Sourced from B&T

In this op-ed, Phil Ohren, CEO & co-founder of Intender highlights the move of GPT ads going from being an industry rumour to a market reality. He believes if your first question is ‘What keywords should we target?’, you’re asking the wrong question.

ChatGPT has officially entered the media mix.

OpenAI’s Ads Manager Beta now includes campaign creation, billing, access management and reporting, with advertisers able to run campaigns in Australia, New Zealand, Canada and the US. Ads are also now appearing for ChatGPT Free and Go users in Australia and New Zealand.

The predictable response will be to ask the same question the industry asked when search advertising emerged: What keywords should we target?

But that’s the wrong question.

GPT Ads are not built around keywords. They are built around conversations. People use AI to ask for help, explore problems and seek advice before they know exactly what product, service or category they need.

The brands that win will not be the ones that target what people say. They’ll be the ones that understand what people mean.

People do not talk to AI in keywords

A family looking for a seven-seat SUV may never mention SUVs.

Instead, they might say: “Our third child is on the way and suddenly nothing fits in the car.”

Or: “Every weekend we need two cars because we can’t fit the kids, the dog and all the gear.”

Same intent. Different language.

For two decades, digital advertising has been built around signals like search terms, audience segments and browsing behaviour. Those signals still matter, but AI introduces something more powerful: context.

People don’t interact with AI using keywords. They describe circumstances, frustrations and goals.

The commercial opportunity often emerges before the obvious keyword appears.

A bank should think beyond “home loan” to the couple wondering whether rent is rising faster than their ability to save for a deposit.

An insurance brand should think beyond “life insurance” to the new parent suddenly feeling responsible for someone else’s future.

A B2B software company should think beyond “CRM platform” to the sales leader realising their pipeline forecast is fiction.

In each case, the signal exists before the search.

The new media unit is the moment

If GPT Ads scale, the winners will be the brands that understand real-world context better than their competitors.

A customer moment might be a homeowner comparing renovation options before requesting quotes, a marketing leader trying to understand why performance has stalled, a consumer deciding which financial provider they can trust.

These moments matter because they sit before the transaction. They are where people define the problem, compare options and decide who feels credible.

That’s why GPT Ads should not be treated as a small experimental item on the media plan, but prompt marketers to ask a bigger question:

Where are our customers asking for help before they make a decision?

What marketers should do next

First, stop briefing GPT Ads like search campaigns. Start with customer context, not keywords.

Ask:

• What situation makes our product or service relevant?
• What would a customer ask before they need us?
• How can our brand be genuinely useful in that moment?

Second, bring teams together earlier.

Paid media, SEO, content, brand and conversion teams need a shared understanding of the customer. In an AI environment, people don’t care which department
owns the answer – they just want help.

Third, measure quality, not just volume. Don’t just ask whether GPT Ads drive clicks. Ask whether they drive more informed prospects, stronger engagement and higher-quality demand.

Context will expose weak strategy

This is where some brands will struggle. Many organisations still operate with different teams working from different assumptions about the customer. In a conversational environment, those disconnects become obvious.

The ad, content, landing page and follow-up experience all need to reflect the same customer reality.

Generic copy won’t cut it. Neither will a keyword list dressed up as an AI strategy.

The brands that succeed will understand the situation behind the query – what customers are trying to solve, what concerns they have and what information they need next.

The future belongs to brands that understand meaning

The first wave of GPT Ads will probably resemble the early days of search: broad targeting, recycled copy and campaigns built around obvious keywords.

That won’t last. GPT Ads are not just a new channel. They’re a signal of where advertising is heading: towards context, intent and usefulness.

By all means, test GPT Ads. But test them properly.

Start with the customer moment. Build around the situation. Connect the message, content and
landing experience.

Because the future of advertising won’t belong to the brands that simply target what people say.

It will belong to the brands that understand what people mean.

Sourced from B&T

By Casey Newton

And yes, AI is a factor. Replika and Wabi founder Eugenia Kuyda on how advances in coding changed her hiring calculus

This is an interview about AI. My fiancé works at Anthropic. See my full ethics disclosure here.

