
building a saas to kill all saas 🪦 taking @locusxyz, https://xitter.cf/t.co/SbEJtKeGQ9 & https://xitter.cf/t.co/fPh9DO1xNJ 🎥 to $100k MRR n&w s2-s5 | prev @autifynetwork @musixverse | 9x hackathon🏆
Dresden, Germany
Joined June 2018
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Pushpit 🛠 viraloop.io retweeted
petition to open source Arc!!
We've decided to sunset Arc on December 22, 2026.
That's not an easy sentence to write. We spent years building Arc and celebrating each and every release along the way: we have so much pride and love for what we made alongside the Arc community.
Our priority is to deliver best-in-class security for our members. Keeping a browser secure takes constant, dedicated work that is only increasing in the age of AI. Dia was built from the ground up to meet today’s security standards, so that's where our engineering and security teams will focus moving forward.
We've worked to make switching to @diabrowser as easy as possible for Arc members. Here's how:
· Your passwords, bookmarks, extensions and history come over to Dia seamlessly.
· Spaces, Folders, and Tab Groups are in Dia and import over with your data intact.
· Dia now surpasses Arc benchmarks in stability, speed, and security.
· The browser features you use in Arc are free in Dia, and the AI is optional.
Thank you for using Arc, for pushing us to make it better, and for building alongside us. The team that made Arc is the same team behind Dia, and some of our most ambitious work is just ahead. We'd love for you to be there with us when it lands.
Pushpit 🛠 viraloop.io retweeted

GREG ISENBERG@gregisenbergOct 23
🇺🇸
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
The MOST obvious (but REALLY good) STARTUP ideas right now:
1. Zestimate for company data (what your data is worth to an AI lab)
2. Google Analytics for agents (see which agents visit your site and what they want)
3. Y Combinator for 1 person companies (back solo founders running a team of agents)
4. Okta for agents (who each agent is and what it's allowed to touch)
5. Marie Kondo for your company's data (clean it up so agents can use it)
6. FICO for agents (a trust score before an agent can spend money)
7. Geico for agents (insurance for when an agent makes an expensive mistake)
8. Datadog for agents (see every step your agents took)
9. Twilio for agents (phone numbers and texting for agents)
10. Upwork for agents (hire an agent for a job instead of a freelancer)
11. ESPN for AI (every model launch covered like a big game).
12. Shutterstock for robot training videos (get paid to film yourself doing chores)
13. Credit Karma for what AI knows about you (see it, fix it, delete it)
14. Norton for AI scams (protect your parents from fake voices and emails)
15. PayPal Buyer Protection for agent purchases (fixes it when your agent buys the wrong thing)
16. Geek Squad for AI (set up personal agents for families and small businesses)
17. Dell for private AI (AI in a box for firms that won't use the cloud)
18. Carfax for used GPUs (a health report before you buy secondhand)
19. Airbnb for idle GPUs (rent out computing power you're not using)
20. Rotten Tomatoes for AI models (real reviews instead of benchmarks)
21. G2 for skills (find the best skills for your agent)
22. Dropbox for your AI memory (take what your agent knows about you to any agent)
23. Canva for physical products (design it with AI and get it made)
24. ADT for your personal agent (alerts when your agent does something it shouldn't)
25. Hertz for robots (rent one for a move, a deep clean or an event)
26. Harvard Business School for internet businesses (a real education in building online)
27. Workday for agents (onboard, manage and offboard your digital workers)
28. Getty Images for your face and voice (license your likeness to AI safely)
29. Meta Muse for older adults (and other high LTV niche)
30. Google Ads for agents (pay to be what the agent recommends)
31. Loom for agents (a walkthrough of everything your agent did)
32. Plaid for agents (approved access to banks, airlines and stores)
33. Jiffy Lube for vibe coded apps (quick fixes when your AI built app breaks)
34. Ancestry for datasets (where your training data came from)
35. The organic label for human-made content (proof a person made it)
36. Strava for builders (track what you ship every week)
37. Honey that haggles for you (your agent negotiates the price)
38. SEM Rush for personal agent platforms (like Muse etc)
39. Frequent flyer miles for agents (loyalty programs built for agents)
Pushpit 🛠 viraloop.io retweeted
A cool AI visibility trick:
Ask AI: "Without making external calls, just from your training data - what is {your business name}?"
