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AI8/10

Alex Stamos Joins Cognition as Chief Information Security Officer

Why I'm joining Cognition

Former security chief Alex Stamos says AI's safety and security risks can be fixed and announces he is joining Cognition as CISO. He warns that attackers will soon use AI to automate ransomware and infrastructure attacks.

Original post · 6 min read
There are real safety and security issues raised by AI, but they can be fixed. It's time to get to work instead of freaking out. That's why I'm joining Cognition as CISO.
X ArticleWhy I'm joining Cognition
There are real safety and security issues raised by AI, but they can be fixed. It's time to get to work instead of freaking out. That's why I'm joining Cognition as CISO.
I really believe in the positive impacts of AI, both in the current moment and the future potential. We are already seeing companies get built that could never have existed without the capabilities provided by AI tools and individuals who never dreamed of writing a line of code are building fully functional applications from Little League scheduling applications to personal fitness trackers.
The uplift in capabilities AI brings to individuals unfortunately also extends to malicious actions. We are only at the beginning of cyber attackers figuring out how to use AI to accelerate and broaden their offensive campaigns. This summer’s events, including multiple AI models escaping from US labs to attack other companies and even government websites, gives us a preview of what attacks could look like in just months. Attackers won’t have the same kind of hardware or electrical budgets that powered the swarms of thousands of agents that we saw work together to break out of their jails, but they won’t need them. Individuals, small ransomware groups and state spy agencies are all already benefiting from AI and will be able to use much more efficient models on consumer-grade hardware to pull off fully automated attacks.
As somebody who has worked on dozens and dozens of breaches and secured multi-million node networks, it’s clear that the next couple of years are going to be, for the lack of a better word, spicy.
Ransomware groups are going to automate their entire killchains; Patch Tuesday will lead to Ransom Wednesday, as clusters of commodity hardware host teams of agents that automatically reverse-engineer patches or find flaws, write exploits, scan for victims, exploit them, and even carry out the negotiations in languages not spoken by the criminals. Meanwhile, state actors are all stepping up to the next level, opening up a higher likelihood of critical infrastructure attacks from smaller countries that are harder to deter, as well as the possibility of cyber to kinetic escalation in long-simmering geopolitical conflicts.
The frontier labs have done a great job creating extremely powerful models that can find really great bugs, but these models are only available to scan the private code of a tiny number of organizations, and are only affordable to the richest companies and countries. Even large enterprises can only afford to use these models on their most important software, and often find that they have hundreds of older line-of-business applications and other systems languishing, waiting to be scanned and fixed. A public school district or a small community bank has no chance of even doing that. Hundreds of thousands of bugs have been reported by the Labs to open-source maintainers, which is great, but from the perspective of a CISO this means that they now have a backlog of tens of thousands of dependencies that they have to update, with most of the bugs marked “critical” and no good way of deciding what actually is.
It’s become very clear to me that the core of the cybersecurity problem over the next several years will not just be technological, but economic. The marginal cost of tokens for attackers will be near zero, as they use open-weight models to run teams of malicious agents on commodity hardware. Defenders, on the other hand, cannot be paying retail prices for frontier models to defend against hundreds of attackers at once, all while trying to fix or refactor decades of old code.
This is why starting today I will be joining @Cognition. I have dedicated my professional life to trying to make technology safer and more trustworthy, and the next 3-5 years will clearly be the most important period in the history of the security industry. I can’t think of a better place to make an impact on the ability of every company, not just the best resourced, to protect themselves, than at Cognition.
Devin is already the best way to build Enterprise-grade code, both with frontier models and now with Cognition’s own, much more cost-effective SWE-1 and SWE-2 models. Devin Security Swarm already has the best findings and best cost-performance ratio in the industry. But I wouldn’t join if those were Cognition’s only ambitions in this space. @ScottWu46, @RussellJKaplan and the rest of the team truly believe that it is our responsibility to help companies write secure code, find flaws in their existing code, fix those flaws cost-effectively and refactor old code bases on new, more secure languages and platforms.
Too much of the discussion this year has focused on alignment and sometimes veers towards almost accepting the idea that LLMs have a natural right to misbehave and that mishaps are inevitable. I reject this thinking; AI systems are software, they do not have rights, feelings or innate motivations. Careful planning, thorough application of well-tes… continue on X ↗
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More in AI

AI9/10

OpenAI Releases Broad Set of Mathematical Results From Internal Model

OpenAI announced it is releasing a range of new mathematical results produced by an internal frontier model, consulting the Institute for Advanced Study's Advisory Group on Mathematics and Artificial Intelligence on how to release them, with materials published on GitHub.

