The Signal Layer: What to Build When Anything Can Be Built — Lena Hall, Akamai

>> How is the conference for all of you so >> How is the conference for all of you so far? far? far? Great. Awesome. Great. Awesome. Great. Awesome. Um well, I think this was the best most Um well, I think this was the best most Um well, I think this was the best most productive year for so many of us. productive year for so many of us. productive year for so many of us. I'm Lena. A few days ago, I solved a I'm Lena. A few days ago, I solved a I'm Lena. A few days ago, I solved a production incident on a trail near a production incident on a trail near a production incident on a trail near a waterfall. waterfall. waterfall. My friend ran My friend ran My friend ran 18 agents while riding his bike. 18 agents while riding his bike. 18 agents while riding his bike. We're literally drowning in abundance. We're literally drowning in abundance. We're literally drowning in abundance. We have more output, more speed, more We have more output, more speed, more We have more output, more speed, more leverage than any of us have ever had. leverage than any of us have ever had. leverage than any of us have ever had. So, why do we have this feeling like the So, why do we have this feeling like the So, why do we have this feeling like the ground underneath is moving too fast? ground underneath is moving too fast? ground underneath is moving too fast? One of the engineers that I met at this One of the engineers that I met at this One of the engineers that I met at this conference conference conference said yesterday that it feels like said yesterday that it feels like said yesterday that it feels like the opportunity cost for not working the opportunity cost for not working the opportunity cost for not working 9:00 a.m. to 9:00 p.m. 6 days a week is 9:00 a.m. to 9:00 p.m. 6 days a week is 9:00 a.m. to 9:00 p.m. 6 days a week is too high right now. too high right now. too high right now. So, we're all token maxing. We're all So, we're all token maxing. We're all So, we're all token maxing. We're all working all the time. working all the time. working all the time. But the same abundance that made you But the same abundance that made you But the same abundance that made you fast, it also made everyone else fast.

fast, it also made everyone else fast. fast, it also made everyone else fast. So, now everyone can build everything. So, now everyone can build everything. So, now everyone can build everything. Your competitor can build your feature Your competitor can build your feature Your competitor can build your feature this afternoon, too. this afternoon, too. this afternoon, too. So, the cost of the average just went to So, the cost of the average just went to So, the cost of the average just went to zero and so did its value. zero and so did its value. zero and so did its value. A year ago, the superpower, as we were A year ago, the superpower, as we were A year ago, the superpower, as we were told, was to be good at using AI. told, was to be good at using AI. told, was to be good at using AI. But models got so good But models got so good But models got so good and they got so easy and everybody now and they got so easy and everybody now and they got so easy and everybody now is a lot more skilled at using AI and is a lot more skilled at using AI and is a lot more skilled at using AI and everybody's pointing AI at the same everybody's pointing AI at the same everybody's pointing AI at the same goals. goals. goals. Cuz AI gives everyone the same answer Cuz AI gives everyone the same answer Cuz AI gives everyone the same answer because everybody is asking the same because everybody is asking the same because everybody is asking the same question. question. question. It It on data and data is a record of It It on data and data is a record of It It on data and data is a record of what has already happened. what has already happened. what has already happened. So, when you point AI at tasks like, So, when you point AI at tasks like, So, when you point AI at tasks like, "Tell me what users want. Make more "Tell me what users want. Make more "Tell me what users want. Make more money. What should we build? Make this money. What should we build? Make this money. What should we build? Make this viral." viral." viral." It answers from the common knowledge. It answers from the common knowledge. It answers from the common knowledge. Very competent, very confident, but also Very competent, very confident, but also Very competent, very confident, but also very identical to what it tells your very identical to what it tells your very identical to what it tells your competitor. competitor. competitor. To see something that data doesn't show To see something that data doesn't show To see something that data doesn't show yet, we need to have a vision, a point yet, we need to have a vision, a point yet, we need to have a vision, a point of view, a read on where it's going, and of view, a read on where it's going, and of view, a read on where it's going, and then use all that automation to execute then use all that automation to execute then use all that automation to execute it.

