# How to Build an AI-Native Company Today — Transcript (2026-09-06)

https://aidailybrief.ai/e/2026-09-06 · Listen: https://pod.link/1680633614

---

[00:00:00] A year ago, it was a very different time in enterprise AI. companies were still talking about things like how many use cases they had for AI. Now, a year on, we are no longer talking about use cases. Everything, it turns out, is a use case for AI.

And in fact, in 2026, the long- awaited much discussed AI, transition to agentic AI actually began

Surrounding that, companies have undergone a significant transformation process, one that pretty much everyone is still in the midst of. And yet, as companies try to become more AI native, the question is, what does that actually mean?

What are the hallmarks and characteristics of companies that are not just glomming AI and agents onto old processes, but are really doing things in new ways, redesigning from the ground up?

While it's all still emerging, I think we're at the point where we are starting to see a set of features and characteristics that define AI-native companies



podcast and... The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. [00:01:00] All right, friends

Quick announcements before we dive in. First of all, thank you to today's sponsors, Blitzy, Section, Robots and Pencils, and Hyperagent

To get an ad free version of the show, go to patreon.com/aidailybrief, or you can subscribe on Apple Podcasts



and to learn more about sponsoring the show, send us a note at sponsors@aidailybrief.ai.

Finally, we're coming up on your last chance to sign up for the latest cohorts of the next executive agent training programs from Superintelligent. So if you listen to this episode and decide that you too want to be more AI native and think those could help, you can check them out at training.besuper.ai. 

Welcome back to the Welcome back to the AI Daily Brief. Today we have a fun one I feel like at this point, pretty much all of you are either at big companies who are trying to adapt and the next version of themselves in this AI-enabled world, or are the people who are being hired by those companies to help them with that adaptation.

whichever side of that table you find yourself on A core question that exists underneath all of that transformation [00:02:00] is

What are we actually transforming into? A term that gets thrown around a lot is AI native

Part of the attraction of the term is that it separates companies that have simply glommed on AI and agents to old processes from those who have actually rethought from the ground up to take advantage of this new era

But what is the substance of AI nativeness?

what, part of what makes it great for a podcast is that there are a lot of things

and many of them are debatable

Enter Alex Lieberman. Alex is the founder of 10X Labs, which is a company that helps transform existing companies into AI-native companies

And before that he was a founder at Morning Brew

As you might imagine, that experience at Morning Brew means that he is often a great source of content

and the post that inspired this episode is actually a direct crib from his X account recently, 30 Features of an AI Native Company. What I thought would be fun, after turning it into a beautiful 1950s retrofuturist-themed presentation with the help of Codex and GPT Image 2, is to go through these features one by one where I will share the [00:03:00] aspect of AI nativeness that Alex posted about, and then add any thoughts, qualifications, disagreements, although I don't think that I necessarily disagree in a lot of places, and other observations that I've seen in my work with enterprises as well

Now, I should note that I don't think that this is in any particular order

In fact, you can very much tell that this is not AI-generated, as it reads much more like a stream of consciousness than frankly most of the content that you're usedto seeing these days, which is, kind of a breath of fresh air

First feature of an AI native company

is to blueprint every process

To create, in Alex's words, a function-by-function process blueprint of the entire business Now I said I wasn't gonna disagree much, and I'm not exactly going to disagree here But this is one area where although I don't disagree with doing this, I think the reasoning behind it for a lot of companies is actually leading them down the wrong path

some of the reasons that it's valuable to blueprint processes

In other words, to map out how work actually gets done, is that a lot of that information right now lives locked [00:04:00] inside people's heads there are a lot of nuances and edge cases that people have been handling on their own forever and which are perhaps transmitted person to person through random spoken meetings in the hall or side chats on Slack that don't ever find their way into actual operating manuals

That puts the AI and agents that you're bringing in to assist with the work at a disadvantage Because they're recreating things from the ground up

And so having better maps of how work currently gets done is an incredibly valuable piece of context as you redesign your organization around AI and agents. So all that part I agree with. Where I get concerned

is around an inherent assumption of process mapping that I see pretty often. The assumption is that agents are going to do things the same way that humans do