Last week in our series on AI and jobs, Brookings’ Molly Kinder warned us to prepare for a “messy middle”: a long, “politically explosive” stretch in which AI job losses are concentrated among some of the best-paid workers in the economy. This week, for the first-ever Platformer live show, I wanted to talk to someone who believes in that vision: a founder building the tools that might bring it about, and who turned out to be unusually candid about what that might cost us.

I’ve known Eugenia Kuyda for more than a decade. In 2015, after her best friend Roman Mazurenko died in a car accident, she gathered the text messages he had sent to friends and family and built a chatbot that let them speak with him again — a story I covered at the time for The Verge, nearly a decade before ChatGPT made chatbots ubiquitous. That project was the seed for Replika, the AI companion app that now claims more than 40 million users. Kuyda’s latest startup, Wabi, takes AI in a different direction — away from personal entertainment and into the world of work. The app, which is now available for iOS, lets you vibe-code apps on your phone using text prompts. Over the next year, Kuyda hopes to shift more of the team’s work away from standard enterprise software toward apps built on her own platform.

Kuyda argues that we are living in “the Microsoft DOS era of AI interfaces,” and that we’re desperately in need of a Windows equivalent: an easy-to-use graphical user interface that lets the average person take full advantage of agents and personalized software. When that happens, she predicts, the long tail of subscription-based apps — the calorie counters, meditation apps, and fitness trackers of the world — will start to disappear, replaced by software that we make and share ourselves.

But what struck me most during our conversation was her answer to the question at the heart of our podcast miniseries. When we began, I expected that more tech executives would tell me they expect AI to cause job loss. Instead, it’s been the opposite — most of them have said that advances in AI will only increase demand for software engineers and other knowledge workers.

Kuyda is our first guest to say plainly that she believes that is a fantasy. The fear of job loss is “super justified,” she told me; in her view, AI has made hiring junior employees “extremely expensive and completely unsustainable for a startup,” because every hire now competes with the leverage of what she calls a “1,000x engineer.” “I think the crazy protests around jobs and AI are going to start happening,” she said. “We live in this very optimistic city, where it’s all about future, future, future — but as soon as you get out of here, it’s pretty scary.”

She’s building Wabi accordingly: the company is modelled on a soccer team, she told me, with 10 to 15 superstar “players on the pitch” who get sizable equity and public-facing roles, supported by contractors in the back office. She doesn’t think you need more than that to build a billion-dollar company anymore.

Whether that turns out to be true depends in part on Kuyda’s own bet on Wabi. Can vibe-coded apps truly compete with enterprise software in the way that she hopes? Or will most companies continue to prefer the stability and support that comes with traditional software as a service?

We should get more data on that point soon: Kuyda told me on the show that after a year in beta, Wabi will launch publicly before the end of the month.

Highlights of our conversation are below, edited for clarity and length. Listen to the entire conversation wherever you get your podcasts — just search for Platformer — or watch it on YouTube at youtube.com/caseynewton.

And let us know what you think — we’re new to podcast production, and welcome your feedback at [email protected].

Casey Newton: So it’s 2015. You are almost a decade away from ChatGPT. What were you seeing that made you think, “I can actually use the tools that are here to make a kind of prototypical chatbot, and that will be an interesting thing to explore”?

Eugenia Kuyda: We actually started a company that was building chatbot tech in 2013. What kick-started that was a friend of mine who used to work at Google DeepMind showed me this technology called word2vec, which was the original tech to basically transform language into math — to let computers understand words, in a way. ImageNet also dropped, and I was like, whoa — soon that will somehow come together, and we’ll have these new neural networks that will understand language. I used to be a journalist before that, and we had this gigantic sign in neon: “The limits of my language are the limits of my world.”

Newton: Which is a Wittgenstein quote, if I remember right.

Kuyda:I felt like if we figured out how to build language models, that would probably be the closest to understanding the world as well.