it. it. AI is a really smart convergence AI is a really smart convergence AI is a really smart convergence machine. machine. machine. So, if you leave it alone, it makes So, if you leave it alone, it makes So, if you leave it alone, it makes everything the same. everything the same. everything the same. There is one decision, though, that AI There is one decision, though, that AI There is one decision, though, that AI can't and shouldn't make for you. It is can't and shouldn't make for you. It is can't and shouldn't make for you. It is to decide what to point at. to decide what to point at. to decide what to point at. So, the job, the new job for everyone of So, the job, the new job for everyone of So, the job, the new job for everyone of us is deciding what it makes, being the us is deciding what it makes, being the us is deciding what it makes, being the reason the right people choose your reason the right people choose your reason the right people choose your version over the identical-looking rest. version over the identical-looking rest. version over the identical-looking rest. But also, I'm sure many of you uh walked But also, I'm sure many of you uh walked But also, I'm sure many of you uh walked around the Expo Hall at this conference, around the Expo Hall at this conference, around the Expo Hall at this conference, and there are so many amazing products, and there are so many amazing products, and there are so many amazing products, so many tools and vendors. so many tools and vendors. so many tools and vendors. They're all solving important problems. They're all solving important problems. They're all solving important problems. But what Why do they all sound the same? But what Why do they all sound the same? But what Why do they all sound the same? So, when anyone can build anything, what So, when anyone can build anything, what So, when anyone can build anything, what makes me different? What makes you makes me different? What makes you makes me different? What makes you different? Why should anyone pick your different? Why should anyone pick your different? Why should anyone pick your version, your product? Um I call this version, your product? Um I call this version, your product? Um I call this work uh signal layer. work uh signal layer. work uh signal layer. And there are two There's two halves to And there are two There's two halves to And there are two There's two halves to solving it, to getting this right. So, solving it, to getting this right. So, solving it, to getting this right. So, that's how we will walk through it.

that's how we will walk through it. that's how we will walk through it. The first half is knowing your signal, The first half is knowing your signal, The first half is knowing your signal, being able to define it very clearly. being able to define it very clearly. being able to define it very clearly. What you're building and why it's yours What you're building and why it's yours What you're building and why it's yours and not the average. So, that's the and not the average. So, that's the and not the average. So, that's the build side, the code, the product, the build side, the code, the product, the build side, the code, the product, the road map. road map. road map. And the second half is emitting that And the second half is emitting that And the second half is emitting that signal without distortion. So, making signal without distortion. So, making signal without distortion. So, making sure that your customers um making sure sure that your customers um making sure sure that your customers um making sure what your customers come to believe what your customers come to believe what your customers come to believe about you actually matches what you about you actually matches what you about you actually matches what you believe and what you've built. believe and what you've built. believe and what you've built. That's the ship side the content and go That's the ship side the content and go That's the ship side the content and go to market engineering. to market engineering. to market engineering. And I've had an unusual vantage point in And I've had an unusual vantage point in And I've had an unusual vantage point in this. I've built products as an this. I've built products as an this. I've built products as an engineer. I created my own as a founder. engineer. I created my own as a founder. engineer. I created my own as a founder. I brought other people's products to I brought other people's products to I brought other people's products to market. So three very different jobs market. So three very different jobs market. So three very different jobs with one identical challenge. The signal with one identical challenge. The signal with one identical challenge. The signal doesn't always survive. So let's start doesn't always survive. So let's start doesn't always survive. So let's start with the build side. with the build side. with the build side. So what do we even work on? Everything So what do we even work on? Everything So what do we even work on? Everything is implementable. is implementable. is implementable. Two years ago the best autonomous coding Two years ago the best autonomous coding Two years ago the best autonomous coding agents you know solved on the fraction agents you know solved on the fraction agents you know solved on the fraction of the tasks on the standard software of the tasks on the standard software of the tasks on the standard software benchmark benchmark benchmark and now the best agents are in the high and now the best agents are in the high and now the best agents are in the high eighties. So we nearly tripled the eighties. So we nearly tripled the eighties. So we nearly tripled the amount of amount of amount of writing and shipping barely moved a writing and shipping barely moved a writing and shipping barely moved a third.