I think that that's very unlikely to be true

And in fact, I think artificially constraining agents to do things along the pattern of an old workflow is in many cases the wrong approach. As opposed to, for example, giving them the goal and articulating the guardrails of what they can and can't do, [00:05:00] and letting them figure it out from there.

now again, that doesn't make process mapping not valuable. but we have to understand and prepare for The reality that the best way to do something in the future will not necessarily just look like an efficient version of the way that we did it in the past

Feature number two of an AI-native company is highly uncontroversial And that is to give everyone a daily driver, i.e., everyone in the organization gets to use a daily driver harness such as GrokBot, Claude Cowork, or ChatGPT@work

Now what's interesting here is that nine months ago, 10 months ago, when you saw the term daily driver, you would've assumed you meant just access to a frontier model.

In other words, people have access to ChatGPT or Claude

But what Alex is talking about is a specific work harness an environment in which models operate that is designed specifically for advanced knowledge work and coding, whether that's for software engineers or for non-software engineers who are now using code as part of the way that they do their job.

Getting comfortable with a harness means getting comfortable with context

it means being able to understand and organize skills

As well as understanding how to provision access to different tools Like I said, nothing [00:06:00] controversial here The one thing that I will note is that I think that we are going to increasingly see people rolling their own harnesses, often on the basis of an open source foundation. For example, DeepSeek's harness that just came out because they're gonna want peak flexibility and they're not gonna wanna deal with things Like investing in Cursor only to have it sold

And no longer being able to access certain models through it because of that sale.

feature feature number three is one that many in the comments noted is to use AI's favorite term right now, load bearing for the rest of the features

The idea is to build one intelligence layer to aggregate structure and unstructured data, documents, and business logic into a single source of truth that is queryable and that agentic work can bebuilt on top of

It is unquestionable that AI-native organizations

are going to get good at organizing the context their agents need to work

context management is and will continue to be a major discipline in this new org transformation period

To the extent that I have quibbles here, which is really just for the sake of interesting conversation

I kind of like the metaphor of a mesh or lattice rather than a single [00:07:00] layer

Because I think especially as you get into larger organizations Trying to have a single source of truth for everything rather than sources of truth that can interface with one another and that agents can traverse, perhaps even uncovering and trying to reconcile with human support differences in sources of truth is perhaps a more accurate reflection ofhow this is going to look with the biggest organizations.

But obviously the substantive point underneath remains



feature four of agent native organizations is one that has been a big subject of conversation on this show for the last few months, which is about using model routing to optimize cost per successful task across the business.

I think this is right, but it's not just a matter of task crowding

I think that task routing is part and parcel of an overall model architecture that is designed to be adaptable and flexible to different types of tasks. I think in some cases that will be reducible to using a router.

but in many cases will also implicate a larger architecture based around that idea of matching task difficulty to model capability



interest, number five is an interesting one in the way that he frames it

He says that AI-native [00:08:00] organizations will treat context as code. They'll keep architecture documents and conventions updated while making diligent upfront planning part of the operating discipline

In other words, they will treat context not just as the background info that is required, but as the actual foundations upon which agents are building. It's a subtle but important distinction, and I think reflects the idea that a lot of this AI is not just in operational process, but also comes down to mindset shifts as well

Speaking of, feature number six

is totally about mindset Be willing to throw away everything you've built every three months and reimagine the workflows from first principles. Now, I think throwing away everything you've built might be slightly dramatic, but it is absolutely the case

That we need to design these new systems for assumptions of change rather than assumptions of stasis. The The frequency with which systems will need to be updated, whether it's three months or six months or nine months or a year or a sort of perpetual update marked by bigger periodic reimaginings.

