Gamma's product chief opens up the harness behind one AI presentation, roughly 20 models and 200 calls, and maps where the next vertical agent companies will come from.
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yesterday
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educational
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Big Idea
The argument in one line.
Models are close to interchangeable, so the product is the harness around them, and the best openings are vertical harnesses for subjective work where taste and real-user experiments, not evals, decide what good looks like.
Who This Is For
Read if. Skip if.
READ IF YOU ARE…
A founder or indie builder deciding whether a vertical AI agent is still worth building now that the big labs ship their own agents.
A product engineer moving from hand-chained LLM calls to an agent loop who wants a concrete picture of the tools, sandbox and QA passes that loop needs.
Someone building in a creative or taste-heavy space (design, fashion, interiors, games) who keeps hitting the limits of evals and benchmarks.
A growth or marketing lead who wants to know how a company at nine-figure revenue tests new models and product changes in hours instead of weeks.
A builder hunting for a startup idea who wants specific, unclaimed examples instead of another list of crowded categories.
SKIP IF…
You want a hands-on build tutorial. This is a strategy conversation with no code, no repo and no step-by-step setup.
You are training foundation models. The guest's stance is that almost everything comes from prompts and context, with fine-tuning as a late cost optimization.
You already run a mature A/B testing program. The experimentation sections cover fundamentals you likely practice daily.
TL;DR
The full version, fast.
Most of the value in an AI product is the wrapper, not the model. An agent harness is the set of tools an agent uses to gather context, act in its environment, check its own work and hand back something finished. Gamma's version spins up a cloud sandbox, asks clarifying questions, pulls in connectors, picks a template with vision models, generates slides and images in parallel, then runs several rounds of visual QA, about 20 models and 200 calls per deck. The open opportunities are vertical harnesses for subjective work people care about, where founder taste, proprietary data or a physical service is the edge. Ship on frontier models, lean on prompts over fine-tuning, and judge changes with live A/B tests on real behavior like edits, shares and paid upgrades.
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Setup: Gamma's co-founder and CPO on agent harnesses, the platform relaunch and the startup openings around vertical agents.
01:23 – 04:25
02 · What an agent harness is
From hand-chained LLM calls to one agent loop. The harness is the tools that let the agent act, inspect its work and deliver. Why the wrapper turns out to be most of the work.
04:25 – 08:23
03 · Inside one Gamma prompt
A messy brief becomes a deck: sandbox, clarifying form, connectors, outline, template search, parallel generation and rounds of vision QA. About 20 models and 200 calls.
08:23 – 13:48
04 · Where vertical harnesses fit
Subjective work with a human in the loop is the sweet spot. Personal video games, physical services, legal and medical. Three kinds of edge and the 'what tools do I uniquely have' test.
13:48 – 15:49
05 · AI smell
When everyone used Gamma, everything looked like Gamma. The fix was a system that cycles creative looks like fashion trends, a thousand smells instead of one.
15:49 – 18:40
06 · Evals versus taste
What an eval is, why it fails on 'does this look cool', and why Gamma leans on large A/B tests measured by edits, shares, paid upgrades and retention.
18:40 – 22:01
07 · Who pays, and the harness he wants
Consumers dodge AI subscriptions while professionals pay. Find your own domain for 'Gamma for X'. The Slack agent that learns your voice by watching you.
22:01 – 26:25
08 · Building on other people's models
No shame in renting frontier models. Closed models are underrated, the stack is broad but shallow, 98% prompts, and new models get tested within the hour.
26:25 – 29:10
09 · Running experiments
Eight tests fired off from an Uber for a new small model. The fast-mode test that lost because the speed bumps made users more invested.
29:10 – 36:19
10 · The marketing engineer
Mass personalization never arrived and brand still wins, but marketers now run agent armies. Salesforce-to-deck automations and everyone getting promoted to VP of agents.
36:19 – 38:26
11 · Experiments as a numbers game
Lessons from Optimizely: at-bats beat one magic test, experimentation is a trainable muscle, and every channel has a trough of sorrow.
38:26 – 45:40
12 · Image models and startup openings
Image models are underhyped. The couch-shopping harness that measures fit, prosumer and enterprise tiers, and why a big company is a thousand niches.
45:40 – 49:21
13 · What he'd build next and closing advice
The next frontier of vibe coding: video, voice and games. Build something you'd use yourself, and marry the niche, date the product.
Atomic Insights
Lines worth screenshotting.
The so-called GPT wrapper was always most of the work: models are similar in core capability, and the harness is what turns one into a product.
A single Gamma presentation touches roughly 20 different AI models and around 200 calls before the user sees the finished deck.
Gamma makes 30 to 40 model calls before it starts building slides, just to ask questions, pull context and pick a template.
After generating a deck, a vision model inspects every slide for cut-off heads, misaligned text and bad contrast, then the system fixes and re-checks in multiple rounds.
The best cases for your own agent harness are subjective tasks where two people disagree about what good looks like and the user cares about specific output.
Copycats are copying where you were three to six months ago, so a distinct thesis about where the world is going is a real edge.
An agent harness is just a bunch of tools, so the founder's question is which tools you already have or can build that nobody else has.
AI smell can't be removed entirely, but a product can offer a thousand different AI smells instead of one monolithic look everyone recognizes.
Evals work for objective checks like following brand guidelines and break down for questions like 'does this look cool' or 'does this sound like me'.
In a taste-driven domain there should be many harnesses, the way Nike and Adidas would build different ones because their taste differs.
Paid upgrades are the hardest-to-fake signal in a freemium product, which makes them the best north star for comparing two models.
Consumers resist AI subscriptions, but work subscriptions built the SaaS boom, so prosumers and B2B are where AI spending is growing.
Roughly 98% of Gamma's AI value comes from prompts, with fine-tuning reserved for cutting cost or squeezing out the last few points of quality.
Gamma usually has a new frontier model in a live user test within the hour of its release and reads results two hours later.
A fast mode that skipped the outline and questions lost its A/B test: the slower path produced higher satisfaction and more paid conversions.
Experimentation is a numbers game like sales or dating: your first test usually fails and the wins come from at-bats.
Image models are underhyped because they stopped making obvious mistakes and people kept using them for the same three or four tasks.
A couch-visualizer harness could render the couch in your room, then measure it in pixels with ordinary code and correct the size, something the labs won't build.
A big company is often a thousand little niches stacked together, so you can start with one and expand into the adjacent ones.
Marry the niche, date the product: the product will change many times, but loving the people you serve keeps you going.
Takeaway
The harness is the product, not the model.