third. third. The benchmark was measuring the part of The benchmark was measuring the part of The benchmark was measuring the part of software engineering software engineering software engineering that has a greater that has a greater that has a greater and shipping is where all the ungraded and shipping is where all the ungraded and shipping is where all the ungraded parts come back in. So parts come back in. So parts come back in. So here is the rule underneath it. Anything here is the rule underneath it. Anything here is the rule underneath it. Anything that you can measure you can train that you can measure you can train that you can measure you can train against as Sarah Guo puts it. against as Sarah Guo puts it. against as Sarah Guo puts it. A compiler is a free grader. A test A compiler is a free grader. A test A compiler is a free grader. A test suite is a free grader. suite is a free grader. suite is a free grader. And the instant a task can grade itself And the instant a task can grade itself And the instant a task can grade itself you can grind a model against you know you can grind a model against you know you can grind a model against you know that grade until it wins. Automation that grade until it wins. Automation that grade until it wins. Automation of code was first because it's the most of code was first because it's the most of code was first because it's the most checkable thing that we have. checkable thing that we have. checkable thing that we have. So implementation is converging for free So implementation is converging for free So implementation is converging for free for everyone at the same time for everyone at the same time for everyone at the same time and the most buildable thing and the and the most buildable thing and the and the most buildable thing and the most valuable thing are almost never the most valuable thing are almost never the most valuable thing are almost never the same thing. same thing. same thing. So the model will build whatever you So the model will build whatever you So the model will build whatever you point it at but it will tell you nothing point it at but it will tell you nothing point it at but it will tell you nothing about where to point. Anything visible about where to point. Anything visible about where to point. Anything visible is replicatable.

is replicatable. is replicatable. So now, some people when they hear So now, some people when they hear So now, some people when they hear everything is implementable, they panic. everything is implementable, they panic. everything is implementable, they panic. Um but we can flip the question. Um but we can flip the question. Um but we can flip the question. The pointing is actually the job. It has The pointing is actually the job. It has The pointing is actually the job. It has always been the job. We just had so much always been the job. We just had so much always been the job. We just had so much implementation work in the way implementation work in the way implementation work in the way um that we never had to get good at it. um that we never had to get good at it. um that we never had to get good at it. So, how do you decide where to point So, how do you decide where to point So, how do you decide where to point that? that? that? Paul Graham shared some wisdom on this. Paul Graham shared some wisdom on this. Paul Graham shared some wisdom on this. Where you find something that people Where you find something that people Where you find something that people genuinely want is by feeling the need genuinely want is by feeling the need genuinely want is by feeling the need yourself. yourself. yourself. Build something you and your friends Build something you and your friends Build something you and your friends need because the market hasn't formed need because the market hasn't formed need because the market hasn't formed yet, surveys can't see it, and your own yet, surveys can't see it, and your own yet, surveys can't see it, and your own need is the only signal that isn't a need is the only signal that isn't a need is the only signal that isn't a crap signal. crap signal. crap signal. And the best ideas may sound genuinely And the best ideas may sound genuinely And the best ideas may sound genuinely lame at first, like a guy uh strapped lame at first, like a guy uh strapped lame at first, like a guy uh strapped with a with a camera on his head live with a with a camera on his head live with a with a camera on his head live streaming his his life. That sounds streaming his his life. That sounds streaming his his life. That sounds really ridiculous, but it became Twitch. really ridiculous, but it became Twitch. really ridiculous, but it became Twitch. Um and the convergence machine doesn't Um and the convergence machine doesn't Um and the convergence machine doesn't really, you know, propose proactively really, you know, propose proactively really, you know, propose proactively these these these uh weird, specific, genuinely uh weird, specific, genuinely uh weird, specific, genuinely embarrassing ideas.

embarrassing ideas. embarrassing ideas. But even with the Twitch example, But even with the Twitch example, But even with the Twitch example, it worked, but there were a thousand it worked, but there were a thousand it worked, but there were a thousand other similar startups I start startup other similar startups I start startup other similar startups I start startup ideas that didn't. ideas that didn't. ideas that didn't. So, the weird specific signal is So, the weird specific signal is So, the weird specific signal is necessary, but it is not sufficient. necessary, but it is not sufficient. necessary, but it is not sufficient. It's also really tempting to say that It's also really tempting to say that It's also really tempting to say that we just need to have good judgment and we just need to have good judgment and we just need to have good judgment and good taste and call it safe. But taste good taste and call it safe. But taste good taste and call it safe. But taste is really just preference under is really just preference under is really just preference under feedback, and preference under feedback feedback, and preference under feedback feedback, and preference under feedback is exactly what these systems can learn. is exactly what these systems can learn. is exactly what these systems can learn. Anything you can demonstrate enough Anything you can demonstrate enough Anything you can demonstrate enough times times times uh with a better or worse signal uh with a better or worse signal uh with a better or worse signal attached, the machine can eventually attached, the machine can eventually attached, the machine can eventually imitate. So, broad good taste is not imitate. So, broad good taste is not imitate. So, broad good taste is not really a differentiator. really a differentiator. really a differentiator. What actually resists What actually resists What actually resists training is more narrow and more training is more narrow and more training is more narrow and more durable. So, two things. durable. So, two things. durable. So, two things. Taste and judgement about what hasn't Taste and judgement about what hasn't Taste and judgement about what hasn't happened yet. happened yet. happened yet. Because there's no data for an event Because there's no data for an event Because there's no data for an event that hasn't occurred. And then taste and that hasn't occurred. And then taste and that hasn't occurred. And then taste and judgement embedded in a relationship judgement embedded in a relationship judgement embedded in a relationship that the model can't observe.