However it actually plays out, change is the name of the game, [00:09:00] and it's something that organizations are gonna have to get way, way more comfortable with than they are today

and that includes not getting attached to the exciting way that you figured out to do something just a couple of months ago because if the AI companies do their jobs well

advances should mean

That we have new ways to do things that are either easier or more powerful

Feature Feature number seven is about info sharing across the organization. Alex says that AI-native organizations will use a skills distribution system to manage agent behavior and improve token efficiency by triggering consistent skills throughout the workflow

In other words, organizations will distribute skills, not just prompts

I think this as well is emblematic of a bigger shift, which is the discipline of agent management coming to the fore

Skills are a key aspect of agentic systems

and so having ways to improve them, share them, access them, et cetera, across the organization, not just within the silo of any individual, is going to be increasingly important

important Feature number eight is about the changing relationship between technical and non-technical team members I think we're mostly past the days where people think that when we talk about [00:10:00] vibe coding or using code for knowledge work, we somehow mean that all of a sudden The folks in marketing and HR are gonna be the software developers instead of the existing engineers.

That's not what's happening. But what is happening is both that those knowledge workers are for the first time able to build things themselves as a way to help do their job. And second, they have more ability to contribute to product and engineering discussions than they might have in the past Feature eight of AI-native companies from Alex's list is to separate intent from implementation, to keep technical implementation separate from high-level specifications so non-technical staff can contribute in a format agents can turn into implementation plans.

play, I think one of the ways that this will play out is as we see more agents triggered from shared spaces

That's a natural place for some of this to happen. For example, when Claude Tag was announced, one of the more remarkable things about it was members of the Anthropic technical team saying that that was how they initiated a lot of their building now.

And by a lot, I mean the number that sticks out in my head is like 60% or something ridiculous like that. If agents are [00:11:00] being triggered from shared spaces, that creates more of an opportunity for different people to contribute to those conversations But that in and of itself is going to create a different type of burden and new types of system requirements, which is what Alex is talking about here

here Feature number nine is a cost efficiency feature where AI native companies will make cost per accepted pull request a key software metric and drive it down through better token efficiency. I think we are just at the beginning of the period of figuring out what the key metrics of agentic delivery are.

And what's clear to us at this point, 

is that whatever the metrics we land on are, it's likely that they include

some sense of completeness

As well as cost per completeness

in order to be able to better compare model harness combos in a more apples to apples kind of way

Every AI coding tool on the market does the same thing first. It starts writing code. Blitzy does the opposite. Before writing a single line, Blitzy spends days reverse engineering your entire code base

Thousands of agents ingest millions of lines, mapping every dependency, every [00:12:00] undocumented constraint, every architectural decision made over the last decade. 

the result is a dynamic knowledge graph that understands your software the way a principal engineer would after 30 years in the building

Other tools guess at context with grep searches and markdown files. Blitzy never guesses. it builds true understanding first, then delivers over 80% of entire software epics autonomously

validated end-to-end tested production grade pull requests. That's why Fortune 500 engineering teams trust Blitzy with the code bases that matter most

See for yourself at blitzy.com. That's B-L-I-T-Z-Y.com



Here's a harsh truth. Your company is probably spending thousands or millions of dollars on AI tools that are being massively underutilized. Half of companies have AI tools, but only 12% use them for business value. Most employees arestill using ai. To summarize meeting notes, if you're the one responsible for AI adoption at your company, you need section.

Section is a platform that helps you manage AI transformation across your entire organization.

It coaches, employees on real use cases

tracks who's using AI for business impact and shows you exactly where [00:13:00] AI is and isn't creating value.

The result, You go from rolling out tools to driving measurable AI value. Your employees move from meeting summaries to solving actual business problems, and you can prove the ROI. Stop guessing if your AI investment is working. Check out section@sectionai.com.

That's S-E-C-T-I-O-N ai com. 

At this point, it's no longer a question of whether companies are actively using AI

Using it well, on the other hand, is a whole different story. Robots and Pencils, though, is a company that I can point to that is actually built for this time. They're an applied AI engineering firm working directly with clients on problems that matter to the business, not experiments that live in a slide deck.

Every engagement starts by working backwards from the outcome a client actually needs. If you're trying to tell real AI engineering apart from noise in this space that's the difference maker. Head to robotsandpencils.com



the AI... This episode of the AI Daily Brief is brought to you by Hyperagent, where you run fleets of agents your team can manage together.