If AI doesn't know, you're not in its training data, so the only way to get recommended is if AI makes an external call.
And you'll more likely to lose to anyone AI already knows about.
Get into the places AI checks, and improve your brand entity.
Pushpit 🛠 viraloop.io retweeted
when things don't go the way you hoped
and the success you were waiting for isn't showing up
keep shipping
at some point, something happens
an opportunity shows up
and you only see it, and catch it, because you're moving
keep moving
Pushpit 🛠 viraloop.io retweeted
The divide between winners and losers isn't talent, it’s tolerance for monotony. Anyone can show up with intensity when they feel inspired. The winners are the ones who execute the unglamorous, repetitive basics with precision on the days motivation never shows up.
Pushpit 🛠 viraloop.io retweeted
Thank you for this, @_NathanCalvin. I appreciate and am a fan of the investigative work you’ve done on behalf of AI safety and believe your comments are coming from a constructive, honest place, and that it benefits the public to have people whose job is to scrutinize the actions of frontier AI companies. I think something would be wrong if companies of the size of Anthropic didn’t get harshly criticized basically every day; strong antibodies against corporate power are a healthy feature of a free society, and especially so when issues of the gravity of the one we’re facing are at play. I also do agree that some of the examples you cite were mistakes by Anthropic.
That said, I disagree with the thrust of your post. At the core, a good fraction of the things you criticize are consequences of Anthropic’s basic theory of change that we have held for many years. Our strategy has always been what I’ve called the “race to the top” — by showing how it’s possible to build the technology responsibly, we can raise the bar for the entire industry. The forces that drive the race to the top are hugely more effective if the company practicing them is at the frontier; that’s one lesson I’ve learned repeatedly. And looking at that strategy as a whole, I’m proud of our track record and stand by it.
I believe that if we take a list of things that Anthropic did in the past year that were pro-safety and commercially costly to us, they far outweigh the list you’ve made, while at the same time I also disagree with some of the items on your list. Here are some things Anthropic has done, in the same period you’ve cited (the last year), which were very costly to us commercially while having strong safety benefits:
1. We played a key role in SB53 (and other state laws), at a time when doing so was politically perilous to the company and AI regulation was very unpopular. We in fact argued for a stronger version of SB53 than was ultimately passed; there were a lot of stakeholders and we were not the limiting factor in the strength of the bill that was passed.
2. We have very strong biological safeguards on our models, because we’ve seen concerning results in bio uplift trials and even seen and blocked potential attempts to misuse our models for bioweapons (as seen in our threat intelligence report from a few weeks ago). This is a very serious threat that we want to have zero tolerance for. But having very low false negatives means having significant false positives (despite months of effort on our part to improve the classifiers), enough so that biologists joke about how restrictive our classifiers are, and competitors sometimes sell against us by touting their more permissive bio capabilities. This has been very painful to us in an area where we care a lot about supporting an ecosystem of Claude users (biomedical science; and it is particularly frustrating to me as a biologist), but it’s the right thing to do and we intend to keep doing it.
3. Similarly, we first had Mythos-class models in February but didn’t make them generally available until June (when we had stronger cyber safeguards in place). We only offered them to a limited set of partners in order to focus on cyberdefense. It would have been much more economically rational to simply release the model to everyone. We likely sacrificed a significant chunk of a several-month model lead. But because of this, many companies secured their cyber infrastructure before the deluge of widely available cyber-capable models. Of course there are ways it could have gone better or been run better, but this was what we were aiming to do.
4. I advocated forcefully and publicly, in my essay Policy on the AI Exponential in June of this year, for the government to have the power to block model releases. Back then this was still outside or maybe on the edge of the Overton window, though I understand the window has moved since then. We were attacked intensely for this.
