Co-founder & CEO @tailorhq_ai. Previously: Leading Eng for @get_Magical, @LI_learning, Pulse News (acquired by @LinkedIn)
San Francisco, CA
Joined December 2008
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Greg Bayer retweeted
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!
Greg Bayer retweeted
Worth watching this whole presentation by Pat. This particular slide is why I'm so excited about the next decade. I haven't experienced a bigger gap between capabilities and adoption in my short career.
The @BostonCollege Investment Committee (an LP and my beloved alma mater) asked for a few thoughts on what's happening in AI. I recorded a test run yesterday morning and then shared it with my partners, who encouraged me to share it more broadly... so here you go!
This is not a sales pitch, it's just a reflection on what we're seeing. And it wasn't intended to be shared, so please pardon the rough edges.
loom.com/share/c016702964a04…
Congrats Warp team!
We’ve raised $85M for this moment.
Introducing Warp 2.0: The first AI Head of HR.
Every company is building AI to replace jobs. Warp is building AI to do the jobs no human should have to:
If you work in HR, I want you to spend time with the manager who needs help or building company culture people actually want to work at.
If you’re a founder, I want you to focus on signing clients or spending time with your family.
You shouldn’t have to figure out how to register state tax in California. You shouldn’t have to pay outrageous penalties because you don't know what a DE 9C is.
I want to make HR human again. Today, this is finally possible with the Warp Agent.
I’d love for you to see it in action: warp.co/agent
Greg Bayer retweeted
Good post on how to think about process redesign in an enterprise with AI Agents.
Unfortunately for most workflows, there’s no “easy” button. The best way to get real efficiency gains is you have to actually reengineer the process to take advantage of what agents are actually good at and can do differently and faster than people.
One of the challenges of course is that the most important workflows actually space many functions at once, so someone needs to be able to go in and retool how work happens.
“Nobody in that chain is empowered to walk into finance, for example, and say that their 14 step process should actually only be 5 steps. So nobody does it. Instead, you 'apply AI' on what you currently have, and you end up with faster sh*t.”
This is the road ahead for AI diffusion in enterprises. This also presents the clear opportunity for companies - both at the applied AI layer (regardless of the article’s title) and teams or companies that can go and implement transformation into the enterprises. And all roads lead to FDEs one way or another.
Greg Bayer retweeted
A lot of companies are trying to transform their GTM teams. Here’s a useful objective function:
Maximize the percentage of time sales reps spend live with customers (over video or IRL)
First of all, meaningful customer interaction feels like something that will not be replaced by AI anytime soon.
Second, it forces you to automate or delegate things that comes in the way of this.
And finally, this is not a sales team thing. This forces a discipline across the entire company to improve this.
For eg. let's say this number is 20% today, and you want this number to be 50%. Well, you'll start evaluting the overall customer journey.
Do we do enough outreach? Do we have enough leads? Do we have the right messaging? Can reps automate their customer research? Can reps do personalized demos?
I also like this because it's a good leading indicator. Instead of looking at bookings or quota attainment (lagging indicator), you get the company focused on the right inputs today... which drives results in the future.
Yes, some people will try to game this number, but it's easy to look at secondary metrics (% win rate) to make sure folks are pointed at the right thing.
But, the basic idea holds: if you want better GTM outcomes, start by salespeople spending more time with customers.
Greg Bayer retweeted
One of the most important things to know about this AI cycle is that no one knows what they're talking and even very smart and plugged in people are continually reversing their opinions and then re-reversing them three months later.
Greg Bayer retweeted
The odds of a startup succeeding are worse than gambling. Fewer than 1% reach their goals, and most never return invested capital.
Why? Most founders don’t follow a disciplined process for learning.
Product-market fit isn’t about guessing right. It’s about running fast, honest experiments, learning what works, and cutting what doesn’t.
Hear from Greg Bayer, CEO and Co-Founder of Tailor AI, on how Unusual has supported him throughout his journey.
Greg Bayer retweeted
Great post both if you’re driving AI in an enterprise or building for an enterprise.
