OpenAEO

Most companies know their Google ranking.
Almost none know their AI ranking.
We’re making AI visibility measurable.

[OpenAEO @openaeo]

Your company may rank highly on Google…

and still be nearly invisible to AI systems.

As buyers shift from searching links to asking AI directly, a new category is forming: AEO (Answer Engine Optimization).

It measures how your company is interpreted, cited, and recommended by AI answer engines like Claude, Gemini, and ChatGPT.

Companies are now falling into two buckets:

1. Those already deeply embedded into AI responses.
2. Those that barely exist.

Which one is your company?

Run a free scan at https://t.co/qbTz6eNSnZ

[Peter H. Diamandis, MD @PeterDiamandis]

In celebration of @SpaceX's IPO, I asked Elon at the Abundance Summit if becoming a trillionaire made money less valuable...

His answer: YES!

[Dr. Alex Wissner-Gross @alexwg]

https://t.co/CMB1vwwYRE

[Pierre-Eliott Lallemant @pierreeliottlal]

We went from 0 to 2,200 paying customers in under a year by following @ycombinator's 15 rules:

1/ Do things that don't scale. Get your first 10 customers by hand.

2/ Launch now, not when it's "ready". A mediocre product in front of real users teaches you more in a week than 6 months of polishing in the dark.

3/ Charge from day one. If nobody will pay, you don't have a startup, you have a hobby.

4/ Talk to users every single day. The roadmap you need is sitting in your customers' heads, and they'll hand it to you for free

5/ Always hunt the 90/10 solution. For almost any feature there's a way to capture 90% of the value with 10% of the effort.

6/ There are only two real jobs: write code and talk to users. Everything else (conferences, press, VC coffees, corp dev calls) is fake work.

7/ You pick your customers as much as they pick you. 10 users who love you beat 1,000 who kind of like you.

8/ Growth is an output, not a strategy. Grow before product market fit and all you're buying is churn.

9/ Do less, really well. Pick one or two metrics and judge every task against them.

10/ Know if you're default alive. Paul Graham's question: on current growth and current burn, do you reach profitability before the money runs out?

11/ Don't hire until it hurts. Headcount is not progress, it's burn. Every great startup was embarrassingly small for embarrassingly long.

12/ Momentum is the only real moat in year one. Ship something every week, even something tiny.

13/ Every great startup is badly broken at some point. The game isn't avoiding fires, it's how fast you put them out. Again. And again

14/ Ignore your competitors. Startups die of suicide, not murder. In year one, the only company that can kill yours is your own

15/ Startups rarely die from running out of money. They die because the founders fall out. Brutal honesty with your cofounder is the cheapest insurance you'll ever buy

Good luck !

[Elon Musk @elonmusk]

🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀🚀

[Hirey AI @HireyAI]

If you're in SF and your agent runs half your day
you let it write code, read email, book flights. The one thing you don't? Find your next customer. 👇

[Dave Blundin @DaveBlundin]

A static world is the worst thing for entrepreneurial people. Market leaders are locked in, and there are high barriers to entry. But when things are changing like crazy? Opportunity is at all-time highs, and we're living through exactly that. The singularity is here, it's lots of fun, and most season executives have NO IDEA how to play their cards.

[Peter H. Diamandis, MD @PeterDiamandis]

Anthropic's CEO admitted AI job displacement may be "intrinsic." Here's my question: Would you trade the old economy — 40-year careers, mandatory retirement, dying broke — for one where AI cures aging, colonizes Mars, and generates post-scarcity abundance while you do what you actually love? That's not a threat. That's the deal.

[Amjad Masad @amasad]

This is what building a company looks like on Replit.

One canvas with your web app, mobile app, marketing & App Store material.

Click into any one of those and start building, changing, and generating new things.

[Elon Musk @elonmusk]

Good interview

[Peter H. Diamandis, MD @PeterDiamandis]

Anthropic just retook the AI crown from GPT 5.5, and split its model in two: Fable for the public, Mythos for trusted organizations. Apple admitted it lost the foundation model race and handed Siri's brain to Google.

-- Brian Armstrong's NewLimit: $435M raised, $3.1B valuation, reversing cellular aging. Human clinical trials targeted for next year.

-- Global longevity investment hit $8.49B in 2024 more than double the prior years.

-- Apple paying Google ~$1B/year for a custom 1.2 trillion parameter model to rebuild Siri.