So we started building that, way before any of the first language models. Then in 2015, Google published a paper where they talked about the first deep learning model applied to dialogue generation, and we decided to hire every possible NLP researcher we could find to focus on these language models. And then, of course, after Roman passed away, we built that AI for him. We were struggling to find a consumer application, and that was it. We were like: maybe we can’t yet build a chatbot that talks eloquently with people and has meaningful conversations, but maybe we can build one that can listen, and that would probably be enough for many people out there.

Newton: How confident were you when you were doing that?

Kuyda: We thought it would work at some point. The models were so crappy in 2016, when we started Replika, that they would just produce non sequiturs. These were sequence-to-sequence models, and of course there were no models off the shelf or through APIs, so we had to build our own — this was right before people moved on to transformers. So it was very hard to say, “Okay, this will become what it is today.” We just felt that it would happen; we didn’t know when. For us it was more like: okay, maybe the tech is lagging, but it’s less about tech capabilities — it’s going to be more about human vulnerabilities. There were so many people who wanted so much to have some connection — someone to listen, someone to hear them out, to accept them, to understand them — that maybe in the beginning just those people would react positively to it, and as the tech got better, we could increase the range of people it would resonate with.

Casey: I think there’s something really poignant about the fact that even though the technology, by today’s standards, was maybe not that good, the human desire and need for support and connection was so powerful that people looked right past it. But at the same time, I wouldn’t downplay the technology either, because when I was talking with Roman’s friends and family, the part of the story that will still make me cry when I tell other people about it is how much people learned about their friend and family member after he passed away, and how their relationship with him changed after he passed away, because of the conversations they were having in this app. That was honestly the moment when I started to take AI more seriously, because I thought: if people can feel that deeply even in this very primitive version of the thing that we have today, there just has to be something there.

Kuyda: I think so. And looking at my previous relationships — oftentimes we do have relationships with people where maybe they don’t respond that much, or it’s more about our fantasies. How much do we put in? A good example is talking to God. So many people talk to God, and maybe he doesn’t really respond.

Newton: He’s sort of famous for leaving you on read.

Eugenia: I also had a lot of experience going on dates where you just listen, and maybe ask, “Oh, tell me more,” and then the guy would be like, “Oh, that was the best conversation I’ve ever had.” And you space out half the time when they’re talking — you’re thinking about all the groceries you need to buy. So I’m like, if this is the level of understanding that’s required for the most amazing conversation, we can probably build that.

Casey: Once you realized how low the bar was, you thought, “There’s a unicorn here.” 

I want to zoom out and ask you a question about your two companies, because on the surface they look quite different, right? One is about an AI that you develop relationships with; the other is a tool for making apps. In your mind, are they completely different, or do you see a through line there that you’re chasing?

Eugenia: They’re definitely different things, but for me the idea was always: how can we make a person’s life better, or help them unlock their potential? With Replika it’s easy — it was always about building an AI to help people flourish and feel better in the long term. We had some big studies published around that with Stanford and Harvard, some of them published in Nature, where we proved we were doing that.

With Wabi, the idea is: most of our time today is spent on our phones, using software that’s not built by us — built, in David Foster Wallace’s words, by people that don’t love us, that want us to just scroll or click on things. We shape our buildings, and then they shape us. It’s the same with software — we shape our software, and then it shapes us. Only we don’t shape it; someone else does.

So in this new era, where anyone can really build something in a matter of a few seconds, why not let people take a little bit more agency? Maybe not build every app they use, but at least have software be more decoupled from this model where every app needs to be a business. Wabi is a platform where people can make apps, but can also discover, remix, and use them with their friends and their families. It’s a social platform where you can quickly spin up any app, or find any app, and start using it with whoever you want. In my case, that means creating software that really fits my life — whether it’s helping me learn more about the art movements I’m into, or the language I forgot, or teaching my kids something, or finding cool events to take my kids to, or even just a better weightlifting tracker.

Casey: What is a feature or a design element of something you’ve built that made you feel like, “This is truly, personally for me — I would not expect to encounter this kind of app anywhere else”?