that the model can't observe. that the model can't observe. What this customer in this situation What this customer in this situation What this customer in this situation with this history that you share with this history that you share with this history that you share actually needs. actually needs. actually needs. The model has read everything ever The model has read everything ever The model has read everything ever written about your customer, but it has written about your customer, but it has written about your customer, but it has never actually met them. never actually met them. never actually met them. So, if broad judgement is not safe, and So, if broad judgement is not safe, and So, if broad judgement is not safe, and the AI just handed everyone the ability the AI just handed everyone the ability the AI just handed everyone the ability to build anything, what's left to be to build anything, what's left to be to build anything, what's left to be good at? good at? good at? Richard Hamming uh spent his career Richard Hamming uh spent his career Richard Hamming uh spent his career studying why some scientists did great studying why some scientists did great studying why some scientists did great work and others who were just as smart work and others who were just as smart work and others who were just as smart didn't. didn't. didn't. He found that the great ones worked on He found that the great ones worked on He found that the great ones worked on important problems. important problems. important problems. And the problem isn't important because And the problem isn't important because And the problem isn't important because it just sounds impressive. it just sounds impressive. it just sounds impressive. It's important when you have a It's important when you have a It's important when you have a reasonable attack on it. For example, reasonable attack on it. For example, reasonable attack on it. For example, time travel is consequential, he would time travel is consequential, he would time travel is consequential, he would say, but it's not important because say, but it's not important because say, but it's not important because nobody has an attack on it. nobody has an attack on it. nobody has an attack on it. So, Hamming would tell you to keep 10 or So, Hamming would tell you to keep 10 or So, Hamming would tell you to keep 10 or 20 ideas 20 ideas 20 ideas um um um on important problems in the back of on important problems in the back of on important problems in the back of your mind so that when you finally have your mind so that when you finally have your mind so that when you finally have an attack, a new tool, a new angle I an attack, a new tool, a new angle I an attack, a new tool, a new angle I think that only you noticed, then you go think that only you noticed, then you go think that only you noticed, then you go for it.

for it. for it. But in Hamming's world, the rarest thing But in Hamming's world, the rarest thing But in Hamming's world, the rarest thing was having an attack. And AI just handed was having an attack. And AI just handed was having an attack. And AI just handed everyone an attack on everything. everyone an attack on everything. everyone an attack on everything. So, the rarest thing is knowing which So, the rarest thing is knowing which So, the rarest thing is knowing which problem is actually worth attacking. And problem is actually worth attacking. And problem is actually worth attacking. And that judgement comes from being a real that judgement comes from being a real that judgement comes from being a real person, close to a real domain, with person, close to a real domain, with person, close to a real domain, with your own battle scars, your weirdly your own battle scars, your weirdly your own battle scars, your weirdly specific experience, the thing that you specific experience, the thing that you specific experience, the thing that you care about more than is reasonable. care about more than is reasonable. care about more than is reasonable. And you don't actually need to be first. And you don't actually need to be first. And you don't actually need to be first. You just need to be genuinely close to a You just need to be genuinely close to a You just need to be genuinely close to a problem you actually understand where problem you actually understand where problem you actually understand where your insight is in the delta between your insight is in the delta between your insight is in the delta between what AI has been trained on and what what AI has been trained on and what what AI has been trained on and what should exist. should exist. should exist. So, let's say you did it. You found that So, let's say you did it. You found that So, let's say you did it. You found that sweet spot problem that sweet spot problem that sweet spot problem that the one that you had an honest attack the one that you had an honest attack the one that you had an honest attack on, that you built this thing. It's on, that you built this thing. It's on, that you built this thing. It's genuinely good. It's genuinely yours, genuinely good. It's genuinely yours, genuinely good. It's genuinely yours, not the average. not the average. not the average. You can still lose uh because knowing You can still lose uh because knowing You can still lose uh because knowing your signal is only half the job. The your signal is only half the job. The your signal is only half the job. The other half is getting it from your head other half is getting it from your head other half is getting it from your head into the head of a person that it was into the head of a person that it was into the head of a person that it was meant for.