WHAT TO LEARN
Frontier models are close to interchangeable, so durable AI products win on the tools, context, QA loops and taste wrapped around them, aimed at a niche you understand.
02What an agent harness is
An agent harness is the tools an agent uses to gather context, act, inspect its own output and deliver something finished, so most product work lives there, not in the model.
03Inside one Gamma prompt
Treat clarifying questions as part of the product: asking who a deck is for before generating changes the output more than a better model would.
Split one task into many small model jobs and run them in parallel, sending cheap models the easy work and expensive ones only where quality depends on it.
Add an automated review pass: have a vision or judging model inspect the output for concrete defects, then fix and re-check before showing the user.
04Where vertical harnesses fit
Pick problems where people disagree about what good looks like and care about specifics, because those need a human in the loop and resist generic lab products.
Your edge is one of three things: a distinct thesis you move toward faster, proprietary data, or a physical or service business software-only rivals can't copy.
05AI smell
If your output has a recognizable AI look, give users many distinct styles and keep cycling them rather than trying to remove the AI signature entirely.
06Evals versus taste
Use evals for objective checks like following brand rules, and measure taste with live A/B tests on edits, shares, retention and paid upgrades.
07Who pays, and the harness he wants
Expect paying users among professionals and teams; consumers resist subscriptions, but people pay for tools that make their work better.
A strong personal-agent idea learns from what you already do, such as drafting replies from your past messages, instead of making you fill out onboarding quizzes.
08Building on other people's models
Start on frontier models and focus on winning real usage; fine-tune only later to cut cost or handle a narrow task the big models do badly.
Get most of your quality from prompts and context because they are cheap and portable across models, and let a model write and split your prompts.
Test every promising new model on a slice of real users within hours, since the goal is often matching quality at half the cost.
09Running experiments
Removing friction can backfire: steps that make users review and shape the output raise investment, satisfaction and conversion, even when they slow things down.
Read a test result by segment, since a losing variant can still win for one group, like fast mode for new users on mobile.
10The marketing engineer
Brand discipline still beats endless personalization, because people react badly to discovering they were treated differently from someone else.
Wiring tools together like CRM to deck generator is still specialist work, so packaging what top companies do into an easy default is a real business.
Heavy AI users have effectively become managers of many agents, so tools that help an ordinary person run that operation are underbuilt.
11Experiments as a numbers game
Experimentation is a numbers game and a trainable muscle; expect early tests to fail and keep taking at-bats instead of reverting to gut calls.
Every channel, from social posts to cold email, has a trough of sorrow before results show, and success comes from pushing through it.
12Image models and startup openings
Image models can do far more than logos and menus; look for creative domains where diagrams, textures, icons or room visualizations are not yet productized.
Combine models with plain code to fix what they get wrong, for example measuring a rendered couch in pixels and re-prompting until the size is right.
Serve prosumers and professional teams, not only casual consumers, because they pay subscriptions and give you a more durable position against the labs.
Start with one niche and expand into adjacent ones; a broad user base is often many small niches you discovered by shipping and watching who shows up.
13What he'd build next and closing advice
Build something you would use daily yourself, because the journey is long and you need to want to spend hours in the product.
Choose a niche you care about more than your friends do, and commit to the audience while staying flexible on the product.
Glossary
Terms worth knowing.
Agent harness
The tools, environment, prompts and checks wrapped around an AI model so it can run a loop of acting, inspecting its own work and delivering a finished result instead of answering a single prompt.
Agent sandbox
An isolated cloud computer spun up for each task, where the agent can read uploaded files, run tools and work without touching the user's real systems.
GPT wrapper
A dismissive label for a product built on someone else's language model, implying the product layer is thin and easy to copy.
AI smell
The recognizable signature of AI-made work, such as stock phrasing in writing or a familiar image-model look, that tells people a tool made it.
Eval
A fixed, scored test case for an AI system, like a software test, used to compare prompts or models. Many evals combined make a benchmark.
Context engineering
The practice of supplying a model with exactly the right files, user preferences and instructions so an ordinary off-the-shelf model can do the task well.
Fine-tuning
Further training an existing model on your own examples to specialize it, typically to lower cost or improve a narrow task.
Pareto frontier
The set of options where you can't get better quality without paying more, used here to pick the cheapest model that still does each job well.
Marketing engineer
A marketer who builds and supervises automated systems and agents to run many campaigns and experiments per day across SEO, ads and content.
Trough of sorrow
The long stretch after starting something new where effort produces little visible result, before the compounding payoff shows up.
Prosumer
A professional consumer: an individual who buys tools for paid work, like a freelance designer, and is far more willing to pay a subscription than a casual user.
29:10 – 38:26steadyMarketing engineers and agent management
38:26 – 45:40denseImage models and concrete startup ideas
45:40 – 49:21steadyNext frontiers and founder advice
The Script
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Today we're talking with John Nerona, co -founder and chief product officer of Gamma, and we're going to talk about agent harnesses, all the opportunities around it, vertical agent harnesses. I can't wait to dive in and I want to give a special shout out and thank you to Anthropic, the team behind Claude, for sponsoring today's episode.
John, thank you so much for coming on the podcast. By the end of this episode, what are people going to learn? Thanks for having me, Greg.
I'm really excited to be here. Today, we're going to dive deep on what it means to actually build an agent in practice, including demystifying what is an agent harness and what that looks like for us at Gamma, which is a creative agent harness, an agent for visual communication. And not just, you're being humble.
Gamma is, I mean, it's grown extremely quick. I think it's hit 100 million ARR. You've just relaunched the platform.
Huge, huge release. And there's a lot you're learning. And what I hope to get out of this podcast is really just little insights around opportunities and agent harnesses, what you're learning in real time, and other opportunities that exist, startup ideas and stuff like that.
So, John, can you commit that by the end of this episode, I'm going to learn some of those things? I'm in. Let's do it.
Let's make it happen. So I want to start off by, like back here, you can see. What is an agent harness?
Because I think a lot of people, you know, scroll X, they see agent harness, but they don't really understand what it is. So can you clearly explain what it is? Absolutely.
Let's break it down into the two words, agent and harness. And I'll use maybe Gamma's journey to illustrate both. So for everyone's context, Gamma is a tool primarily for making presentations, also documents, websites, social graphics, that sort of thing.
We've actually been around for several years, long before agents were ever a thing. We started the company in 2020, really took off in 2023. And early on, we were just making these one -off LLM calls to do things like write a single slide or make the outline for a deck.
And we very manually strung them together into these preset workflows to do things like generate a presentation with 10 slides. The big revolution that's happened in the last year or two is this agent revolution, where the most powerful AI products now are not stringing together these individual LLM calls by hand, but they're actually having one higher level loop of an LLM run that will do some kind of work, inspect it, use tools, and then turn that into something over a whole series of executed calls.