Eugenia: When you take away the idea that you have to make an app, put it on the App Store, distribute it, and make it for some audience, it can just be n-of-one. For me, I have an app that teaches me a daily philosophy concept.

But that’s the simple way of putting it. The more interesting reason I really decided to work on this is that I do believe we’re in the Microsoft DOS era of AI interfaces, where everything’s a chatbot. I’ve worked for 10 years on a chatbot, and I do believe there will be a GUI moment — a Windows, macOS moment — that will come to AI. Mostly because even though the model capabilities became so much better over the years, most people — normies, I guess, us included — still use ChatGPT and Claude mostly the same way they used them in 2022 and 2023: ask questions, search, do homework. That’s it. It’s not all these crazy agents — they’re not spinning up cron jobs or figuring out Claude Cowork, even. And really, that’s because through text, through a chatbot, it’s very hard to discover anything.

Casey: It feels like talking to the Alexa in your house, right? It can set a timer, and it can check the weather, and it can do 1,000 things, and you don’t know what those things are — so you just use it to check the weather and set a timer.

Eugenia: Exactly. But even if you set a timer, you need to see that timer. Chat is great as one of the interfaces; it cannot be the primary one. People love to tap, tap, tap, click, click, click, scroll, scroll, scroll. And that’s the only way to really make things discoverable and multiplayer.

Casey: Talk about the demand that you’ve seen for Wabi so far. Sometimes I feel like a freak, because I like to use software — I love productivity tools. Most people don’t feel that way. So talk to me about these people who are out there saying, “I need to build a philosophy app that only I will understand.”

Eugenia: It’s really just about making this tool simpler for people to use. We’re still in private beta — we’re going public in the next two weeks, so I’m super excited about that.

But I think we grow up being more creators, and at some point we become consumers. Kids use Roblox — kids make these games, kids hang out in these environments they make for themselves. And then at some point we just turn into these passive consumers: scroll, scroll, scroll, and subscribe, subscribe, subscribe. I think once you show people that it’s actually very easy to make something — or not even make something; maybe we just suggest some apps for you that someone else made, and it’s very easy to remix them. The agent says, “Hey, I see you added this app. I know all your apps are black and white — let’s change this one into black and white, too.” So it’s proactively helping you use all this software.

But I do believe software needs to change. If we’re just using AI to write the same old apps from the past, that’s pretty boring. What needs to happen is new agentic apps, where all apps have agency and are more alive. What I mean by that is that you can change them, they can suggest how you can change them, they can grow with you, they can evolve with you — and they can also talk to you. Right now, apps can only send you push notifications. With Wabi, all apps have a chat, so the push notification becomes “Time to work out” — but you can also say “Stop messaging me” as a response. You can change everything right there in the chat. Chat becomes the way for an app to talk to you, but also the way for you to change it.

Casey: Tell me about an example of something somebody built that made you say, “This is the promise of what I’m doing, realized” — the equivalent of that early moment with chatbots, when you saw the pieces coming together and how badly people wanted it, even though the technology was primitive.

Eugenia: A couple of things from my personal experience. I built this weightlifting tracker — I’d been tracking my gym workouts in Notes, which I found out a lot of people do. We make all of our apps agentic by default, and it started talking to me after my workouts, giving me some pointers on how to improve them. So I said, “Now also talk to me during workouts, as I’m logging — tell me what I can do as the next exercise.” And all of a sudden this app just felt so much more alive, and so much better than even a really fancy-looking app off the App Store, because it was smart. And not only that — it was also connected to my Apple Health, it was connected to my other apps, so all of a sudden it had a lot more knowledge about me. That was really a magical moment.

Another one: we have a few apps for our team. Our design engineer, Alex, makes lunches for us at the office every day, so we made an app where he puts up the menu for the week, and we can all vote and comment and say stuff — “Oh my god, these poke bowls were so nice.” It was just a little bit magical, because it created another way for us to bond more as a team.

