% of its current value