And so we actually went through this whole journey over the course of this year of rebuilding our product to be an agent. And building your product to be an agent involves building a harness. So the harness is all the tools that agent uses to execute things in its environment, inspect its own work, and deliver something finished to the user, including soliciting from the user what their actual preferences are.
So probably the most famous agent harness is something like Cloud Code. Cloud Code is a harness for programming. So Cloud Code teaches the agent how to go through all the files on your system and see what's there, teaches it how to run tests, and even prompts it with certain rules, like go into plan mode in certain cases.
or run these tests after completing execution or do this stuff on GitHub. In our case, Gamma is not a coding agent. It's a creative agent.
So it needs to do things like understand your art style and creative preferences, help you craft a story. And we care a lot about visual design. We're doing things like generating images and laying them out.
So we need to know how they look and make sure they look right. So our harness takes all of those things and turns them into tools that an agent can use. And I can talk through what that looks like.
Yeah, I mean, please do. Also, I'm curious if you agree or disagree with me. I've been saying agent harnesses are the new GPT wrappers or new wrappers, right?
Is that just a good tweet or is there some truth to it? I think there's truth to it, although I always disputed this concept of the GPT wrapper originally because it sort of implies that 90 % of the work is the LLM and then there's just this thin layer on top, which is the wrapping back and forth. Maybe what harnesses illustrate is that actually the wrapping is most of the work.
You actually have all these LLMs, which are quite similar in their core capabilities, not to say they don't have some variation, but generally all the magic of turning them into a product is building the harness around them. And even the model companies like OpenAI and Anthropic are putting so much of their work into harness development as well.
So they are themselves doing wrapping of these things to achieve different capabilities. Could you walk me through what happens like in 30 seconds after someone types a prompt on Gantt? like how many models does it touch like realistically you know because i think a lot of people are are are not sure how that actually works.
Let me walk you through it. Let's go through a sample prompt. So a very typical use case for Gamma is someone will paste in a bunch of messy notes or a document or PDF and say, help me turn this into a 20 -slide presentation.
So the moment you put that prompt in, a whole bunch of things actually happen under the hood. The first thing is we actually spin up an entire computer in the cloud. This is what they call an agent sandbox.
This is what the opening I1s broke out of famously in the last few months. So we actually spin up this entire sandbox where the agent gets all these files representing the user's prompt, the file they brought in, also other context about them.
We then prompt the agent to go look through all that file and their prompt and run certain tasks. And for us, actually, one of the first tasks is usually to figure out what other information we need from the user. And so typically, we will actually pop up a form and ask a few questions.
Questions like, who's this actually for? You know, is this like an educational presentation for school? Is this for adults?
Is this for your work thing? We will actually call out often to different connectors. So we've built integrations into tools like Notion or Slack or Linear where we can actually pull in all this relevant context.
Just already in that first maybe 10 seconds, we've now made calls using probably three or four different models, including expensive and cheap ones. We'll talk about actually how we manage costs as well. Just to sort of source information and ask a few questions.
We've rendered a UI around it with some generative UI to actually show you what those choices are. Then we're gonna actually write an outline for your deck. That's usually several more LLM calls.
And we will style a template for you. So we have a library of templates with different visual styles. We're gonna use vision models, try to figure out which one's a good fit for you.
um we're going to do some cool search embedding things to sort of look at the space of thousands of templates and find the right one and even customize it if you said yeah but i want it red or blue or whatever it is then we're going to once again show that to you and let you edit it and inspect it We haven't even started making the deck yet.
And already we've probably made something like 30 or 40 different LLM calls to maybe four or five different models doing different tasks. Now you're going to hit that generate button and we are going to build up a presentation for you before your eyes. And there's actually a lot of pieces involved in doing that.
We have to write the words on the slide. We have to lay out all the things. In our case, we generate a lot of imagery as well, whether decorative or more substantive.
We're often making diagrams or infographics or charts. So actually, all of those are now more calls that are going out to different models, often in parallel. There's a huge performance consideration because if we did these one by one, it's going to be quite slow and you're going to basically leave before we ever finish.
So we do a lot of work to make it all pretty speedy. And then in the space of about a minute, you're going to actually see that whole presentation streaming in front of you and generate. And now we've made probably close to 100 different model calls.
And I would say probably 10 to 15 different AI models have been used. somewhere along the way, and we're still not done because you probably won't stick with the first draft. In fact, we don't stick with the first draft.
After we generate that first presentation, we're now gonna have a AI vision model look at every single slide and look for problems that it can find. Misaligned text, the person's head got cut off in the image, there's weird contrast, something's not padded right. It's actually amazing how many things can go wrong.
in something like a simple presentation that a human eye can detect right away. Or sometimes a human eye can't detect because it takes a designer's eye. So not everyone can, but we can train models that can catch that.
So we go through this whole round of visual QA. We actually will do multiple rounds of fixing things based on what we find in that QA and then redo them. And then after all those rounds of fixing, we will finally serve it up to you.
And now at this point, we've probably used 20 different models making like... maybe 200 different calls along the way to deliver what looks to you like one finished output. So what you've just described to me is like a workflow in a particular niche.
In this case, it's creative output. Totally. You started with presentations.
You've now moved. It's essentially a creative suite. Yep.
Where do you see opportunities to create agent harnesses outside? If you were starting a company tomorrow, where do you see an opportunity to create a vertical agent harness? What's an example of a niche that you see underexplored?
That's an interesting one. Just leaning on my experience, I think where we see a lot of value of making our own distinct agent harness is when that problem looks very different from just plain old coding. In our case, what makes it different is that it's very subjective.
There's a lot of judgment calls to make along the way where two different people will disagree about what good looks like, which means you want to have the human in the loop. And also where the person using it actually really cares about the specifics of the output. And so I would think about maybe those two traits as something that go into like, wow, these are a good case for making your own agent harness.
So just thinking loud, some examples are like, I know a lot of people are making games with cloud code. And I'm really bullish on this idea of like the personal video game where you almost like vibe code it yourself. Coding agents are already really good at making video games, but I think they're often not good at actually sourcing the player's opinions on where it should go and where it should evolve.
And if you think about things like Roblox or Minecraft, there's all these things where people are actually shaping the world for themselves. And usually it's like non -technical people who are not coders themselves. I wonder if there's a harness to be built there.
where, in fact, the whole harness runs in like a 3D world or something. I think people should really think broadly and try to, you know, in general, my philosophy of business is try to be different. Try to do something that looks nothing like what everyone else is making.
