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Time charter

* [ai](/partner-content/charter/tag/ai/)  
## ai  


# What AI-Native Companies Do Differently

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![Jacob Clemente](https://static.time.com/v3/assets/bltea6093859af6183b/blt8be0b5713b6c9df7/6a0e2662cee0f3a9e9053e7e/image_(23%29.png?branch=production&width=1200&quality=75&auto=webp&crop=1:1)

by 

[Jacob Clemente](https://time.com/author/jacob-clemente/)

Senior Reporter, AI & Work

Jun 26, 2026 10:30 AM UTC

![](https://static.time.com/v3/assets/bltea6093859af6183b/blt670ca3c8c21ccc09/6a3d9579630ff471442bb620/charter-kim.jpg?branch=production&width=1200&quality=75&auto=webp&crop=3:2)

Art: Charter

![Jacob Clemente](https://static.time.com/v3/assets/bltea6093859af6183b/blt8be0b5713b6c9df7/6a0e2662cee0f3a9e9053e7e/image_(23%29.png?branch=production&width=1200&quality=75&auto=webp&crop=1:1)

by 

[Jacob Clemente](https://time.com/author/jacob-clemente/)

Senior Reporter, AI & Work

Jun 26, 2026 10:30 AM UTC

The companies being built around AI look different from the ones that came before them. A [new working paper](https://papers.ssrn.com/sol3/papers.cfm?abstract%5Fid=6905079) from INSEAD's [Hyunjin Kim](https://www.linkedin.com/in/hyunjinvkim/) and Harvard Business School's [Rembrand Koning](https://www.linkedin.com/in/rem-koning/) puts hard data behind what many have observed anecdotally: Compared with non-AI startups, AI-native firms are 25% smaller, employ fewer entry-level workers and managers, and have flatter organizational structures—yet carry similar valuations. 

The research draws on data about thousands of Y Combinator startups and a broader universe of US venture-backed companies to observe how AI-native firms work differently. Charter spoke with Kim about what these findings mean for established companies, why simply rolling out AI tools isn't enough, and the problem with still asking whether—rather than how—companies should reorganize around AI. Here are excerpts from the conversation, edited for length and clarity:


**You've spent a lot of time studying these companies. If you were to try to generalize, what do they do differently?**

The biggest shift is really about not focusing on whether to use AI or how much AI to use. That's the place where a lot of companies get stuck. \[They focus on\] activity, or how do you do the exact same work that you used to do a hundred times faster or a hundred times more: how you get more lines of code, more documents, more emails, and maybe more tokens—and not \[on what\] actually changes within the firm. 

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The companies where we see that the economics of the firm are changing…they're thinking about, ‘How do we use AI inside the company to change the economics of scaling? What's your core production process? How does what you do turn into growth?’ For most companies, it's actually not going to be about writing your emails faster.

Let's start with one \[AI-native company from the research\], [Fazeshift](https://www.fazeshift.com/). They're trying to get their customers paid in terms of accounts receivables. If you think about that very messy process, you map out where the bottlenecks are. One of the key bottlenecks is you receive all of these checks, and you need to be able to actually reconcile the text on the checks with the names of the companies \[from which\] you're actually getting the money in. If you can do that faster \[with AI\], then as long as you can also solve some of the key bottlenecks down the road…it's going to essentially get you to the output faster. What I have seen through \[our research\] is that you should start with trying to solve that mapping problem in your production process at a place that is closest to your customers, or closest to revenue.


**You found in your paper that AI-native firms have smaller workforces, and those workforces skew more senior with fewer entry-level workers, on average. What’s the mechanism behind this?**

A big part of it is what we are calling the ‘product channel’ in this paper. We talked about [Gamma](https://gamma.app/). The knowledge work \[of creating presentation slide decks\]—that used to be done by a whole economy of workers who would design and format the slides and then check, ‘Did this slide actually get designed and formatted the way that I wanted?’ Now what Gamma does is move that entire thing into the product. That whole workflow \[now\] happens without it being bottlenecked by somebody within the company.