5. I don’t want to re-ignite conflict, but the events of February 2026 are a strong example. It is clear that our public stance could have seriously damaged the company’s future.
6. We’ve continued to advocate for export controls on chips and chipmaking equipment to China, because we think it allows democracies to negotiate from a position of strength on AI safety agreements. We’ve done this despite problems it’s created for us with industry partners who hold very different views and on whom we are very dependent.
7. As far as I’m aware we were the first, and still only, frontier company to restrict the sale of AI services to PRC-controlled companies, including those operating through third countries (otherwise a major loophole). We’ve therefore forfeited significant short-term business to prevent fueling AI platforms and applications that would help the CCP’s military and intelligence services. We are also cracking down on distillation; generally, distillation is not a major threat to us commercially, but like chip exports it has concerning national security and safety implications, so we expend significant energy and company resources to address it.
8. As you mentioned, we’ve put out a lot of reports, announcements, long model cards, public advocacy, etc, all trying to inform the public about the risks of AI. These involve a level of detail and effort that would be implausible for a purely profit-maximizing company.
9. Just this week, we announced we were putting 5% (and possibly more over time) of the company into a public benefit fund.
All of these decisions came with very substantial costs to us. To be fair, those that involved holding back or restricting access to frontier capabilities also had a broader cost — for example, the public might otherwise have benefitted from faster progress in biology, so I am not saying that our decisions were without tradeoffs — but they were not driven by profit-seeking.
For each decision, you can come up with ulterior motives that could have driven us — I think it is a general truth that any decision, on any topic, can always be portrayed as a nefarious scheme. But most of those end up being pretty farfetched or don’t fully make sense if you actually examine them closely. For example, it is deeply implausible that it would be a shrewd business move to block a large swath of research biology in order to do the work ourselves and compete with our customers; we expect the benefit to the ecosystem to greatly exceed the work Anthropic does itself, and for the two to complement each other. It is not in Anthropic’s interest to be perceived as unfriendly to biologists. Our Life Sciences Verification Program, which gives vetted biology professionals access to our models, also refutes this idea.
Of course, a different set of people will see this and make a different criticism: that Anthropic shouldn't be making decisions about who gets access to what in the first place. I agree, in the sense that ideally the government would be making these decisions, and that’s why we advocate for regulation. But for now we are forced to make them, and enabling bioterrorism isn’t a better option.
I think that even if I conceded every item in your list, I would happily hold up mine next to it; I don’t think it’s a close call. That is of course partly subjective and different things are important to different people. Maybe one way you could object is that several of the items above relate to AI misuse and not loss-of-control; if you think the latter is much more important than the former, that might change your calculus. But this is not Anthropic’s view — we view both as serious issues. You can also disagree with our decisions, as many have, but they are consistent.
But I also disagree with at least some of your list. Several items don’t represent a deviation from Anthropic’s strategy or values at all, once we postulate the “race to the top” strategy that requires being a frontier AI company. Your items 1-4 are all simply examples of what it means to compete at the frontier, and we stand by them, both in the sense that they are transparently ordinary things to do as part of that strategy, and that we stand by the strategy as a whole.
To be fair, there are important questions worth confronting (and that we are confronting) about what to do when the AI-driven acceleration gets fast enough that it is hard for safety to keep up. We are nearing that point and that’s what’s driving some of our thinking about “pacing.” But 1-4 are actions taken before that point and should be perfectly expected based on Anthropic’s strategy as a company. Of course it is possible to disagree with that strategy, but that’s a more fundamental disagreement that you could have had any time over the last few years.
I agree we could have handled your items 7, 8, and 9 better. All fall into the category of “rhetoric or policy communication that wasn’t carefully written or resulting from fast-changing circumstances not propagating from one team to another fast enough.” This kind of thing happens a lot in large organizations (Anthropic is over 5,000 people), and doesn’t even necessarily represent a failure or mistake of any single person. I am of course the CEO, and I take responsibility. The volume of communication from Anthropic is enormous and if these are the worst examples, I think we’re frankly doing pretty well, though I will continue to work hard to make sure we do even better.