And for us, that's been a challenge because we try to be different, but then others, you know, copy us or do the same thing and we have to keep looking for more and more different. I think that's, you know, it's not even just for you guys. It's anyone who's building anything in software today.
Yeah. As soon as you... catch lightning in a bottle totally uh people are copying you but i think the thing that people i think the reality is they're copying where you were six you know three months ago six months ago right so you you as the founder have to be like okay where do i what's my strong point of view what's my thesis on where the world is going yeah and how do i essentially create like a a product studio where um you know every two weeks you know I'm building a feature zero to one, exploring a new piece of this puzzle.
I think that's totally right. And I think for anyone thinking about building their own agent harness, the really important question to ask is, what's my edge going to be? And there's a couple different types of edge you can have.
One type of edge is what you just said, which is you have a distinct vision of the world and you are just moving faster towards it. And it's not the one that others are focused on. I think another edge is you have some kind of proprietary data.
You've got some information that other people don't. Maybe because you already have a successful platform or something. I think a totally different edge too is that it's not just software because software is getting very crowded and it's a scary market to be in.
But there's hardware, there's physical services, there's all sorts of real life businesses to be made that incorporate AI in interesting ways. Physical services, can you elaborate on what you mean? Yeah, I mean, to be clear, I'm a software guy, so I don't know anything about this.
But if I were not a software guy, I'd be thinking a lot about how can I integrate AI into some kind of real life experience that people are having where there's actual... some kind of human exchange of value, whether it's like, you know, a person giving you a massage or a restaurant that's serving you a meal or whatever it is.
I think there's so many times of like real businesses that are not quite reinvented with AI yet. I don't know that they all need their own harness, although perhaps some of them do. Like another category where I'm seeing a lot of harness development going on is like legal tech, for example.
I suspect medical tech is like a big one that has a lot of interesting like data locking and regulatory stuff going on. Yeah, I think. Legal was one of those where it was like, obviously this makes sense.
Accounting obviously makes sense. I think the thing I would be thinking about is like, would I be building a legal tech AI agent harness today? Probably not.
Just because it feels so crowded. So I like when you're talking about service businesses. even boring businesses in general, like agent harnesses for HVAC companies or, you know, if someone wants to build an agent harness startup in a niche, how do they approach designing?
designing it in the sense of like you need the key insight you need the niche one really simple answer about what an agent harness is is an agent harness is just a bunch of tools that an agent uses to complete a task and so i think the question to ask is what are the unique tools that either i already have or i can build that are not already out there and ideally i think as a founder, you should always ask the question of what am I uniquely suited to do based on my life experience, where I am in the world, the people I know, that sort of thing.
And how do you think about AI smell? Yeah, totally. You know, cause I think.
A good agent harness is going to kill that smell as much as possible. And I know you have a strong opinion on that. I do.
I have been using this term AI smell all year. I've been talking about deodorizing, spraying it off, all that sort of thing. Maybe for people who just don't know the term, AI smell is what we use to refer to this fact that when people make work with AI, you can often tell they used AI.
I think it shows up most in writing, where when someone writes you an email, maybe with Claude, you're just like, oh. You don't normally use that word. That's not what you sound like.
And I notice it a lot with AI images now, where I just like walk around in San Francisco and every cafe, every restaurant has a poster where I can just smell like that's nano banana, that's GPT image. I do this stuff all day, so I can like spot them a mile away. And I think more and more people are starting to recognize that AI smell as it diffuses just through the economy and normal people.
So for us at Gamma, it was actually a huge goal of this launch. We had kind of been a victim of our own success as one of the early AI tools where when nobody used Gamma, if you used it, your stuff would look really cool and stand out. But once everybody started using it, everything started to look like Gamma and people didn't want to be recognized as using AI.
And so kind of our whole goal of this launch we just pulled off was we need to rapidly expand our sort of creative landscape. The truth is there's still going to be AI smells, but there can be a thousand AI smells instead of one monolithic one. And we can help people jump to different little pockets.
And we needed, this is another form of a harness, by the way, we needed to create our own automated system for constantly cycling through different creative looks. We actually use the analogy of fashion. Fashion has a smell too.
You're like, I know that H &M shirt you're wearing. Like I have the same one. And, you know, with fashion, there's kind of this dynamic where people are cycling through different looks.
You know, right now we're into like really big wide jeans, but soon it's going to be skinny jeans again. And so we realized we had to actually ride that cycle and give people the tools to adapt to it. So is there an eval for this looks generic?
You know, evals are such an interesting topic because I think evals work really well for things that are objective. Actually, if you don't mind, break down what an eval is. Yeah, totally.
So an eval just means a... You could think of it almost like a software test. It's a hard -coded question that you ask in a way of judging it.
So a simple example in our world of Gamma is we often want the agent to ask questions when you do something ambiguous. So we'll give it an eval like, when the user says, make a deck about tigers, the agent should ask something like, is this for kids or for adults? Because there's very different kinds of tiger presentations you could make about that.
And so... We typically use evals to test out new prompts or new models. And usually you score them and you can put a bunch of evals together into a benchmark where you say, oh, it turns out model B scores 3 % higher than model A.
Every time the labs come out with a new model, they're like, oh, Opus 7 crushes like these 15 evals. The problem for us in a very creative domain is that evals have always been hard for very subjective taste -oriented things like, does this look cool? And even more so very personal ones like, is this me?
Does it sound like? something I would say. There's no one objective eval for this.
And so we've tried to create some evals that are proxies for this, like does it follow instructions? For example, if you provide your brand guideline, does it stick to it? That's an objective question that we do test ourselves on.
But we've never limited ourselves to just evals. And I think this is actually interesting insight, too, of how you can compete in this world. Ultimately, what founders can bring to their domain is taste.
They have their own sense of what looks good. They're actually talking to their users and those users have one. And the less verifiable a domain you're in, like say fashion, the more I think there's room for these unique perspectives.
I think when it comes to clothing design, there should be a hundred different harnesses. I think Nike should have a different one than like Adidas because they have a different perspective on taste. Also for us, because we have a super diverse user base who are all different, we actually rely a lot more on A -B testing.
We will actually run two models or two prompts at very big scale and measure across that wide user base what's actually performing better because that captures subjectivity. And what's the metric that matters most there? You know, we have a bunch of different ones.
To give you a few examples, we look at things like after you generate, how much time do you spend editing what you made? Do you end up sharing it or exporting it? Are there signals that show you're investing time?
We also have a freemium product. So people will use our product for free. And then if they like it, they'll upgrade to paying.
That's kind of my favorite signal because it's hard to fake. Generally, people only pay if they're really seeing value out of a tool. So we use that one as like a North Star.