To me, the really important thing is that today we have AI that lives separately in a chatbot interface, and then we have apps on our phones, and everyone’s debating: okay, MCPs or APIs — how are they going to communicate? But really, we should not have that distinction. Every agent or agent skill should just be an app, because a normal, regular person will never understand what an agent skill is, and no one’s going to go read Markdown files on GitHub. Instead, they can totally understand: oh, it’s just an app that looks at your inbox, and whenever there’s a new email, checks whether it’s an important one and sends you a quick summary. That is super easy to understand. If I tell you it’s an email agent that triages your inbox — “here’s the Markdown file, go figure it out” — that’s hard to understand.

Casey: In a world where everyone can make their own software, what does it do to the value of software that other people are selling — SaaS companies, for example?

Eugenia: The biggest problem with vibe coding is that no one’s going to use other people’s apps if those indie developers own the backend and the data. There’s just no way — even for consumers, let alone for businesses. If I build an AI therapy app on Replit and say, “Casey, use my fantastic AI therapy app, here you go,” you’re like: okay, well, Eugenia can read all my logs. [And so you won’t use it.]

And I don’t even need to be a bad actor. Maybe I just forget to maintain it, and then all your therapy sessions go away. Or I’m bad with security, and all of that is exposed to everyone. So the only way for people to share their personal software is to build it on one platform, where all the backend stays in one place, and the platform is responsible for security, the social graph, privacy, maintenance — the apps will never go away. And for B2B, it’s kind of the same premise.

Casey: So what is the sweet spot? If you project a couple of years into the future, what is the mix on my phone of apps that other people made and apps that I made bespoke for myself?

Eugenia: I think the only big, big apps that will stay are the ones that either have network effects — the big social networks, of course — or that have basically an offline business behind them, like Instacart or Uber. You’re not using those for the software, obviously. But everything that’s just software, I think, will go — especially all the subscription apps, the long tail of the App Store. That is going away. There’s just no need for any of it — it’s already barely working. If you really think about subscription apps — if you take out dating apps, social networks, and games, just the pure software — there’s only Duolingo that actually ended up going public. Nothing on the App Store that’s purely software really became a huge, huge business.

Casey: Would you be willing to name a name, or maybe a category, that you just think is actually in a lot of trouble here?

Eugenia: Subscription apps with low retention. Fitness apps, calorie trackers, sports apps, meditation apps — pretty much every app from that lifestyle and health and fitness category. They’re not providing a lot of value. If they have low retention, that means they’re just selling stuff during onboarding — that’s the name of the game for most of these apps — and then people just leave and never come back. Instead of that, I think people will want tools that are a lot more agentic, smarter, and tailored to them, and that they can use with their friends immediately.

Casey: So I used Wabi today — I made a podcast question evaluator. And I’ll give you a bit of gentle product feedback. The app looked very beautiful, but the keyboard was floating over the UI element to submit the question. I said, “Hey, the element is covered,” and it said, “Okay, I’m going to fix that” — and then it didn’t really fix it. To me this speaks to the challenge of DIY software. What has been your experience as you’re trying to bring people along? Do people have the patience to say, “I’m going to stick with this and figure it out,” or do they hit that limit and think, “I’m just going to ask ChatGPT”?

Eugenia: That’s a great question. When we started a year ago, on our evals we had 10 to 15 percent quality, which was: pretty much nothing’s working. So we had to build a lot around it. By November, it went to 75 percent, and now it’s probably at 80-something. And with our public launch we’re actually moving away from React Native to web views, and there we’re seeing closer to 90 percent. So I think that’s just going to be solved — it’s just a matter of time. We’ve seen the cost go down dramatically, the speed improve dramatically, the evals go up dramatically. Compared to Replika — where, in 2016, to think we would have meaningful conversations with computers was really crazy — to think that developing mini apps on the go will be solved in the next year? It’s a safe bet.

And what we figured out is that people forgive when it’s theirs. My weightlifting tracker is not the most perfect one, but it’s mine. I came up with everything. I’m very proud of it. It’s like pruning your own garden — we’re so proud of our kids.

Casey: That’s very real. When I’ve used other coding tools to make little tools that I use at Platformer, you do have a sense of pride, even though all you did was type in the box.