The largest contrasts tend to be in companies where basically embedding AI into the product allows the company to scale in a different way—where they used to scale by hiring people to deliver the underlying work. \[For a business like\] tutoring or therapy…you used to hire a whole bunch of \[tutors or\] therapists because for every customer, whether it's a student or somebody who needs mental health services, you need to match that person with a human. 


One of the examples we give in the paper is [Educato](https://www.educato.com/), which is an AI-native exam prep platform. Instead of actually hiring tutors for every additional hour of tutoring that you're offering, you're building out a system that can help prompt for questions and provide answers that are on top of LLM models. And so what used to be a service business, where you would pay by the person or by the hour, essentially no longer scales by hiring. Instead, \[it scales\] by using more compute.

**What do you think AI-native firms tell us about the future of humans and AI working together?**

I would caution against moving from firm-level effects to market-level effects. Whenever we have a big technology transition, it's very difficult to actually predict where technology is going to affect jobs, because a lot of these models…are based on our current understanding of what jobs are. We're going to have new jobs. The nature of the current jobs are going to change. What the bundles of tasks are within each job is going to change.


What is actually really exciting that we are not paying enough attention to \[are three things.\] The economics of how we build and scale a company may actually be fundamentally changing. You can do much more work and get much more output and serve so many \[more\] people than you used to be able to before. With a team of 100, it might have been that I could only really serve a couple thousand customers. \[Then there’s\] the Gamma team, \[which recently had\] like 50 people but are serving like 70 million customers all across the globe.

The second thing that is really exciting is that the kinds of problems that we might be able to solve are actually becoming more diverse. There's a number of startups \[in our research\] where the economics simply wouldn't have worked out for them \[without AI\].

The third is that it also enables new people—or potentially more different profiles of people —to be able to build and create firms, because they don't need as much access to capital. 


**What else to read:** 

* Charter’s look at Hyunjin Kim’s earlier research on AI’s “[mapping problem](https://www.charterworks.com/why-your-ai-strategy-needs-a-map/)”
* CEOs are [betting big on AI](https://www.charterworks.com/ceos-are-betting-big-on-ai-while-barely-using-it/) while hardly using it
* The ‘genius bar’ PwC is using to [help build AI skills](https://www.charterworks.com/the-genius-bar-pwc-is-using-to-help-build-ai-skills/)

---

## Read more from Charter

[READ MORE](https://time.com/partner-content/charter/)

[Open article: The Ex-Meta AI Exec Trying to Solve the Entry-Level Hiring Crisis](https://time.com/partner-content/charter/the-ex-meta-ai-exec-trying-to-solve-the-entry-level-hiring-crisis/)

![](https://static.time.com/v3/assets/bltea6093859af6183b/blt652943d258d909c5/6aa482aff65bda7211021836/charter-shih.jpg?branch=production&width=1200&quality=75&auto=webp&crop=3:2)

### The Ex-Meta AI Exec Trying to Solve the Entry-Level Hiring Crisis

[READ MORE](https://time.com/partner-content/charter/the-ex-meta-ai-exec-trying-to-solve-the-entry-level-hiring-crisis/)

[Open article: Why You Shouldn’t Give AI Agents a Name](https://time.com/partner-content/charter/why-you-shouldn-t-give-ai-agents-a-name/)

![](https://static.time.com/v3/assets/bltea6093859af6183b/bltd41a41b3233b820f/6a90b839381f6b1785e817df/ai-agents-name-02.jpg?branch=production&width=1200&quality=75&auto=webp&crop=3:2)

### Why You Shouldn’t Give AI Agents a Name

[READ MORE](https://time.com/partner-content/charter/why-you-shouldn-t-give-ai-agents-a-name/)

[Open article: Why Leaders Should Ignore Predictions About AI](https://time.com/partner-content/charter/why-leaders-should-ignore-predictions-about-ai/)

![](https://static.time.com/v3/assets/bltea6093859af6183b/blt3d5ffb61a0270b51/6a7fa10a6afff54ec5eef0f4/charter-ve%CC%81ilz.jpg?branch=production&width=1200&quality=75&auto=webp&crop=3:2)

### Why Leaders Should Ignore Predictions About AI

[READ MORE](https://time.com/partner-content/charter/why-leaders-should-ignore-predictions-about-ai/)

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