Sometimes we'll even look at like downstream retention. Like are people still coming back to the product a week later or a month later? It's obviously slower to measure, but it's also one of those hard to fake signals.
The chart that's going viral this week is I think an A16Z chart that says I think only 2 % of people are spending on AI tools. What's your opinion on that? Yeah, it doesn't surprise me.
I think in general, subscriptions are always a bit of a niche thing towards people that are doing some kind of valuable work. I don't think consumers like subscription. I, as consumers, don't like subscribing for things.
I'm still always looking to jump on somebody's Netflix account or whatever it is. No, I will not share my password with you. Are you sure?
What if I had to explain what an agent harness is? But I think it's different for work. I think for work, it's a totally different story where work subscriptions are what has driven essentially the entire SaaS boom of the last 10 to 15 years in software industry.
And those things will keep being successful. And I think that same chart says that when you actually zoom into professionals, the numbers are rising like crazy in terms of who's paying for AI. So that's certainly our main focus.
And I think most people's main focus in AI is how to find those. those prosumers, the professional consumers and how to break into B2B where there's a totally different kind of spending going on. If someone wanted to build the gamma for X, what would, what would you pick?
Like if someone listening to this is like, wow, like, you know, I'm inspired by what gamma is building. Yeah. But I, I think that like, you know, basically what you just described, but like for a different niche, anything coming to mind?
It's such a personal thing because if, you know, if it was my thing, I'd be building it already. I think you need to do the thing that I don't know anything about, whether it's like Pokemon cards or fashion or sneakers or whatever it is, like find your domain where you know what's going on and build it.
And you personally, is there like a harness that you wish existed that doesn't exist that you'd use tomorrow if it came out? You know, I'm really intrigued by this whole sort of like personal agent space. And I haven't gotten to use all the latest ones, but what I feel nobody's totally cracked is like work collaboration.
I think if I could build a harness for anything, it would be Slack. And I'm a little disappointed that Slack themselves have not built the harness for Slack. What would that look like in your mind?
So there's just this absolute flood of communication for anybody whose company uses Slack or Microsoft Teams, of course. There's so many messages going through. you're constantly trying to stay on top of like, well, which ones do I actually have to do something about?
I've seen them nibbling around the edges of this of like, you can summarize 30 Slack messages into one, but I want something that just like runs it all truly for me. And it learns me. It learns.
how I would reply before I reply. I want to be able to open any Slack thread someone's pinged me on and have the first draft drafted for me. And it learns you not by answering like a quiz or onboarding, by actually like your actions.
By observing me. Yeah, by observing. I have sent approximately a billion Slack messages.
That should be all you need to know how I'm going to reply. Right. Even an email doesn't feel totally cracked.
I've seen Gmail has the little things that are like, oh, you know, like, yeah, so I'd love to go to the dentist on Thursday, which is not how I'd ever reply in my life. I think there is so much more to just represent me in a professional setting authentically to me. Maybe that's what I'm really craving.
And it's a little bit of what Gamma does is I want an agent that can learn my voice. And of course, that's hard because it's personal. And this is why the agents haven't done it.
It's really hard to make agents personal. So at Gamma, you're building something on top of other people's models.
What's your advice for founders building on other people's models in general? I think there's no shame in building on other people's models. We have done this from the very start, often bouncing between Anthropic, OpenAI, Google models.
And so much of the discourse has always been like, no, you got to train your own stuff. You got to fine tune it because you need the moat. I have always felt that that's unrealistic for early stage startups in particular.
I mean, first of all, the money involved in training your own models is really hard. But also the models out there keep on getting better. I rarely would want to be competing against the models that are out there.
I think, though, it does change as you get further along in the journey, because you start to amass real usage. And I mean, let's be honest, just amassing real usage is the main battle people should be focusing on. By all means, use the models that are out there to get that usage in the first place.
Once you do, though, you can start to do really interesting stuff. We've actually had great success fine -tuning models to bring our costs down, and in some cases, even to start to do more personalization for people. We also have started to identify specialized tasks that the big models are bad at.
Like I mentioned earlier, this whole process of QAing a slide and telling if it's good, we've actually not had great results using any of the models out there for it. So we are actually doing some of our own research to build models for those specialized tasks. I think people should absolutely aspire to that, but by all means, use the great models that are out there.
What are your current thoughts on frontier models or cloud models versus open source, local?
I think cloud models are underrated. Again, I think open source gets a lot of hype and excitement, but follow the money. It's all going to the closed source models because they're really darn good.
So much work has been done for you. And the great thing for all of us is there is intense competition in the world of closed models. I think in a world where Anthropic had just purely run away with it, this would be actually a different story because we wouldn't begin to choose between great open AI models, great Gemini models.
I feel like all these companies are just a few months behind each other. And for us in the application layer, that's the best thing ever. It keeps the heat on them to keep doing better and better.
You've once said that you were surprised with how shallow your AI stack was. Is that still true today? You know, I would say that our AI stack is broad but shallow.
What I mean by that is we actually use a ton of different models, but we often use them in fairly simple ways. Like we get so far with prompt engineering, with providing, and maybe your listeners have heard the term context engineering increasingly of just like providing the right context to the models. I really view that as kind of the whole game here of just, and it comes back to harness building.
Can you get the right context from the user? Can you express the AI's output to the user in the right way? And can you distill all that into things that make the LLM's job really easy?
When you make the LLM's job, really easy. You can use any off the shelf model and get good results.
There's a big, uh, debate prompts or fine tuning. Yeah. You have a strong opinion there.
Yeah. I think for us it's 98 % prompts is where we get the value out of because prompting is easy and it's portable. So like, and by the way, nobody's writing prompts by hand anymore.
Like Claude's writing your prompt for him. And so it's really easy. Um, and so I think most problems can be solved with a prompt, including breaking up a prompt into different pieces.
And fine -tuning, to me, is more of an optimization on top when you want to do things like control costs or eke out that extra 2 % or 3 % of quality. When a new model drops, how quickly are you implementing it? Are you kind of waiting and seeing, or how do you think about it?
Usually within the hour. If it's a model that I have reason to believe is going to be good. Not every model is interesting, but I would say for us...
like any model from anthropic or open ai is interesting generally if they're dropping it and i think i mentioned we have all these different jobs in our harness some of those jobs we use a like, you know, Fable or Astra level model for. Some of those jobs we use a little Haiku Gemini Flash level model for.
Some we use a little baby GPT Luna model for. And so anytime any model comes in this class that can help us fill out that sort of Pareto frontier, I want to try it and see how it does. And generally, because our use case is so subjective, yes, we'll run some evals, but I just want to run it on a sample of users.