meant for. meant for. And it's about reaching the right And it's about reaching the right And it's about reaching the right people. people. people. And what do most of us And what do most of us And what do most of us do for that? do for that? do for that? We make content. So, let's talk about We make content. So, let's talk about We make content. So, let's talk about what uh AI convergence machine does to what uh AI convergence machine does to what uh AI convergence machine does to that. What happened to the internet in the What happened to the internet in the last 2 years? Open any feed, last 2 years? Open any feed, last 2 years? Open any feed, everything has started to sound the everything has started to sound the everything has started to sound the same. same. same. The same LinkedIn posts, the same, you The same LinkedIn posts, the same, you The same LinkedIn posts, the same, you know, three bullet points and a bold know, three bullet points and a bold know, three bullet points and a bold takeaway, and takeaway, and takeaway, and the same blog post that uh says nothing the same blog post that uh says nothing the same blog post that uh says nothing but actually looks very polished. but actually looks very polished. but actually looks very polished. Um your readers can now pattern match AI Um your readers can now pattern match AI Um your readers can now pattern match AI in just half a second. So, if a model in just half a second. So, if a model in just half a second. So, if a model could have written your post from a could have written your post from a could have written your post from a one-line prompt, your reader brain just one-line prompt, your reader brain just one-line prompt, your reader brain just skips it for the same reason. skips it for the same reason. skips it for the same reason. So, AI has really learned the algorithm. So, AI has really learned the algorithm. So, AI has really learned the algorithm. It has learned the format that performs. It has learned the format that performs. It has learned the format that performs. It has learned what gets clicks, and It has learned what gets clicks, and It has learned what gets clicks, and everyone wants to hand the machine a everyone wants to hand the machine a everyone wants to hand the machine a paragraph and say, you know, "Make it paragraph and say, you know, "Make it paragraph and say, you know, "Make it viral. Make me rich." It fills every gap viral. Make me rich." It fills every gap viral. Make me rich." It fills every gap that you leave with sameness.

that you leave with sameness. that you leave with sameness. So, what do you put in and what do you So, what do you put in and what do you So, what do you put in and what do you let it fill in? let it fill in? let it fill in? Cuz these there there are two different Cuz these there there are two different Cuz these there there are two different ways to use this thing, and they look ways to use this thing, and they look ways to use this thing, and they look very identical from the outside. One is very identical from the outside. One is very identical from the outside. One is you give it an average prompt, and gives you give it an average prompt, and gives you give it an average prompt, and gives you the average output. you the average output. you the average output. And you ship one more indistinguishable And you ship one more indistinguishable And you ship one more indistinguishable drop into an ocean of indistinguishable drop into an ocean of indistinguishable drop into an ocean of indistinguishable drops. drops. drops. So, you've automated your own So, you've automated your own So, you've automated your own irrelevance very efficiently. irrelevance very efficiently. irrelevance very efficiently. And two, you can bring in the part that And two, you can bring in the part that And two, you can bring in the part that it can't have, your specific point of it can't have, your specific point of it can't have, your specific point of view, view, view, the real story that you were actually in the real story that you were actually in the real story that you were actually in the room for, and then let the machine the room for, and then let the machine the room for, and then let the machine do the converging work, the formatting, do the converging work, the formatting, do the converging work, the formatting, the drafting, the algorithm the drafting, the algorithm the drafting, the algorithm optimization, the cleanup around the optimization, the cleanup around the optimization, the cleanup around the core that it core that it core that it could have never generated. could have never generated. could have never generated. The signal distorts on the way out. So, The signal distorts on the way out. So, The signal distorts on the way out. So, you can have the signal perfectly clear you can have the signal perfectly clear you can have the signal perfectly clear for you and still watch it fall apart for you and still watch it fall apart for you and still watch it fall apart between your brain and your users' between your brain and your users' between your brain and your users' understanding of it. understanding of it. understanding of it. And in my experience, it breaks in three And in my experience, it breaks in three And in my experience, it breaks in three places.