Forget local agents and chat workflows waiting on your laptop to be [00:14:00] prompted. deploys always-on agents in the cloud doing real work across the tools your team already uses

marketing agents turn competitor moves into landing pages. Sales agents enrich leads, draft emails, and updates the CRM. Ops agent chases the paperwork and tracks the budget. Every agent has access to shared context and follows your rules about scope and approvals

It's time you had agents that feel like teammates Hire yours at Hyperagent. Get $100 in credits at hyperagent.com/aidailybrief feature 10 is perhaps one of the features that many organizations are thefarthest along with, which is to build agent native development systems, letting fleets of coding agents plan, write, test, review, and ship code while humans define intent and acceptance This is a lot less controversial than it would have been a year ago, but obviously there are still many organizations that are using a traditional process

Despite there likely being an inevitable shift that will happen in the coming years

Feature 11 once again gets at the subtheme of token efficiency

dividing the business [00:15:00] of work into planning phases and execution phases, using higher effort models for heavy planning and then executing with cheaper and faster models

we-- The good thing will be that if we've done our job with feature number four about designing efficient token architectures

type, this is the type of division of labor that should naturally fall out of those systems.

Feature Feature number 12 feels at first glance fairly term heavy

It's use CLI tools to parse metadata in markdown files and traverse dependency relationships, allowing agents to be precise about input token usage. but really this is a technically specific way

of designing a more token-efficient system

is ar- basically what Alex is arguing for is organizing a company's knowledge in a way,that agents can only load the slice they need rather than the whole thing.

it feels related to the idea of progressive disclosure, which is an information architecture pattern where complexity is unpacked gradually and in sequence

in order to not create too much context overhead when an agent is working. Now, in addition to just making agents work better, there are obviously cost dimensions of that as well

[00:16:00] And why I'm glad Alex included it is that so far we've been operating at a really high level, but this is an example of where you start to get granular and actually do things differently

i.e., making sure that the metadata that an agent can reference to understand whether something is useful is right there at the top of knowledge files that it has access to so that it doesn't waste context window on things it doesn't need

With feature number 13, we're starting to get into specific parts of the organization.

he suggests that finance will run more continuously, moving accounting and record-keeping towards continuous processes, resetting forecasts on a much tighter cadence

OpenAI CFO Sarah Friar actually recently wrote about how she had done this inside OpenAI and about what a mindset shift and a technological discipline it took to make this sort of change



als-- Alex also suggests that in AI native organizations, other parts of the organization, and specifically the larger agentic operating system through which it runs, will have access to those financial models so that that can be part of the logic as strategy and tactics are designed

AI native company feature 15 is the citizen developer's [00:17:00] SDLC It's an approach that Alex has talked about elsewhere as well that enables non-technical employees to take a solution that they are building with coding tools from idea to production with the company's governance, access, versioning, and software conventions built into it.

It's basically a process of reconciling the things that the non-engineers are making with the way that engineers build. again, not with the idea of replacing software engineering in any way, shape, or form.

but in order to have the new things that people are building for themselves or their teams, or even some segment of customers based on the part of the customer life cycle that they touch, to have that all contiguous with the engineering organization it reflects again that shifting relationship between different parts of the organization in this new AI native space.



en- feature 16 gets to the sort of loop engineering that we covered in the webinar that Ishared on the show earlier this week. Make non-engineering workflows self-improving by learning from previous runs through external performance metrics and internal evaluations

The idea of loops is that instead of prompting agents, we give them a goal and bumpers around what they can [00:18:00] do, and design a process that they can loop through over and over again until they achieve that goal.