And we're very lucky to have a big user base where I can start a test in an hour and get data two hours later on how it's doing. So one of the things I learned, because I'm a co -founder of a design agency called LCA, and we actually worked on a project together at Gamma. And one of the things I learned working with you guys was you guys run a lot of experiments.
Yeah. I'm curious about maybe one experiment that the results surprised you. Because you guys are just, it feels like you guys are running a lot at a time.
We run a huge number of experiments. At any given time, we probably have 10 or 20 running. And we're cycling through.
Many of the experiments last less than a day. So walk me through the process of an experiment because I think it's really, really interesting. Well, I'll walk you through one that I just started in the Uber ride over to talk to you.
I just fired off eight different experiments because Cloud Haiku 5 .5 came out yesterday. We were really excited to get to use it because I mentioned that we have a lot of these tasks that don't need that super fable -level model. They're quick and cheap, like picking a template from a library or sometimes writing an outline based on something.
And we've been looking forward to new small models we could use to test this. So this morning, I fired off eight different tests for it. And right after we got off this podcast, I'm going to check the results and probably cancel four of them and see how they did.
And we're testing it for things like making micro edits on a slide or revising an outline, whatever it is. In terms of the kinds of things we learn from these tests, Often the great thing that we learn is that we can use a model that is half the cost of another model and get the exact same results.
But sometimes we actually get the opposite finding. We find like, wow, actually the big, beefy, expensive model is necessary to achieve a certain task. Sometimes we even discover things like good friction.
Like we actually just ran an experiment a few days ago of a fast mode of our products. And fast mode just means skip through some of those steps that I told you about earlier, like showing you an outline of your presentation or asking you questions up front. And the hypothesis was, of course, if you remove friction in the process, then people will get through it faster and get to that sort of magical value moment more quickly.
I was really rooting for this test. I wanted to see it win. But it did not win for us, actually.
It turns out those speed bumps we had along the way, like seeing your outline and reviewing it, choosing the kind of high -level arc of your story, answering those questions led to much higher satisfaction ratings. Even though it was slower, the people that got through felt more invested in their output and did more. And they actually converted to paying at a higher rate.
But I think what's also cool is a test is never just one answer or another. You can like dig a little bit deeper. So we actually shipped a version of fast mode for brand new users on mobile because, you know, on mobile, you don't want to be typing a bunch of answers to a question.
I just, right before this, I posted an X article about marketing engineers. My thesis is that marketing's changed. If you think about the Mad Men era, It was like, you know, you did one big commercial a year and in the 50s and 60s and 70s, then it became in the 90s and 2000s, the digital marketer, maybe a few more experiments.
Then we had the growth hacker era, a few more experiments per week. I think in 2022, I would say AI assisted marketer, more experiments. And, you know, I think we're now entering the age of the marketing engineer where you're You know, as a marketer, you're running dozens of experiments per day.
How are you able to do that? You're using AI agents to go and do that for you. I'm curious your POV on that.
Do you think that marketing has changed? And like, where do you think, where do you see it going? It's funny because before I worked at GAM, I worked at a company called Optimizely.
And this is where my love of A -B testing came from. We were A -B testing as a service. And a lot of our vision was about personalization.
This idea that, you know, as it gets easier to change your website, we are going to create a hundred little micro variations of it. And everyone will get served a slightly different version. And that will optimize conversions in all these interesting ways.
Surprisingly to me, that vision, I would say, largely has not come true out on the Internet. I would say websites are still mostly one size fits all. So are mobile apps by and large, except for these like specialized areas, which are like hyper personalized and succeeding.
When I say specialized areas, I mean things like your TikTok feed, your Instagram reels. Like we know that companies are spending billions of dollars to eke out every last. you know, a dollar from you in those surfaces.
I heard that McDonald's is testing personalized pricing. No way. Wow, that sounds like a backlash waiting to happen.
That's why I saw it. Yeah, yeah. But I think maybe that McDonald's example illustrates why this isn't happening.
First of all, I think there's a real societal allergy to this kind of thing. In the same way that if you found out that we both bought the same airline ticket, but they charged me five bucks more because I was in San Francisco. Well, I think in general, like even surge pricing, like I feel like when I had surge pricing last night, uh coming home from dinner and i was like i was like oh you know like yeah it just it's not a good feeling yeah yeah and you know i actually think in this age of hyper efficient you know like performance marketing Actually, what's working is companies that really doggedly invest in their brand.
This like Apple mindset of like just protect the brand at all costs. And so I think that trend is going to counter some of what you're describing of everybody just going wild with their marketing agents. That said, I still buy the thesis that like the marketing engineer is rising.
And I just love that term marketing engineer because it sounds like a contradiction. You know, much like GTM engineer sounded like a contradiction. These are supposed to be different departments.
But I've loved seeing how everybody's skill sets are just blending. We're seeing this with Gamma, actually, where people are more and more using Gamma not to just make one presentation here or there, but creating these really cool automations where they're doing things like, every time a new deal opportunity is created in Salesforce, let's automatically create a pitch deck based on the call notes that have happened.
How do you set that up? We've actually been building kind of connectors where you can automate more of it. So you can just like plug into Salesforce.
But I would say there's still a fair amount of plumbing going on. People are using maybe a tool like N8n or Zapier or something to trigger it all together and wire it. That is a good example of a thing where it's still the domain of specialists, I would say.
There's like these really smart people or agencies that know how to help a business like wire things up. I think there is still a business opportunity in taking these practices that are done by the top one or two percent of companies who are really savvy about engineering it and just turning it into a super easy thing that everyone does.
Another example of this, to go back to the personal agents, is like what's happening now with tools like Muse and Instinct. You could do some amazing stuff with OpenClaw or Hermes. if you could get your Linux box set up in the right place and avoid getting hacked.
Personally, I skipped out on that whole trend. I was like, I'm a busy dude. I don't have time to set up my OpenClaw.
I'm going to wait a few months for them to just make it easy for me. And now I got Muse and Instinct blowing up my phone because they made it easy for me. Right.
So you don't have a Mac mini running. I never got my Mac mini. I'm ashamed to say as an AI founder.
Going back to marketing engineering, in your mind, what does a top 1 % marketing engineer look like? Well, one thing that comes to mind is they're really a marketing engineer data scientist. So they are ultra data -driven and grounded in what's performing.
And this is something that's gotten easier than ever. Absolutely, Claude is my data scientist. And so I would say now everybody on our team is constantly armed with very fine -grained analysis of failure modes and opportunities.