places. places. And there are fixes for each, but And there are fixes for each, but And there are fixes for each, but they're different depending on product, they're different depending on product, they're different depending on product, the type, and the size of the company. the type, and the size of the company. the type, and the size of the company. One of them is source distortion, which One of them is source distortion, which One of them is source distortion, which is very common in startups. is very common in startups. is very common in startups. Founders, actually, they usually know Founders, actually, they usually know Founders, actually, they usually know the signal so well the signal so well the signal so well that they always have this accidental that they always have this accidental that they always have this accidental gift of compressing it past legibility. gift of compressing it past legibility. gift of compressing it past legibility. They often assume the context that the They often assume the context that the They often assume the context that the audience doesn't have, and the room audience doesn't have, and the room audience doesn't have, and the room hears something technically very cool, hears something technically very cool, hears something technically very cool, but they but they but they doesn't they don't really understand why doesn't they don't really understand why doesn't they don't really understand why it matters. it matters. it matters. I helped this one YC company with uh I helped this one YC company with uh I helped this one YC company with uh this exact thing recently. this exact thing recently. this exact thing recently. Absolutely brilliant founders, genuinely Absolutely brilliant founders, genuinely Absolutely brilliant founders, genuinely new product, but every pitch that they new product, but every pitch that they new product, but every pitch that they started um was you know starting with started um was you know starting with started um was you know starting with architecture, with the clever parts, architecture, with the clever parts, architecture, with the clever parts, with things that they were very proud with things that they were very proud with things that they were very proud of. But it really landed as noise of. But it really landed as noise of. But it really landed as noise because the customer pain has been because the customer pain has been because the customer pain has been deleted from the whole story. deleted from the whole story. deleted from the whole story. So, we rewrote the opening to include So, we rewrote the opening to include So, we rewrote the opening to include the thing that the user hated, and this the thing that the user hated, and this the thing that the user hated, and this product actually killed. So, the same product actually killed. So, the same product actually killed. So, the same product, the same week, the next product, the same week, the next product, the same week, the next conversations converted into pilots, and conversations converted into pilots, and conversations converted into pilots, and then we turned that into repeatable GTM then we turned that into repeatable GTM then we turned that into repeatable GTM system.

system. system. Organization distortion is another type Organization distortion is another type Organization distortion is another type of distortion that almost every big of distortion that almost every big of distortion that almost every big company has. company has. company has. As signal travels through layers of As signal travels through layers of As signal travels through layers of management, through legal, through management, through legal, through management, through legal, through sales, through every department, at sales, through every department, at sales, through every department, at every hand handoff, it gets rewound every hand handoff, it gets rewound every hand handoff, it gets rewound towards the average. towards the average. towards the average. And this really doesn't come from And this really doesn't come from And this really doesn't come from incompetence, it comes from investment. incompetence, it comes from investment. incompetence, it comes from investment. So, hand a founder and the person three So, hand a founder and the person three So, hand a founder and the person three layers down the same task and the same layers down the same task and the same layers down the same task and the same AI, and you get two different things. AI, and you get two different things. AI, and you get two different things. Um the founder really sweats the Um the founder really sweats the Um the founder really sweats the unaverageable details because the unaverageable details because the unaverageable details because the outcome is really theirs and they're outcome is really theirs and they're outcome is really theirs and they're personally invested and affected by it. personally invested and affected by it. personally invested and affected by it. And others just ship it to spec, they And others just ship it to spec, they And others just ship it to spec, they close Jira tickets, they were asked for, close Jira tickets, they were asked for, close Jira tickets, they were asked for, you know, something like compliance, not you know, something like compliance, not you know, something like compliance, not as much conviction. as much conviction. as much conviction. So, a long delegation chain plus So, a long delegation chain plus So, a long delegation chain plus convergence machine is really a factory convergence machine is really a factory convergence machine is really a factory for automating the signal out of your for automating the signal out of your for automating the signal out of your own company. own company. own company. So, the first instinct usually is to add So, the first instinct usually is to add So, the first instinct usually is to add more process, which adds layers, more process, which adds layers, more process, which adds layers, bureaucracy, and slows everything down.