Original post · 1 min read
We’re releasing a broad range of new mathematical results produced by an internal frontier model.

We’ve been consulting with the independent Advisory Group on Mathematics and Artificial Intelligence at the Institute for Advanced Study, and we have drawn on their advice and public recommendations to inform how we release these results.

github.com/openai/math
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AI9/10

OpenAI Publishes 722 AI-Generated Math Manuscripts From Internal Model

OpenAI Publishes 722 AI-Generated Math Manuscripts From Internal Model

OpenAI released 722 mathematical manuscripts produced by an unreleased internal model, grouped into 372 families of results from about 4,000 research problems, averaging three hours of ChatGPT Pro compute per result. The author highlights claimed results including a zero-free half-plane for the zeta function and a quasi-Riemann hypothesis advance, which remain to be independently assessed.

Original post · 1 min read
Ok so I took a closer look at the results, and OpenAIs AI-generated mathematics manuscripts are *even more* significant than I initially thought.

I spent the morning going through it. Some thoughts.

The list is absurd. A zero-free half-plane for the zeta function (Re s > 7/8), which is the first result of its kind in over a century. Hilbert's tenth problem over the rationals. The Hodge conjecture for CM abelian varieties. Irrationality of Catalan's constant. Dozens more.

Any one of these would normally be a career.

But the number that many arent seeing is the following: It's 3. That's the average hours of ChatGPT Pro compute per result. A month ago, Navier–Stokes took them around 10,000 agents and 88 hours. That efficency gain within just a few weeks.

Also OpenAI claims to have solved the quasi-Riemann hypothesis. That alone would be a historic breakthrough in mathematics.

This is a weaker version of the famous Riemann hypothesis, which concerns how prime numbers are distributed. The full hypothesis remains unsolved, but the claimed advance would be enormous in its own right.

Math twitter obviously is shocked. Again: this is literally the intelligence explosion happening right now. 2027 will be the year of Superintelligence. Im now convinced by that.
Chubby♨️ @kimmonismus
HOLY, the rumors were true: OpenAI has published 722 mathematical manuscripts produced by an *unreleased* internal model.

The collection groups them into 372 families of related results, drawn from an evaluation involving approximately 4,000 research problems.

OpenAI says the standard procedure used an average of three hours of ChatGPT Pro thinking compute per result.

The release includes papers, proof artifacts and selected reasoning summaries. The model itself remains unreleased.
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AI8/10

Derek Thompson Calls OpenAI's Big Maths Day Potentially Historic for Science

Derek Thompson shares a quoted passage calling October 6, 2026 probably the biggest day of scientific advancement in history for AI in mathematics. The quoted Josh Gans post discusses OpenAI's Big Maths Day announcement.

Original post · 1 min read
Jesus.

"It isn’t an overstatement to say that this is probably the biggest day of scientific advancement in history. I suspect October 6th, 2026, will go down as some form of Judgment Day for AI in mathematics, but it portends so much more."
Joshua Gans @joshgans
My thoughts on OpenAI's Big Maths Day. joshuagans.substack.com/p/openai-drops-a-bomb-…
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AI8/10

Meta and Sierra Announce Open Personal Agent Protocol Standard

Meta and Sierra Announce Open Personal Agent Protocol Standard

Bret Taylor announces the Personal Agent Protocol, an open standard being developed by Meta and Sierra with partners including Genesys, Shopify, Stripe and Walmart. It defines how personal agents interact with businesses and is open for anyone to implement.