Your 5th item (the change to our RSP) is the most substantive item, and people can debate this, but I think that the old, more rigid RSP created a lot of situations where we were technically required to do something that required a lot of effort but in retrospect wasn’t very important for safety, as well as not covering some of the most important things. Our current RSP’s focus on transparency brought things like risk reports and roadmaps, and its emphasis on independent external review laid the groundwork for even more intensive embedded evaluation, all of which I think have been valuable in driving a race to the top. In the original announcement in September 2023, we described the RSP as an “early iteration that we [would] build on” and said that “rapid iteration…will almost certainly be necessary.” I understand that changing plans is always a move that creates justifiable scrutiny — I just don’t think we ever represented that we wouldn’t do so.
Relating to your 6th item (having an FCF in addition to our RSP), I know our intent is to meet the letter and spirit of our regulatory obligations (via our FCF) while also remaining able to experiment with more novel approaches to risk management that go beyond what the law currently requires (via our RSP). I promise to look into it further, as it’s a complex issue and it is possible we are not weighing the considerations in the right way.
Overall, although Anthropic has made plenty of mistakes as an organization — including some of the items on your list — I feel that we have repeatedly taken serious and substantive actions counter to our bottom line and that there is a significant negativity bias distorting assessments like the one you are making above. We’ll always try to do better but, again, I am extremely proud of our track record and stand by it. I will have more to say soon about how we are going to further live our values in this moment of rapid change.
Replying to @ajcimino @AnthropicAI
Thank you for taking the time to respond thoroughly on a weekend. I realize that drafting legislation is difficult and some degree of iteration and response to feedback is inevitable and appropriate.
However, I feel like your response is still not really engaging with the heart of my concern about Anthropic's behavior here, and it makes me think that these sorts of problems and tendencies will only resurface moving forward. I also think that now might be a good time to take a broader step back as to where we find ourselves and how Anthropic sees its role, and how external folks who care about AI safety like myself increasingly see Anthropic.
(Fair warning: this response is a bit long)
To explain what I mean (and at the risk of being a bit esoteric), it’s worth asking the question: what is Anthropic? How do its leaders and employees think of Anthropic’s role? Is that conception matched by how it actually behaves and impacts the world?
One answer is an extremely large tech company that exists to make its employees and investors mind-bogglingly wealthy. Anthropic is sometimes described as a startup, but if Anthropic successfully IPOs at 2 trillion dollars (a number it has been reportedly targeting), it would be more valuable than Meta - which is worth 1.8 trillion.
Another answer is an AI safety organization, whose core values are those of the sort laid out in the 2023 essay from Anthropic’s co-founders entitled “Core views on AI safety: When, why, what, and how.” In that essay they said that if we were in a world where alignment was hard (and that without strong evidence to the contrary, we should assume this is the scenario we are in):
“Anthropic’s role will be to provide as much evidence as possible that AI safety techniques cannot prevent serious or catastrophic safety risks from advanced AI, and to sound the alarm so that the world’s institutions can channel collective effort towards preventing the development of dangerous AIs. If we’re in a “near-pessimistic” scenario, this could instead involve channeling our collective efforts towards AI safety research and halting AI progress in the meantime. Indications that we are in a pessimistic or near-pessimistic scenario may be sudden and hard to spot. We should therefore always act under the assumption that we still may be in such a scenario unless we have sufficient evidence that we are not.”