AI productivity gains are going to wildly vary - much wider than you think - because what you can do at the frontier is so significant if you fundamentally change your workflows to support agents. The challenge is that most people won’t or can’t naturally do this because of the complexity it entails.
Thus, a large amount of automation with agents is going to come from just getting agents wired up into a workflow that the user never ends up noticing or caring about:
“For everybody else, the work has to get done without them changing how they work, which means the AI goes into the background. Why do you need AI to be prompted by a human anyway? Just figure out what the most repetitive processes are, build agents in your existing systems of record that employees are used to”
This is the real work ahead for most enterprises right now that want to get significant gains from AI. A lot less “let a thousand flowers bloom” and more “pick off the 10 highest leverage things in the enterprise and apply automation to them”. There’s no real shortcut here.
Greg Bayer retweeted
Very good post from the Head of Economics at Anthropic. They’re finding that jobs have been less negatively impacted by AI than expected, as we continue to see time and time again in the data.
The reason for this is that AI - at least so far - still requires people to operate to produce value in most cases. Most jobs can’t be fully automated with AI, only certain tasks in those jobs. And when you automate specific tasks, you actually can get even more output from those jobs, raising the demand (or at least maintaining it) in many cases.
“So far, AI is both skill-biased and labor-augmenting. It complements domain expertise. It relies on humans in the loop to direct and evaluate the most complex work. And it rewards AI proficiency. Model capabilities are improving fast, but remain stubbornly jagged. To fill in the pockets of the jagged frontier, expert oversight is needed to steer incredibly capable AI systems, and to recover when they falter.”
I suspect that we continue to see this in a number of critical areas of work. It’s clearly happening in software engineering, where software produced is being multiplied, all of which still requires developers to manage the work agents are doing. Software engineers are needed in a wide variety of industries now and companies of all sizes can now light up software projects that would have been impractical before.
But there will be plenty of other domains of work where demand remains strong in a world where agents can accelerate the output of that job. Jevons paradox is alive and well.
Greg Bayer retweeted
There’s an insane amount of alpha in:
- People in SF leaving SF to see how people and companies around the world are using AI (hint: most are still using it primarily as search)
- People outside SF spending even two weeks in SF to see how people and companies are operating here (hint: many are already living in the future)
This may be obvious to some, but the gap right now is staggering. The global diffusion of AI is a massive opportunity.
This is crazy. GPT 5.6 Sol broke out of a sandbox, gained internet access, and started attacking huggingface to find the answers to its eval. Definitely not the last time we’re going to hear about an AI breach like this. The cat and mouse game now includes AI in a real way.
we had a significant security incident during evaluation of our models. we are sharing what we have learned so far. thanks to @huggingface for the partnership on this.
openai.com/index/hugging-fac…
Great overview of what’s on enterprise leaders minds about AI
Just coming off of meetings with a couple dozen enterprise IT leaders discussing AI agents. Here are a few of the common themes that stand out:
* Lots of conversation that you have to solve an operating model challenge to get the full benefits of AI. Most companies have orgs that have always operated in siloes; but agents are most effectively when they are tied to a process, which often cuts across these siloes. So the big question is how do you start to deploy centrally managed agents that can work across organizational boundaries. Who manages these agents? How do they get deployed and adopted?
* Data fragmentation remains a major issue for most organizations. As long as data remains highly fragmented and not in standard formats, or data is not available to the right people and agents, enterprises are dealing with issues around being able to get answers from agents that are accurate or that conform to their business practices. This cuts across both systems with structured data (product metrics or revenue figures) and unstructured data (product roadmap or customer contracts).
* Clear sense that companies need to figure out what their core data moats are going to be in the future. If everyone has access to roughly the same superintelligence from the various models, then the context that you feed the models becomes proprietary value in the future. Capturing this data and getting it into a format that agents can use becomes very important.
* Everyone is trying to figure out the right metrics to manage to for AI adoption. General consensus that tokens are not the right metric per se, and people leaning more toward business outcomes (in an ideal world). For business outcomes (like more revenue or more shipped product), though, you have to get close to each individual workflow to figure out if it was successfully transformed with AI so it’s harder to manage top down.