bureaucracy, and slows everything down. bureaucracy, and slows everything down. And we don't want that. Um And we don't want that. Um And we don't want that. Um to fix this, we have to help take the to fix this, we have to help take the to fix this, we have to help take the signal back signal back signal back and reattach it to the outcome like a and reattach it to the outcome like a and reattach it to the outcome like a founder and add the very thin signal founder and add the very thin signal founder and add the very thin signal layer to your go-to-market engineering, layer to your go-to-market engineering, layer to your go-to-market engineering, where its only job is to validate and where its only job is to validate and where its only job is to validate and carry the original intent across the carry the original intent across the carry the original intent across the handoffs intact. handoffs intact. handoffs intact. Machine distortion is another way you Machine distortion is another way you Machine distortion is another way you can lose signal. You write one careful can lose signal. You write one careful can lose signal. You write one careful launch, launch, launch, your claim, your evidence, and your your claim, your evidence, and your your claim, your evidence, and your scope is very clear, but then of course scope is very clear, but then of course scope is very clear, but then of course AI remixes it AI remixes it AI remixes it um into a tweet, into a sales deck, into um into a tweet, into a sales deck, into um into a tweet, into a sales deck, into a partner one-pager. For example, you a partner one-pager. For example, you a partner one-pager. For example, you might have had this one very narrow eval might have had this one very narrow eval might have had this one very narrow eval that scored 94% that scored 94% that scored 94% but it was repeated enough times that but it was repeated enough times that but it was repeated enough times that your customers actually heard it as a your customers actually heard it as a your customers actually heard it as a promise. promise. promise. So, we see the same through line. Your So, we see the same through line. Your So, we see the same through line. Your signal has to survive the trip signal has to survive the trip signal has to survive the trip undistorted. undistorted. undistorted. And this is something you can engineer. And this is something you can engineer. And this is something you can engineer. So, we need a thin signal layer, a small So, we need a thin signal layer, a small So, we need a thin signal layer, a small deliberate function whose job is to make deliberate function whose job is to make deliberate function whose job is to make sure that what your users take away is sure that what your users take away is sure that what your users take away is still the specific thing you meant.

still the specific thing you meant. still the specific thing you meant. Say you're building a monitoring tool. Say you're building a monitoring tool. Say you're building a monitoring tool. There are 12 other tools in this There are 12 other tools in this There are 12 other tools in this category, but yours does something category, but yours does something category, but yours does something different. It tells you what not to wake different. It tells you what not to wake different. It tells you what not to wake up for, for example. It stays quiet on up for, for example. It stays quiet on up for, for example. It stays quiet on the noise, so when you the noise, so when you the noise, so when you get paged at night, you believe it. So, get paged at night, you believe it. So, get paged at night, you believe it. So, that quiet, that trust earned by silence that quiet, that trust earned by silence that quiet, that trust earned by silence is your signal. is your signal. is your signal. So, first, say it in one sentence with So, first, say it in one sentence with So, first, say it in one sentence with the limit built in. Definitely don't say the limit built in. Definitely don't say the limit built in. Definitely don't say intelligent AI-native observability intelligent AI-native observability intelligent AI-native observability platform. platform. platform. Say something like uh stays quiet on Say something like uh stays quiet on Say something like uh stays quiet on anything it can't tie to a real user anything it can't tie to a real user anything it can't tie to a real user impact and shows you everything it impact and shows you everything it impact and shows you everything it silenced so you can overrule it. The silenced so you can overrule it. The silenced so you can overrule it. The promise and the scope are welded promise and the scope are welded promise and the scope are welded together here. together here. together here. Then make sure that the limit can't be Then make sure that the limit can't be Then make sure that the limit can't be edited out. So, in the product, every edited out. So, in the product, every edited out. So, in the product, every suppressed alert is visible. In the suppressed alert is visible. In the suppressed alert is visible. In the launch, statements like 90% fewer pages launch, statements like 90% fewer pages launch, statements like 90% fewer pages uh live next to statements like every uh live next to statements like every uh live next to statements like every silence is visible and reversible. So, silence is visible and reversible. So, silence is visible and reversible. So, when AI chops your launch into a tweet, when AI chops your launch into a tweet, when AI chops your launch into a tweet, it can keep the impressive number, but it can keep the impressive number, but it can keep the impressive number, but also remove the part that also remove the part that also remove the part that keeps the part that uh keeps the product keeps the part that uh keeps the product keeps the part that uh keeps the product honest.