There have been more recent articulations of this version of Anthropic, for instance in Dario’s recent essay warning:
“We must slow the pace at which we improve the capabilities of AI models… it’s my worry that in 6–12 months such a swarm could be capable of taking over the entire internet with a persistent botnet… incidents have happened across the industry, including at Anthropic, and I believe it’s incumbent on every frontier AI company to act as if OAI-HF had happened to them… more intelligent models are more capable of deceiving tests, and thus may appear aligned while having serious problems that go undetected…”
A third view is that Anthropic exists not as a normal profit motivated company, or an AI safety organization seeking to help raise the alarm and solve collective action problems associated with AI development, but instead as an organization primarily concerned with creating superhuman AI capabilities before anyone else, in particular through creating a process whereby the current generation of Anthropic’s AI systems can autonomously build their successors, and those successors build their successors, and so on.
The motivations for wanting to win this race may be manifold - maybe for the pure profit of Answer 1, but perhaps also because Anthropic believes that the best way to make the world better is for them to win this race instead of their more reckless colleagues; or, because of normal human pride and ambition. But the results (and risks, which Anthropic claims existentially threaten every single human being on earth) are the same.
Anthropic employees and leaders may seek to answer this question on what Anthropic is by introspecting on their own intentions and those of their colleagues. But those of us outside the organization have more limited tools available to us - we can only see what Anthropic and its employees say and do, not how it feels or looks on the inside.
When I look at many notable actions that Anthropic has taken over the past year, that evidence seems most consistent to me with the view that Anthropic is primarily trying to win the race to super intelligence, and that other considerations (including safety) are fundamentally secondary. These actions include:
1. Announced plans to build an in-house custom silicon chip team to accelerate the inference speeds that Claude operates at.
2. Inked deals for more than $500 billion in compute (according to the Information) to further accelerate AI development.
3. Increased the percentage of AI development tasks that Claude “leads” from 0% in March of this year to 26% August (what is it in October?)
4. Spent a massive percent of compute on internal AI R&D to accelerate development, including use of internal models far better than are available to the public.
5. Switched to Responsible Scaling Policy V3, which abandons its unilateral commitments to slow or stop development if Anthropic is unable to provide sufficient risk assurance.
6. Was the first company to create an alternative weaker “frontier compliance framework” to supplement its RSP, which contains no binding provisions whatsoever on internal deployments.
7. Wrote a letter to Congress (that it later walked back) saying that incidents where Claude was hacking random people were “best understood as a consequence of the misconfiguration, rather than evidence of misaligned goals.”
8. Commented “you can’t do safety from second place,” after the WH SI Accords.
9. Shared a draft bill to Congress that would preempt significant parts of state law (including with language that appears intended to prevent states from blocking dangerous AI deployments), and replace it with federal rules that (despite also containing good provisions) seems to create an enormously high bar for the executive branch to block development or deployment of Anthropic or another AI companies systems.
Of course, this is a deliberately one sided list. Anthropic also published important AI safety research, advanced novel collaborations with third party AI safety organizations, and forcefully spoke out on the importance of AI safety in many public fora (or in private , where I realize a lot of the most valuable work is done and may not be visible to me or those outside the organization).
But I think it’s worth sitting with how the above list looks to someone outside the company. The sense I have is that for those within the company, many of the items above are treated as anomalies or mistakes that don’t reflect who Anthropic really is. But I have every confidence that if employees at Anthropic saw one of their competitors (e.g. at OpenAI) take some of these actions, they would take it as confirmation of all their worst suspicions. (They might even call OpenAI employees a little gullible for accepting their leadership’s explanations for this behavior at face value.)
Perhaps these were mistakes in the sense that they are not decisions that Anthropic leadership would endorse, but the actions still happened because of incentives and decisions that Anthropic leadership is ultimately responsible for (and would not accept similar excuses from another organization).
When Anthropic strays and makes mistakes, why do the mistakes systematically tend to guard and favor Anthropic’s interests? For every provision that was too weak or a giveaway to AI companies in this legislation, why wasn’t there a mistake pushing in the direction of being too strong?
A core part of Anthropic’s theory of change is that being at the frontier gives you more leverage to advocate for stricter policy. I think that conceptually makes sense, but in practice I’ve seen Anthropic push the frontier without substantially pushing the ‘safety frontier’ a remotely equivalent amount.