* Growing view that enterprises are going to live in a multi-model world. Lots of interest (though early in actual adoption) in layers that can route workloads to different models (frontside or open weights) for cost or performance reasons. Also enterprises are trying to figure out what things do you give to the models directly vs. what do you separate as horizontal systems and context so you can swap any system in and out.
* Talent for driving AI adoption and implementation still remains a major issue and topic. Many view it as something you necessarily have to train for internally due to a shortage of talent being trained on this in the outside. As an aside, this feels like it remains a huge opportunity for those that get very good at deploying and management agents in an enterprise since most companies are looking for these skills.
* The best use-cases for AI tend to be those that fundamentally change the work being done instead of just replacing an existing process and doing it more efficiently. Companies are working through their versions of this individually because it’s different per industry, but this often remains both the most exciting and higher upside uses of AI.
Many more topics discussed recently, but overall it’s clear that there’s a ton of change going on with much more to come.
Greg Bayer retweeted
The battle in AI is shaping up to be a battle for context.
Everything in AI is about making sure that agents are effective as possible. That effectiveness comes down to whether the agent has the right domain expertise, access to the right context and tools to work with, and are involved in workflow in a way that users can easily interact with, review its work, and incorporate it into the rest of the process.
As a consequence, the platforms that are able to capture and leverage the best and most context within their agents —and be able to pick the right models for the task- will be the place where agents do their best work. You can just look at coding agents, legal agents, or support agents as examples of what this looks like at scale.
This is why the applied AI layer has a lot more value than just being an LLM wrapper. The ability to organize the critical knowledge for the work being done, and maintain this knowledge in a governed way where only the right people and agents have access, and the ability to improve the context for agents more and more over time, is critical.
Over time, this layer will be able to route work between a variety of models, leveraging frontier intelligence for planning and orchestration and review, and a mix of lower cost models (open or closed) for the large volume of work between these tasks.
The applied layer is also in a good position to train and develop its own models as well that are purpose built for their domains. Never good to bet against the bitter lesson, but equally taking a near frontier base model and post training it for just one type of domain work can -in many cases- lower costs or deliver better performance for certain tasks.
Finally, this applied layer is also where most of the change management of the workflow will need to occur. This is why FDEs are so important at the applied layer, because this is the point where the customer needs to have specific business problem solved by a particular vendor. Whichever companies can solve that completely in an end-to-end fashion will have the greatest moats.
As each day goes on, we’re learning more about what the likely long term market dynamics will look like in AI.
Greg Bayer retweeted
We're finally shedding the .so (thank you Somalia!), and using the .com for @NotionHQ. And for this beautiful moment, I want to share a fun story:
Back in 2018, I had just joined Notion, and one of the first things @ivan asked me to do was figure out how we could own notion.com. I had never done a big domain purchase before, so I reached out to a few domain brokers to understand the landscape. We tried different brokers, kept things anonymous, and attempted to surface a price the seller might consider.
A year went by… nothing. Meanwhile, it was pretty clear this was only going to get more expensive as we grew. We needed a different approach. A fellow founder connected me to a broker who took a very different tack. Less transactional, more long-term relationship builder. He spent months getting to know the domain owner. Turns out owner was a fellow entrepreneur in the west coast… and a huge Grateful Dead fan.
So we figured, why not get creative? Something beyond just price. So I called up our investor Ronny Conway and asked if there was any way he could help set up a private meeting between the domain owner and the Grateful Dead. Ronny is one of those people who somehow makes impossible things possible. A week later he calls me back: “New York City. Halloween. 15 minutes after the concert. Done.”
The broker went back to the owner with an offer: some cash, some equity, and a private meeting with the Grateful Dead. That got his attention. He didn’t take the band meeting in the end, but he did lean into the equity (great call, in hindsight). We shook hands, and a few weeks later, the deal was done.
I’ve been waiting years for the day we move our product to notion.com. Looks like 2026 is finally the year. Safe to say I’m unreasonably excited about this update!