They're looking at traffic on our site. They're finding gaps. I look at our...
you know, the person we have on our team who's closest to a marketing engineer. And he is just constantly thinking of new ways to do SEO, ad creative at just an unbelievable scale for one person. You know, they're like.
They've gone from being like individual creatives to people who are running this army of agents. And they are just in this high level supervisory role. Yeah.
I mean, it's basically a department. It's department. Yeah.
It's like we're getting to the place that if you're a top 1 % marketing engineer, your surface area of what you're working on isn't just. Well, the service area is huge. You're looking at SEO, GEO, you're looking at social content, you're looking at paid ads, you're looking at pretty much like all creative brand, you know.
Absolutely. I think that's what's interesting to me because I think that the people that figure out how to become a top 1 % marketing engineer. have an unfair advantage around getting their product seen.
Well, and the other 98 % are quickly becoming dinosaurs if you can't keep up with the people in the market that are doing this. I've kind of had this idea that all of us over the last maybe like year or two, at least all of us who are using these tools, kind of got this rapid series of promotions. We went from individual contributor to manager to director to like VP.
We now have these like layers and layers of agents under us in a pyramid. It turns out that job's actually kind of hard. Like there's a reason that like few people do it because you are sort of constantly barraged by information.
You're having to make so many decisions. You kind of are like overwhelmed and confused by all of it. You need tools to even make sense of it all.
You also need people to help you make sense of it all. You kind of need your assistant and you need your chief of staff and you need all of these things. And so all of us are having to rapidly upskill to essentially like how to manage a thousand people.
Building that skill is one of the most important things we can all do. Also, I think building tools to help the average person become a VP of their own life is still underexplored, going back to my Slack idea. I also think experiments in general, it feels like building a startup today or building a business in general, it feels like almost a little more science than art now.
In the sense like the ones who are going to win are the ones that have the experimentation muscle and just trying a bunch of different things. That's why I love talking to you because it feels like that experimentation muscle is, I mean, that's your thing.
What's one thing at Optimizely that you learned that every AI founder should, you know, would be good for them to know? That's a great one.
You know, one thing that comes to mind for me, I think from years of like helping other companies do experimentation was that so much of it just comes to like how many at -bats you have. It's all just sort of a numbers game, just like sales, just like dating, just like kind of anything else in life. You got to really try a lot of different stuff versus expecting that you'll run one big test that magically unlocks your business.
I like how you called it a muscle because I do think it is, first of all, because it's trainable. you know, yeah, maybe some people are born with more muscle than others, but if you put in the work, that muscle is going to grow. I also think like your first experiment is not going to work.
Totally. Just like if you're trying to get fit and you're trying to. build muscle yeah you have to just show up to the gym every day yeah and you have to put in the work absolutely and then two months later then then you might see a six -pack right but you can't expect it on day one i think uh that's the hard part about being uh an experimenter is like there's like a trough of sorrow that you have to go through um and i think what a lot of people end up doing is they're like you know what i'm just gonna go with my gut absolutely um By the way, the same is true of things like posting on social media, writing a blog, sending cold email outbound.
There's a trough of sorrow in all of it. And I think all success in life comes from swimming through some kind of trough of sorrow. What's something that's underhyped right now that you're like, you know what?
People are just not talking enough about this. I was talking with a friend of mine about voice AI. that felt like underhyped.
And right now people are talking about personal agents a lot. Anything that comes to mind? Maybe it's a lame answer, but I actually think AI image models are underhyped.
It's a lame answer because we've been talking about image models for years. Ever since like Stable Diffusion came out in 2022, people have been making like cool stuff. But I think ironically, as they got good, people stopped talking about them.
As they stopped messing up in so many ways and just became sort of like part of the fabric, we sort of like, forgot that we have this unbelievable technology that can make anything and i think if you look at the way people are using like chat gpt images and a few other things it's kind of just the same three or four use cases over and over you know they're they're making like maybe the the menu for their small business they're making like a logo for their dad's side project there's like a handful of things they're doing but these models are capable of so much and we have a really cool vantage point to this at gamma we didn't talk as much about images but we use probably like 10 different image models as well for different tasks and the pace of innovation there has been so much And just this sort of like unexplored capabilities.
For us, it's things like making beautiful diagrams or infographics to go in a presentation, making cool little textures that can be the background of a presentation, making custom icons. Like all these things are possible now and most people just don't realize it's there. And I think you can pay attention to almost like any domain that's creative and find ways that you could be using these things.
And especially the average person just has no idea. You know, even one that I think is like, probably many AI native people are doing, but most people don't realize you can do is using AI image models for like clothing and like home decor.
You know, like you found a new couch you want to buy on Amazon. You can actually just chat GPT to say, what would that look like in my house? And then say like, go find me a few others and visualize those too.
Like so many of these things are just waiting to be more deeply productized. Do you think there's an opportunity to build vertical products there? Like just focused on, you know, interior?
design, for example? Or do you think the big model companies are just going to own that use case so it's not even worth trying? I think there is so much room for these vertical use cases, and it's all about understanding the specific needs of the people that do them.
Let's just take this furniture one, for example. I actually just bought a couch recently. Congratulations.
Thank you. It's really comfy. And I went through this, and it was so interesting trying to do this.
And it turns out the AI image models are amazing at showing me what the couch will look like, but they're not actually showing me exactly how it's going to measure. And there's a huge difference between, let's say, a 68 -couch and a 58 -inch couch if your wall's a certain size. So could you actually show me the units in the display?
That's a great example of a harness. The models are actually capable of a lot of these things. One way they're capable is you could imagine doing a thing where you render the couch in my room, but then you use some good old -fashioned programming to draw a line across in pixels and measure how long it is and see if it actually came out of the right size and prompt the model to tweak it to get exactly right.
I don't see the labs doing that anytime soon. Or prompt, hey, like, This couch isn't the right couch for you.
Yeah, totally. But here's another couch from Restoration Hardware that I think would be perfect for these reasons. Totally.
And dude, what I learned about couch shopping is there's hundreds of websites that sell couches and you'll have to click through a lot of little buttons on all of them. I ended up on Google Shopping. It was not a pleasant experience.
Maybe somebody's already built a great product for this and I missed it, but I haven't seen it yet. I think there's been examples. I think Levels .io has like a...
Yeah, he has an interior, I think it's called. Yeah, but I think there's still room. You know what I mean?
I think, especially as the models are getting better, the image models are getting better, the concept of a harness is getting more and more mature. There's more here. I think it's safe to say that also the consumer experience today around couch shopping or furniture shopping isn't optimized.
And what's cool about that specific use case is it's high ticket. Totally. And there's margin, right?