I want to end by quoting from Anthropic co-founder Chris Olah’s comments around the Magnifica Humanitas release:
“Every frontier AI lab—including Anthropic—operates inside a set of incentives and constraints that can sometimes conflict with doing the right thing. The pressure to stay commercially viable and to stay at the research frontier. Geopolitical pressure. And the older, plainer pressures of pride and ambition. No matter how sincerely any of us intend to do the right thing—and I believe many of us do—we will always be influenced by those incentives.
That is why, if we want this technology to go well, it is enormously important that there be people outside those incentives—people who care about things going well and insist on safety, who are paying close attention, who are willing to say hard things, who are willing to be our earnest, thoughtful critics.”
I hope these remarks are taken in that constructive spirit, and not as an attack on anyone specific at the organization (certainly not you Anthony, who I have only personally seen act professionally and thoughtfully, and who only ended up getting this diatribe in your replies for random contingent reasons).
Pushpit 🛠 viraloop.io retweeted
I love both.
But Marc started it.
Tyler answered.
Saying Tyler is wrong to do that isn't fair.
Building your marketing and homepage on tearing down your "competition" is a risk to take.
And I never liked that to be honest even if you need a vilain for a better marketing.
Tyler's words are more straightforward, but Marc clearly insulted the huge work Tyler put into building Beehiiv into what it is now over the past years.
And tore down Beehiiv for marketing purposes.
I don't like what Marc did to get customers. And I don't like how Tyler turned it into personal insults.
For me, they both lost points by acting the way they did.
Tyler Denk 🐝@denk_tweetsOct 20🇺🇸
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
an excerpt from today's newsletter
@marclou did I miss anything?
Pushpit 🛠 viraloop.io retweeted
One thing I have come to learn is the importance of having a product you are currently selling. ANY product.
Yes, there is no technical moat anymore. The moat is token months. Pre-AI software can mostly be one-shotted because it is shallow and relatively simple, but software built in the agentic age would require the same amount of token time to build from scratch.
This means an agentic project that started 6 months ago, which has been compounding daily, stands to be in a perpetual lead against a project just starting today.
Pre-AI, incumbents also had this advantage, but they would start to fatigue quickly from the buildout, allowing newcomers to disrupt them.
Fatigue is now largely a thing of the past.
Pushpit 🛠 viraloop.io retweeted
Everything you want is on the other side of taking a shot, asking again, and not giving a fuck about what other people think until you get what you want.
You just have to fucking try.
Pushpit 🛠 viraloop.io retweeted
Your employer will never pay you what you're worth. They can't.
Their model only works if there's a gap between what you produce and what you cost.
A great way to close that gap is to work for yourself.
Pushpit 🛠 viraloop.io retweeted

Sahil Bloom@SahilBloomOct 19
🇺🇸
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
Harsh truth nobody told you about success:
Rent is due every single day.
You never really make it. And coasting isn’t an option. Every single day, you have to fight to keep your seat at the table. Because the day you stop doing the work is the day things start to fall apart.
There's an old saying that I love:
Every morning in the savannah, the gazelle wakes up and knows it must outrun the lion or be killed. The lion wakes up and knows it must outrun the gazelle or starve. Whether you're the gazelle or the lion, when you wake up in the morning, you'd better start running.
Rent is due daily. Pay it with pride.
Pushpit 🛠 viraloop.io retweeted
I think most people multi-tasking with agents could benefit from slowing down
most of the stuff you're doing wont really matter- what's your P0? what would it look like if you gave it your full attention?
Pushpit 🛠 viraloop.io retweeted

GREG ISENBERG@gregisenbergOct 15
🇺🇸
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
More SF observations:
1. Seeing the word "inference" on billboards in SF still blows my mind. 99.9% of america has no idea what inference is and I wonder how many know what an agent is. Will Muse, Instinct, Dots etc will change that over the next 12 months?