One word that gets used a lot to talk about what you’re doing is “democratizing,” right? You’re taking something that used to be the province of an elite, and you’re putting the tools into lots of people’s hands. There are many questions right now about the near-term future of software engineering, given that tools like yours exist. You are somebody who employs software engineers. How are you thinking about that question?

Eugenia: I guess there are two sides of it. First is the beauty of the idea that everyone can build — because up until now, there were maybe 6 million Android developers and 4 million iOS developers in the world, and billions of people using these apps. That’s a real mismatch. Instead of that, now everyone can be that person. And I do think there’s something beautiful in how it’s a little easier to create software than to make content — because one could argue, well, you can make great YouTube videos or Instagram stories. But there, if you look better, if you’re richer, it’s easier for you to do these things. With an app, it’s truly the quality of your idea. Anyone can create anything, and I like that a lot.

But the second part of the story is the questions about jobs. Compared to Replika, one of the reasons I wanted to start a new company was to work again with a team of 10 to 15 incredible people, instead of 100-plus people. Because now it’s just crazy how expensive it is to hire another person if you get it wrong.

Ten years ago there was this article about “below the API” and “above the API” …

Casey: Tell me about it.

Eugenia: When Uber and the on-demand economy were really happening, the idea was that you should stay above the API. Below the API means you’re working a job where the API tells you what to do — you’re an Uber driver, and an algorithm tells you what to do. And then there are people above the API — the software developers at Uber HQ who are developing the algorithm that will tell the driver what to do. So the whole idea was: stay above the API. And now it’s: stay above the AI.

But before, you could hire a 10x engineer — incredible — but you could also hire a 1x engineer,. and okay, the difference is 10x, whatever. Now, either you hire a person who is incredible at coming up with stuff and spinning up all the agents and doing the work — you’re hiring a 1,000x person — or you hire just some person who’s going to take up the time of the 1,000x person, and it’s really expensive. So hiring a not-so-great person, or a junior person, becomes extremely expensive — and completely unsustainable for a startup. And that, I think, is really hard.

That’s really bad news, frankly. I don’t have a solution. I think tech probably needs to create a better narrative for how this is going to go. I think the crazy protests around jobs and AI are going to start happening. We live in this very optimistic city, where it’s all about future, future, future — but as soon as you get out of here, it’s pretty scary. People are really struggling to find jobs, and I think this can only get worse.

Casey: How justified do you think that fear is? Do you think that two years from now there will be more software engineers, as we know them today, or fewer?

Eugenia: I think it’s a super justified fear. And I don’t believe in this “oh, it’s just another technology, and we’ll have even more jobs” line. People say, “Radiologists still exist!” I’m like, yeah, but I’m not hiring people anymore for these junior jobs.

Casey: First of all, thank you for saying what you just said. When I’ve talked to other folks in this series, there’s been a lot of reluctance to say, “I think there are going to be fewer software engineers.” Basically to a person, everybody has said: I think there’s going to be more — or maybe we won’t call them software engineers, but we’ll have more “builders.” It sounds like you started this company assuming maybe it will never be the size of your previous company, because you’re just not going to need as many people.

Eugenia: Yeah, I really believe that.

I think two things need to change. We’re still building the software of the past using the tools of today and the future. So we need to think: what’s the software of the future? How can apps change? We don’t need the same old apps and the same old distribution platforms operating this way when you can spin up an app in seconds.

And then the second question is: we should really think about a new type of company building. It’s almost like everything changed. For example, Figma designers — that is definitely going away. It’s just completely crazy to design everything, mock everything first, and then go develop it, and then test it and iterate. Of course everything should just be built at the same time.

I do think that right now, if you’re building a startup — specifically an application-layer startup like ours — you probably need 10 to 15 people, but absolutely insane people. And in order to attract them, because so many big companies are trying to get them, you need to change what you’re offering. You’re not going to attract them with 0.1 percent of equity and whatever the startup salary is. So we’re trying to do it differently.