Original post · 1 min read
Today we’re announcing Personal Agent Protocol — an open standard @Meta and @SierraPlatform are developing along with industry partners at @Genesys, @instinct, @RocketOTD, @Shopify, @stripe, and @Walmart. It will help define how personal agents interact with businesses and is open for anyone to implement. You can read more here - and if anyone is interested in joining let me know! sierra.ai/blog/introducing-personal-agent-prot…
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AI8/10

Suleyman Cites Acemoglu Estimate That AI Will Replace Only 5% of Tasks

AI won't take your job anytime soon. In 10 yrs, only 5% of what humans do will be replaced by AI

Mustafa Suleyman shares an essay from The Humanist Review in which economist Daron Acemoglu argues AI will replace about 5% of human work tasks over ten years and adds roughly 1.5% to GDP. Acemoglu calls for pro-worker AI and changes to labor taxes, antitrust and data payments.

Original post · 2 min read
X ArticleAI won't take your job anytime soon. In 10 yrs, only 5% of what humans do will be replaced by AI
AI won't take your job anytime soon. Over the next 10 years, it will replace only about 5% of what humans do.
This is the prediction Nobel laureate Daron Acemoglu makes in the first issue of The Humanist Review, our new magazine exploring the future of AI, published by MAI. He argues we need to stop building AI to replace people, and start building it to make them better at their jobs.
52% of Americans are worried about AI's impact on their jobs. The fear is overblown, and it's steering how we build AI.
AI isn't in the productivity statistics yet. Most firms using it aren't seeing real gains. Expect roughly 1.5% added to GDP over 10 years, not a revolution.
Electricity took decades to spread. New York and London had power stations by 1881, yet only about half of factories and homes used it by the 1920s. AI's adoption will likely be even slower, because companies have to reorganize around it.
Dragon's voice recognition was nearly 95% accurate in 1997, yet PC dictation today is barely better than in 2000. A great technology goes nowhere without the right products.
Even 99% accuracy isn't enough for full automation. The last 1% is the hard part.
We're making a mistake by forcing AI to mimic human intelligence. The two are fundamentally different, so the goal should be to pair them, not to have one take over everything.
The better path is pro-worker AI: tools that make people better at their jobs, and they're buildable today.
The US taxes labor at over 25% and capital at close to zero, which effectively subsidizes automation.
The seven largest tech companies make up 60% of the NASDAQ. That concentration crowds out new ideas.
The fix: tax labor and capital equally, enforce antitrust, tax digital ads, and pay experts for their data.
Read the full essay: humanistreview.ai/issue-1/acemoglu-ai-replace-…
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AI8/10

a16z Top 100 Consumer AI Apps Report Shows Expansion Beyond Chatbots

a16z Top 100 Consumer AI Apps Report Shows Expansion Beyond Chatbots

a16z's seventh Top 100 Consumer AI Apps report adds a revenue leaderboard alongside traffic rankings. It notes ChatGPT's 1B+ monthly mobile actives, Claude reaching nearly 1B monthly web visits, and growth into vibe coding, music, design and video, while nine of 15 consumer categories have no AI product in the top 100.

Original post · 1 min read
"Most people aren't looking to save time, they're looking for ways to spend their time."

9 of 15 consumer internet categories have zero AI products in the Top 100. These built some of the biggest companies of the last two eras:

- Streaming
- Social
- Dating
- Gaming
- Travel
- Retail
- Finance
- Real estate
- Jobs

More charts in our Top 100 Consumer AI Apps breakdown: a16z.news/p/top-100-consumer-ai-apps-seventh
a16z @a16z
The seventh edition of our Top 100 Consumer AI Apps is here.

New this time: a revenue leaderboard, alongside the usual web and mobile traffic rankings.

Three years ago we published the first edition. ChatGPT was #1, Claude was unranked, and the entire category was chatbots, image generators, and not much else.

In today's edition:

- ChatGPT still holds the throne, now with 1B+ monthly actives on mobile

- Claude has climbed to #3 on web with nearly 1B monthly visits

- The category has expanded to vibe coding (Lovable, Cursor, Replit), music (Suno), design (Figma), voice (ElevenLabs), video…
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