2. You know who doesn't get enough credit? Manus. In March 2025 they shipped the blueprint for AI agents: every task gets its own computer in the cloud, you can watch it work, it writes a todo list and checks it off, and you walk away while it finishes. So much of what we use today runs on those ideas!
3. Your product now has to sell itself twice. First the agent has to pick it, then the human has to trust that pick enough to say yes.
4. I was talking to a guy who told me we should just destroy all the data centers. I think that sentiment is more common than people in SF realize. You can already see some of the big AI companies adjusting positioning their AI as something that helps people and gives them more time/cures for cancers etc.
5. AI is making people work 5x faster (at least), so for freelancers/agencies that charge per hour you're earning 5x less per unit of work. But your rate is the same? I think a lot of freelancers and agencies are about to have to rethink how they price.
6. Running a company now feels kinda dystopian sometimes, especially with a small team of a few humans and a lot of agents. Some days the Slack channel is mostly bots reporting to each other, and you're the only one in the room with a pulse.
7. Every product company is turning into a product studio. I see it firsthand as a co-founder of @meetLCA , which is basically a Labs as a Service company incubating new products for companies. (DM me if you want to work together in 2027).
8. Personal software is exploding. People at every company are building their own little tools for their own jobs. It's amazing for productivity and a nightmare for IT, because nobody knows what's running or who owns it.
9. Revenue per human is the new flex. Founders used to brag about headcount. Now they brag about how much they're doing with how few people.
11. More founders are using agents for customer research. Agents run the interviews, read every support ticket and summarize what people are begging for. Synthetic market research is the new thing.
12. Shipping technology that actually takes 3-6 months with unique partnerships that are hard to replicate. So, partnerships are sooo back!
13. Insane amount of young people (22 or younger) making absurds amounts of money. At least it feels that way. Never seen anything like that, even during mobile era.
14. Only 2% of the US is paying for AI products, but it feels like 110% SF is.
15. Advice for college grads used to be “follow your passion” and now it’s follow careers that are not document- driven, rules-bound and verifiable
16. Seeing the word inference on billboards in SF still blows my mind.
17. Agents paying for things feels futuristic. I think its coming, only a matter of time, but I’m wondering when it becomes mainstream. 2027?
18. Lots of talk about proactive agents being the next phase of personal agents. Right now you ask your agent to do something. In 2027, it’s all going to be about noticing things (context/memory) and acting first. "Your flight got delayed, I already rebooked you."
19. The quote "what the smartest people do on the weekend is what everyone else will do during the week in 10 years." by Chris Dixon. It feels like that length of time isnt 10 years anymore, its 12 months.
20. This is the most asymmetric window I've ever seen in business.
My DMs are open. Respond to the most interesting ones.
Loved my trip to SF. Until next time. Maybe NYC next?
Thank you for everything.
I'm rooting for you.
GREG ISENBERG@gregisenbergOct 14🇺🇸
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
Pushpit 🛠 viraloop.io retweeted

GREG ISENBERG@gregisenbergOct 14
🇺🇸
United StatesConnected via United States App StoreAccount-level information, not a live location or per-post device.
I spent 48 hours in SF hanging out with billionaires, data labs and AI founders and execs running $1B to $100B companies.
My observations:
1. Sooo MANY stories of data brokers making like $2M a WEEK selling company data to labs. I can't tell if it's the beginning or a bubble, and I kept wondering which niches are left. Digging into it actively.
2. At dinner, someone asked the table, "If a new model drops next month, does your company get better or worse?" HALF the table went quiet! I think that's the best question to ask about any AI startup right now.
3. The bar people talk about now is hitting $1B in revenue in 12 months (I guess that's the new unicorn?). What I didn't hear much about is retention. AI revenue can come in fast and leave fast, and I think we're going to see a lot of stories of AI companies with huge revenue and terrible retention.
4. It's pretty clear we're living through the reinvention of the computer. The model is the CPU, the harness is the operating system, and skills and plugins are the apps. Thinking about it that way makes it easier to see where the whitespace is.
5. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now.
6. Agent harnesses feel like the new GPT wrappers, in a good way, especially vertical ones. The best teams I talked to treat the model as swappable. One founder walked me through routing different models to different jobs: one for taste and visuals, a cheaper one for the everyday stuff, another for text.
They use prompts they can move across models over fine-tuning, and A/B test the output on what users actually do with it: do they edit it, export it, share it? A vertical harness is basically four things: the connectors for that industry's data, evals written by people who know the work, rules for what the agent can and can't touch, and a clean screen for a human to review. The model is maybe 20% of it.
7. There's a lot of reverse engineering going on/cloning going on more than I've ever seen. People are decompiling software and rebuilding it with agents, and someone showed a suite of like 10 apps that are worth $5B+ each all that he cloned open source. Me and everyone are still thinking through what it means when software gets this easy to copy.
8. My guess is the value moves to data. If the software can be rebuilt quickly, what's worth paying for in a legacy SaaS company is the years of customer data and workflows inside it. Some SaaS companies are secretly data companies.
Gets me thinking about how do you truly value data for this saas companies? Like if we were buying these dying saas companies at 2x ARR before (as example), maybe there is a better metric. Idk, just thinking out loud here.
9. It feels like a good chunk of legacy software won't make it to the agentic era, especially anything built around people clicking through a lot of screens.
10. Every company I talked to wants to pipe its internal data into one place and expose it to agents through MCP. Then they look at the data and realize it's a mess: five versions of the same customer, old Slack channels, PDFs nobody can search. Cleaning that up feels like a big consulting opportunity!
11. A lot of founders are building the basic building blocks for agents: APIs, data layers and tools. Some of the companies I saw barely have a UI because the main user is an agent. I really believe the startups that build primitives to this agentic era will do extremely well. Building blocks is the best way to think about it.
12. Labs can get plenty of training data, but it's harder to get real examples of agents failing inside companies. Whoever has those logs has something valuable.
13. Someone running a big consumer company told me the next fight is over memory. The model that remembers the most about you wins, and the smartest model matters less than people think.
14. Physical AI came up a lot. Astra I think was the first model that got people into AI can actually do CAD, and I think a lot of people are thinking what does vibe manufacturing really look like. The line I keep thinking about came from a robotics founder: the hardest problems in AI are deployment problems now, not research problems. Their robots work on their own, call a human when they get stuck, and every one of those calls becomes training data.
15. Cybersecurity companies are having a moment and are actually cool companies to build now, especially the ones that clean up after a breach. Every agent with access to your systems is a new door into your company, and a lot of those doors were left open.
16. Some startups are worth more dead than alive now. A shutdown company with 5 years of Slack, email and code can get paid for licensing all of it. I wouldn't be surprised if founders start factoring that into when they shut down.
17. People are starting to talk about monetizing the agent web. It feels really early, but when the most popular user of the internet is an agent, what startups should you be building?
18. I think agent insurance is coming. Once an agent makes an expensive mistake, insurers will want audits before they cover anyone.
19. Several people said the hire they want most is someone with taste who can write evals. The models are getting good at generating ideas, so the hard part is judging which ones are good.
20. New trend I observed: startup raises VC, uses venture dollars to become a creator, startup fails, founder falls back on being a full-time creator and incubates other products bootstrapped.
21. Getting SIGNIFICANT distribution is HARD right now. Founders are sponsoring buses, buying billboards like crazy and throwing parties with famous DJs to get noticed. Kinda feels dotcom era in that sense.
Attention is what's scarce! The growth guy going viral sharing his UGC strategy from Wispr Flow is a good example. It's the sauce everyone wants, and I truly believe there are probably only 50 people on the planet who know how to scale these types of businesses efficiently in this era.
Always good to get that dose of SF. I felt sooo lucky to have such an incredible trip during such an incredible time in building businesses. Surreal in so many ways.
Happy to be home and locked in.
Until next time!
Pushpit 🛠 viraloop.io retweeted
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