-- Fable 5: 80.3% on SWE-Bench Pro. On the harder FrontierCode Diamond set: 29.3% vs Opus 4.8's 13.4%

[Amjad Masad @amasad]

🇺🇸🇺🇸🇺🇸

[Anthropic @AnthropicAI]

AI is advancing at a pace our policymaking institutions were never built for—and the gap between the two is becoming the central challenge of the technology. In his latest essay, our CEO Dario Amodei lays out how to close it.

We're launching three new initiatives to support the efforts he outlines.

[Brianne Kimmel @briannekimmel]

fwiw, Pedro was jailbreaking iPhones at 13, received cease-and-desist letters from Apple, sold his first company at 20, and created 2 unicorns by 30.

A lot of people have strong opinions on AI, but this episode goes many levels deeper on how we should be building companies.

[Ihtesham Ali @ihtesham2005]

A mathematician coined the term "artificial intelligence" in 1955, built the language that dominated AI research for 30 years, and predicted cloud computing 40 years before AWS existed and almost nobody outside the field knows his name.

His name was John McCarthy.

He was born in Boston in 1927, earned his PhD in mathematics from Princeton in 1951, and spent the next 55 years working on a single question that most of his peers considered either impossible or insane.

Can a machine think?

In the summer of 1955, McCarthy sat down and wrote a two-page proposal for a workshop at Dartmouth College. The proposal opened with one sentence that changed everything: "every aspect of learning or any other feature of intelligence can in principle be so precisely described that a machine can be made to simulate it."

He needed a name for the field he was proposing. He chose "artificial intelligence." Before that document, no such field existed. After it, every researcher working on thinking machines had a name for what they were doing, a home discipline to publish in, and a founding document to point to. McCarthy did not just contribute to AI. He created the container it lives in.

The Dartmouth Conference ran for eight weeks in the summer of 1956. It was the moment AI became a real scientific discipline.

McCarthy kept building.

In 1958 he invented LISP, the second oldest high-level programming language still in use today, older only than FORTRAN by one year. LISP was designed for a specific purpose: symbolic reasoning. It could manipulate ideas, not just numbers.

It became the language every serious AI researcher wrote in for the next three decades. From 1958 through the late 1980s, if you were working on AI, you were almost certainly working in LISP.

Inside LISP he invented garbage collection in 1959, the technique that automatically frees up memory a program no longer needs. Java uses it. Python uses it. JavaScript uses it. Every modern language that manages memory automatically is using the idea McCarthy worked out while building LISP.

In 1961 he stood at a centennial celebration at MIT and said something that everyone in the room thought was science fiction. He proposed that computing would one day be delivered as a public utility, the same way electricity or water is delivered to a home. You would not own the computer. You would pay for access to it over a network.

AWS launched in 2006. Azure launched in 2010. Google Cloud launched in 2011. What McCarthy described in 1961 is now a trillion-dollar industry. He was 45 years early.

In 1962 he founded the Stanford Artificial Intelligence Laboratory, SAIL, which became one of the most important research centers in the history of the field. The researchers who trained there shaped the next 40 years of AI.

He won the Turing Award in 1971. The National Medal of Science in 1990. The Benjamin Franklin Medal in 2003.

He retired from Stanford in 2000. He died on October 24, 2011, at his home in Stanford, California. He was 84.

The researchers at OpenAI, Google DeepMind, and Anthropic building the models you use today are working in a field McCarthy named in 1955, using memory management he invented in 1959, inside an industry structure he predicted in 1961, toward a goal he spent his entire career insisting was not only possible but inevitable.

He was right about all of it.

He just did not live to see the part where the rest of the world finally believed him.

[Marc Andreessen 🇺🇸 @pmarca]

Confidencemaxxing.

[Peter H. Diamandis, MD @PeterDiamandis]

ChatGPT just hit 1 billion monthly users faster than any product in human history. That record will probably be broken very soon again!

[Dave Blundin @DaveBlundin]

https://t.co/aJlLDRbwwT

[Dario Amodei @DarioAmodei]

Today I'm publishing a new essay, Policy on the AI Exponential. AI is progressing extremely fast—much faster than the policy process was built to handle. The essay lays out where I think the technology is now, and the action needed to close the gap: https://t.co/Lh6PWae178

[Peter H. Diamandis, MD @PeterDiamandis]

We expected the Turing test to be passed with a BANG... Recursive self-improvement will arrive the same way: no alarms or headlines... Just an average week where models began training their successors, and the majority didn't realize it.