One of the necessary requirements of a loop

is some verifiable success metric that is objective rather than subjective

i.e., I need to achieve an X percentage result on this test is a lot more definable an outcome goal

than is our interface needs to look good

AI native organizations are going to be good at creating those sort of clear metrics of success, not just for the easy deterministic tasks, but for the broader array of knowledge work tasks that don't necessarily have that sort of success criteria built in natively

Feature Feature 17 is really two parts. one is to use some AI ROI framework to have an idea of what the organization is looking for out of its AI efforts, and to be able to measure against that. a l- the second part is a little bit more opinionated from Alex about the way to set that up

With his recommendation being experimental scaling and optimization phases,

and bets placed across infrastructure, innovation, and efficiency. whatever the phases that you end up using and the way that you organize different types of [00:19:00] efforts

I think the big recommendation here is to have a complex ROI architecture that can understand the goal of different efforts as being different from one another. but the organization having the ability to judgethem even if they are different all within the same framework

Feature 18 is my Doctor Strange theory of agentic work come to life

The idea is that AI native organizations will use agent swarms to deploy many, many, many, perhaps hundreds, perhaps thousands of paid marketing creative variations for testing before increasing spend on ads

one of the things that I underestimated when I was first thinking about that, which by the way I still think is completely inevitable, was the way in which compute constraints in the short term would limit the viability of that sort of approach.

now that we've crossed this capability threshold

where many, many models are good enough right now to actually do this sort of creative work and will be vanishingly cheaper than they are right now six months from now. I think that's when you'll start to see this sort of experimentation become a little bit more normalized, and I think it's going to be super, super interesting to see

Fascinatingly, and it's way beyond the scope of this particular conversation, [00:20:00] I almost see a marketing barbell where you are going to have just Doctor Strange crazy agentic swarms on one end of the spectrum and utter number denying human taste for brand campaigns on the other

Basically Rick Rubin on one side and Machines on the other

And somehow it'll work

Feature Feature 19 is another specific marketing recommendation of auditing, rewriting, and generating SEO and AEO optimized articles every week, then measuring to see whether any of it worked. I think the broader idea is that there is just so much interesting room for experimentation with content-based strategies now that the cost of producing content has gone down

Like so many of these recommendations, the idea here is that the AI native organization is not just going to do the thing, but to build learning systems around the thing to do it even better in the future

With With Feature 20, we're getting into cybersecurity, something that has obviously proven itself to be extraordinarily important over the past several months

Alex suggests that AI native organizations are going to fight AI with AI using agentic cybersecurity systems built to defend [00:21:00] the organization against AI powered threats.

Now, I think this is absolutely true, but boy, is there a lot to figure out about exactly what type of cybersecurity capabilities organizations and legitimate defenders are going to have access to and how that's going to be provisioned These are going to be some of the most important design and policy questions for AI companies and governments in the immediate term now that we've crossed some of these critical cyber thresholds



fi- we're in our final third, and I'llpick up the speed a little bit from here.



Feature 21 is another approach to token efficiency and cost management

Combining a reinforcement learning gym with first-party data to fine-tune open source models for high volume processes that needs state-of-the-art performance at reasonable cost basically we now live in a world where the prevalence of customizable and post-trainable open weights models that are very near the frontier opens up a lot of new opportunities for AI native organizations, many of which will find that this sort of model discipline is actually going to be useful for them.

still I would say that I don't believe that every organization is all of a sudden going to be rolling their own models. I think that there are going to be lots and lots of ways that all the labs and hyperscalers try [00:22:00] to deal with cost efficiency. but to the extent that you're an organization that actually has this technical capability, it's definitely a place that you could be exploring to get an edge right now



Feature Feature 22 we'll call the human sandwich, keeping human touch and judgment at the first and final mile of most processes

argument, if the argument is that even in AI-native organizations there should always be people on eithersides of the work sandwich, that I agree wholeheartedly with. sure, what I'm less sure is where we'll find the right intervention points are for humans in the middle of processes.

Yes, there will be many where it pretty much all happens agentically, but I don't think that we know exactly the right patterns. so to speak of this one too generally

feels harder for me to have a lot of confidence around

around Feature Feature number 23 I think is a little bit less arguable, which is the idea of making eval's core infrastructure. Now, the specific example Alex gives is whenever new models arrive, use a standing apparatus to test their cost and performance against the company's core processes. But I just think everyone is now in the eval business more generally.