I’m a big soccer fan, so I’m like: okay, let’s try to do a soccer team, where there are players on the pitch and there’s the back office. What are the most important roles for us? Let’s make them players on the pitch. They’re the team. Let’s give them the fame. Let’s give them a lot more ownership than employee number 15 would usually get — all 10 to 15 will get very meaningful, sizable equity grants. It’s a relatively flat hierarchy, but they need to be absolute superstars. And because you’re able to give a little more — pay them a little more, give them fame and ownership in a way that not a lot of other startups can — you create this incredible team on the pitch.

And everyone who is just doing one thing — maybe we need someone to deal with accounting, or legal, or motion design — we hire them as contractors, even if they’re full time. We want the top people there too, but that’s part of the agreement: you guys are coming in to fill a role, and the founding team is the founding team. And the people who put on a jersey with the name of the company — we want them to be active on socials, we want them to put their names out there.

Casey: And to cook lunch.

Eugenia: Cook lunch, yeah — everything. But that allows you to hire really top-tier people, not just as a first or second employee, but even as employee number 15. And I don’t think you need more than 10 to 15 people to build a billion-dollar company.

Casey: So in this moment, it’s possible to build a billion-dollar company, and you don’t need more than 10 or 15 people.

Eugenia: Well, some people are trying to do it with one.

Casey: I imagine some folks sitting here are thinking: Eugenia, I would love for someone like you to consider me insane, and a superstar, and worthy of putting on the jersey. What does that mean in practice? Has the skill set changed? What do people actually need to be able to do in a world where maybe there are only ever 10 or 15 seats at this company?

Eugenia: It really depends, because we have designers, product people, generalists, engineers. When it comes to engineers, it’s either really incredible generalists or people who are super good at that one particular thing. For example, Swift — it’s very hard to find a fantastic Swift iOS engineer, and we went through, I think, 120 people recruiting one, for our second-highest person. Because the question is always: will GPT-6 replace the person I’m hiring? And if the answer is maybe yes, then it’s an extremely expensive hire for us — it’s better maybe not to do it, because we’ll spend more time coaching and rewriting. But for everyone on the team, I think it’s agency, product intuition, design taste — whatever your specialization — and not being an asshole. Three very important qualities.

Casey: I think I understand what you mean by every one of those, but agency could mean a lot of things. What does a high-agency person look like at your company? What are they doing?

Eugenia: Well, Alex is one.

Casey: I think we can all agree Alex is extremely high-agency.

Eugenia: But frankly, I really think management at that stage is almost counterproductive, so people need to figure out what they do. We also build a lot of agents internally that are actually managing our company. But it’s people who can decide what needs to be done, go get it done, and push it to production. That is what’s needed. Ideally, you need a few generalists who can do it all the way, and some specialized people who are very good at backend, very good at frontend, polishing the stuff that they’re building. But you really just need people who know: okay, this needs to be done — quickly agree on it, and just go get shit done. If someone needs to go somewhere and agree, and then mock something up, and then develop it — you’ve already lost, because everything’s moving so quickly. There’s no time for that.

Casey: So being able to initiate a project and get it done without much help along the way — this is a core skill that you are hiring for.

Eugenia: Yeah. Get shit done. Also known as agency.

Casey: Last question. You’ve talked a little bit about your concerns about these new times that we’re moving into. Is there anything out there that is making you optimistic about AI and jobs in the near-term future?

Eugenia: The idea that we can all be creators, and can channel our creativity a lot more — can build stuff that before was constrained by developers or designers. I think that’s cool. We spend so much time on our phones using other people’s apps, doing what other people decided we should be doing. It would be really awesome if we could build stuff that would make our lives better. To me, that really is the way to ultimately connect with other people — use apps with friends, use apps with family, use apps with people you didn’t really know before. To me, that’s the beautiful part of it: all these new opportunities that are going to open up. And I think this is probably the first time where the iPhone is somewhat fragile. Maybe there is a way to build a better operating system that’s more serving us, versus serving companies through the apps that they built.

By Casey Newton

Sourced from Platformer