For example, I don't think it's crazy to do a deal with Restoration Hardware where they're like, we'll give you 5 % commission if you recommend. If you sell us more couches, yeah, absolutely. I think the other version of this, when you think about what will survive competition with model companies, And we're certainly facing this question constantly at Gamma.
There is the pure consumer version, which to use the couch analogy is like help me buy a couch. But then there's the like prosumer version, which is, hey, I am an interior designer. Maybe I'm a part -time interior designer, but I also have like two kids at home and that's what I do.
But I do it by the hour. Those people need a tool and they will seek out the best. And if it helps them do their job better, they will happily pay a subscription for it.
Then there's also the true pros. Restoration Hardware itself has people who work there. What tool are they using?
I bet it's not just ChatGPT to do all of this. I think there is actually an opportunity to build companies for those. And I do suspect that highest tier of professional, the bigger companies, is where you can really build something durable.
Yeah, and I feel like it's underexplored. Yeah, it is. Because the narrative, I think, is there's not that much opportunity creating vertical SaaS or vertical AI products, which is unfortunate because I think that there is.
And it's a big world out there. That's maybe one other thing we've learned from the Gamma journey. Our user base is very broad.
If you just think about how many people on Earth make presentations, it's a lot of different kinds of people. For us, Less than a quarter of our revenue is in the United States.
It's spread across many different job roles from consultants to teachers to marketers to salespeople to lawyers. I didn't know lawyers make presentations. Turns out they do.
And the more you can tap into little niches, in many ways, a big company is just a thousand little niches piled on top of each other. So if you can find something that cuts across, and you don't have to start broad. You can start with just one, but then look for the adjacencies you can tackle.
100%. And you actually don't know where it's going to lead, right? Totally.
You didn't realize like, hey, lawyers are creating presentations and then you end up, who knows, you end up creating something specific for lawyers. Absolutely. And then all of a sudden that becomes a huge part of your business.
Yeah, totally. And that's why I'm always like... get started like put something out there put something out there you gotta just find out that goes back to experimentation experimentation doesn't just mean run an A -B test with like 10 ,000 users in each arm it usually just means put up a landing page get a product someone can try which has never been easier if you I know you're not leaving Gamma but like let's say you were to leave Gamma tomorrow what's something what's a company you would start to build oh that's a good question man you know it's I just love circling this space of like enabling people's creativity.
I think there's other kinds of like creativity that like intrigued me though. Like as crowded as the space is, I'm so intrigued by just like vibe coding as I practice and just the way I had this experience, maybe as somebody who knows a lot of other tech people and lives in San Francisco earlier this year, where every time I would catch up with a friend I hadn't seen in.
three or four months, I'd be like, what are you up to? And they'd be like, check out my app I made. It sort of took the world by storm.
I want to know maybe what's the next vibe coding or what's the next frontier of vibe coding. As people can make anything, where will that take them? And so like within that, I'm certainly intrigued by video models finally getting good.
I'm intrigued by voice models getting good. Like what's the version of some kind of thing you could call and it would do something for you. Games are so interesting to me.
I just think like the video game industry is ripe for some kind of disruption, but I haven't cracked it yet. I don't know. I think on the video side, it does feel like we're hitting like the vibe coding moment for video.
Yeah. Opus 5 .5 has, is obviously. It makes some cool stuff.
Makes some cool stuff. Yeah. So I think the question is in a world where anyone can create video.
what are the types of videos they can create? And then I'm also interested in, as a founder, I'm like, okay, in a world where anyone can create video, what are some startup ideas that I can create on top of that? And that goes back to agent harnesses.
Yeah, absolutely. Everything goes back to agent. Everything goes back to the harness, baby.
Dude, thank you so much for coming on the podcast. Is there anything you want to leave people with? One thing?
I would just always encourage people who are thinking of starting a business to know that it is a long and sometimes lonely journey. And because of that, you really have to love the thing you're working on. One of my kind of rules for myself before we started Gamma, and it would stick around if I ever did another startup, is it has to be something that I could dog food, that I could be a user of, and that I could believe in.
And to be honest, that actually rules out a whole lot of boring businesses. It was good for my soul. I just needed to have something that I wanted to spend hours every day in.
And so I always encourage people to dig deep and think about like, what is your passion? And the thing that only, you know, the thing that you want to spend eight hours a day on that when you look around, your friends don't want to spend eight hours a day on and zero in on that as your North star. I think that's the important point too, around like the thing that you want to spend eight hours a day on that other people don't.
Totally. you might be really into sports or something, but a lot of other people are into sports and that might be very competitive. I think if you're choosing a startup idea or choosing a business, I always say marry the niche, date the product.
I love that. Fall in love with who you're trying to serve. Because we live in an age where you're going to be iterating on the product so many times and it's going to look so different.
But if you love the niche, And that's why I always encourage people to build media, build a following in that niche because then you can sell them a bunch of different products. Now you have a built -in audience.
I think that's totally right. You know, Jeff Bezos has a very similar quote where he says, be stubborn on the vision, flexible on the details. Well, I'm glad I'm in good company with Jeff Bezos.
You sure are. Thank you. I appreciate you.
Thanks for having me. This was great. This was awesome.
The Hook
The bait, then the rug-pull.
Gamma crossed nine figures in annual revenue making presentations, then rebuilt the whole product as an agent. Here its product chief walks through what that actually means: the sandbox, the questions, the twenty models and the visual QA loop behind one deck, and where the same pattern is still wide open.
Add Modern Creator as a preferred source and Google shows you more of our breakdowns in Search, Top Stories, and AI Overviews. It only changes what you see, and you can undo it in your Google settings anytime.
Add to Preferred SourcesOpens your Google source preferences with us pre-loaded. Tick the box and you're done.
A solo podcast host scrolls through one whiteboard doc, thirteen cards deep, arguing that every durable business left to build either delivers work through agents or owns the thing agents still can't replace.
Greg Isenberg watches sixty minutes of Sam Altman's Dev Day keynote and picks the four launches, out of more than twenty, that he thinks hand outside builders a real business.
A two-person breakdown of OpenAI's top-tier model that skips the game demos and goes straight to code audits, nine money-making agent prompts, and a Raspberry Pi speaker built and shipped in about 30 minutes.
A solo walkthrough of five open-source GitHub repos getting traction right now, an AI writing editor, an agent-run CRM, a video-editing agent, a skill security scanner, and a phone-controlling harness, each with the exact install command and the first small workflow to try.
Greg Isenberg and developer Vinny break down WebMCP, the experimental browser feature that lets any AI agent search, compare, and buy on a website without scraping the page, then price two startup ideas built on it.
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