Again, going back to the entire architecture of looping instead of [00:23:00] prompting, you have to basically build in evaluation against,some standard, or else the loops don't work

Thinking in terms of evals is just a new part of the discipline, and it's going to get to some weird parts of the organization that you wouldn't necessarily expect

Feature Feature 24, again, totally unarguable to me is that everyone is a builder and that AI native organizations will make building parts of every role, including and especially perhaps those held by C-level executives

I am not sure that every single person and every single knowledge worker goes from doing their work to managing agents that do their work I think that'll be a big chunk of work

But again, saying all of it is a pretty big swath to cover. What I feel much more confident saying is that the capacity to build, to use code, to develop prototypes, to develop products, to do your work

is now a critical capability. thing. Now, of course, over time, the space between people and the code that they use will get farther and farther as products obscure and abstract the technical details away.

But that won't make people less of builders if they can still build things to solve their problems [00:24:00] and create new opportunities. practical, this one is both practical action and mindset shift, and has some pretty big implications for how enterprises even organize themselves.

Feature Feature 25 is kind of the twin of feature one. Record everything worth learning from, because what the organization does not capture cannot be turned into AI-enabled work. This also gets at evals, context transmission. we just need to live in a paradigm of capturing a lot more

much to the delight I assume of the 75 meeting note takers that show up in every Zoom call you have now

now Feature Feature 26 I love because people don't talk about this enough. Governance is so frequently seen as a blocker of innovation, but AI native organizations are going to treat governance as a transformation partner. They are going to treat it, in other words, as a way to unlock innovation. That's only going to work if you have legal, HR, and IT work in lockstep with the owners of the AI agenda

Helping to design the enabling policies that address issues while unlocking new types of work This I think in many ways will be a key hallmark of [00:25:00] truly great AI-native organizations versus those that are still kind of bolting AI onto old ways of working.

working.uh, Feature 27 harkens back to the idea of constant transformation

AI-native organizations will maintain a bias towards disrupting the company before someone else does it for you

This extends beyond just how you do your current work, but cuts all the way to what work you should be doing. I think a lot of the big transformation of AI is not just going to be in the efficiency with which you do today's tasks, but finding those sort of orthogonal and aligned opportunities that you might not have gotten into yet, but which AI now enables you to go do



Feature Feature 28 iskind of the technical twin to governance as unlock instead of governance as blocker, and you could sum up as guardrails before features. Agents inherit the permissions of whoever is asking, and those permissions are enforced in the data layer.

you build the guardrails in, you don't have to relitigate it every time.

Feature Feature 29 could be an entire show on its own, and it's the idea that autonomy must be earned

these sophisticated powerful agents are not given full autonomy right away, but climb a ladder

[00:26:00] Observation, suggestion, acting with approval, acting alone, until they can run whole workflows inside a defined boundary Even if we're trying to identify everything, we don't have to identify everything all at once

and the old adage about an ounce of prevention being worth a pound of cure, Ithink is very applicable here

here Feature Feature 30 once again is about the information system that surrounds everything that you do in this new agentic era, tracing every output to its prompt model data and approver so human feedback attaches to something specific, not to a vague sense that something is off

We have the ability to create much more comprehensive and complex systems that surround our work. And if we do so, it comes with all sorts of benefits that improve the next work to be done after that that So that is Alex's list With a lot of great food for thought in there

When it comes to what's missing, one of the best answers I saw in the comments

Came from a number of people, but was summed up by Binti Jamil, who writes, " Clear ownership and accountability. AI can automate a lot, but someone still needs to own the outcome. Every AI workflow should have a clear owner, measurable goal, and someone responsible when things go wrong. n- the best AI native companies won't just ask, 'Can AI do [00:27:00] this?'

They'll also ask, 'Who owns the result?'" This, my friends, is nothing short of a new management discipline, and it's a management discipline that applies not just to the current managers, but to everyone, because everyone is becoming a manager of agents as well as an implementer of their own work.

For that reason, a lot of thisis going to have to be pressure tested in practice, learned through experience and failure

and then summed up as collective wisdom that we can all share. hopefully there is some good food for thought in here about how you help your company become more AI native. For now, though, that is gonna do it for today's AI Daily Brief.

Appreciate you listening or watching as always, and until next time, peace. 

​