honest. honest. And before you scale it, check what And before you scale it, check what And before you scale it, check what people actually heard. So, give a readme people actually heard. So, give a readme people actually heard. So, give a readme to an SRE who has never seen your to an SRE who has never seen your to an SRE who has never seen your project and ask a person to describe the project and ask a person to describe the project and ask a person to describe the product back to you. The gap between product back to you. The gap between product back to you. The gap between what they say and what you meant is the what they say and what you meant is the what they say and what you meant is the distortion that you were about to distortion that you were about to distortion that you were about to broadcast. broadcast. broadcast. And it's a very lightweight signal And it's a very lightweight signal And it's a very lightweight signal layer, and a lot of it is buildable, So, layer, and a lot of it is buildable, So, layer, and a lot of it is buildable, So, you can automate more of the checking you can automate more of the checking you can automate more of the checking and the catching and the surveying than and the catching and the surveying than and the catching and the surveying than most people realize. most people realize. most people realize. So, step back and ask what all of this, So, step back and ask what all of this, So, step back and ask what all of this, the building, the shipping, the the building, the shipping, the the building, the shipping, the undistorted signal, is actually for. undistorted signal, is actually for. undistorted signal, is actually for. It's for one thing of getting a human or It's for one thing of getting a human or It's for one thing of getting a human or increasingly an agent to choose you and increasingly an agent to choose you and increasingly an agent to choose you and rely on you when they have an infinite rely on you when they have an infinite rely on you when they have an infinite identical-looking alternatives. So, identical-looking alternatives. So, identical-looking alternatives. So, that's trust. Trust is the one thing that's trust. Trust is the one thing that's trust. Trust is the one thing that's left with no greater. There's no that's left with no greater. There's no that's left with no greater. There's no benchmark for it, no reward signal. It benchmark for it, no reward signal. It benchmark for it, no reward signal. It can't be entirely automated because it's can't be entirely automated because it's can't be entirely automated because it's granted slowly through relationship with granted slowly through relationship with granted slowly through relationship with consent. For example, doctors who consent. For example, doctors who consent. For example, doctors who open one particular tool every morning, open one particular tool every morning, open one particular tool every morning, they didn't have that habit trained into they didn't have that habit trained into they didn't have that habit trained into them.

them. them. And what happens if we get this wrong? And what happens if we get this wrong? And what happens if we get this wrong? Getting your signal wrong is actually Getting your signal wrong is actually Getting your signal wrong is actually not neutral. It's negative. not neutral. It's negative. not neutral. It's negative. Producing averageness is not free. You Producing averageness is not free. You Producing averageness is not free. You actually pay for it in tokens, in infra, actually pay for it in tokens, in infra, actually pay for it in tokens, in infra, in the salaried hours of good people, in the salaried hours of good people, in the salaried hours of good people, you know, with with customers that you know, with with customers that you know, with with customers that take a look at your product once, decide take a look at your product once, decide take a look at your product once, decide once, and never come back. So, every once, and never come back. So, every once, and never come back. So, every generic post teaches them that your name generic post teaches them that your name generic post teaches them that your name isn't worth the click. So, you spend isn't worth the click. So, you spend isn't worth the click. So, you spend real money to make yourself harder to real money to make yourself harder to real money to make yourself harder to choose. choose. choose. So, back to the main question. So, back to the main question. So, back to the main question. We got faster, We got faster, We got faster, but the speed is not where the value but the speed is not where the value but the speed is not where the value went. went. went. Uh the value moved up to deciding what Uh the value moved up to deciding what Uh the value moved up to deciding what is worth building, what is worth saying, is worth building, what is worth saying, is worth building, what is worth saying, what deserves trust. And where does the what deserves trust. And where does the what deserves trust. And where does the thing that you actually thing that you actually thing that you actually um that that you meant survives the trip um that that you meant survives the trip um that that you meant survives the trip to the people that it was for. to the people that it was for. to the people that it was for. So, you don't need to be first. You need So, you don't need to be first. You need So, you don't need to be first. You need a real problem and enough conviction to a real problem and enough conviction to a real problem and enough conviction to carry the signal clearly through to, you carry the signal clearly through to, you carry the signal clearly through to, you know, right people to find it.

know, right people to find it. know, right people to find it. So, when you can build anything, you So, when you can build anything, you So, when you can build anything, you should build trust. should build trust. should build trust. Have the strongest conviction, define Have the strongest conviction, define Have the strongest conviction, define the signal yourself, protect it from the signal yourself, protect it from the signal yourself, protect it from distortion, and use AI aggressively for distortion, and use AI aggressively for distortion, and use AI aggressively for everything else. everything else. everything else. Thank you. Let's connect and happy to Thank you. Let's connect and happy to Thank you. Let's connect and happy to chat with you afterwards. chat with you afterwards. chat with you afterwards. Thank you. Thank you. Thank you. >> [applause]