The AI Daily Brief: Artificial Intelligence News and Analysis - Skills for the Code AGI Era
Episode Date: January 25, 2026As coding agents and vibe-coding tools push software creation into a fundamentally new phase, the real question shifts from what AI can do to what skills actually matter. This episode unpacks the emer...ging divide between two critical roles in the code AGI era: the Agent Manager, who knows how to direct and scale AI agents effectively, and the Enterprise Operator, who knows what problems are worth solving and why. With execution becoming cheap and abundant, skills like systems thinking, async orchestration, domain expertise, problem recognition, and workflow redesign are becoming the true sources of leverage.Link: https://x.com/natolambert/status/2014023020302704698Brought to you by:KPMG – Discover how AI is transforming possibility into reality. Tune into the new KPMG 'You Can with AI' podcast and unlock insights that will inform smarter decisions inside your enterprise. Listen now and start shaping your future with every episode. https://www.kpmg.us/AIpodcastsZencoder - From vibe coding to AI-first engineering - http://zencoder.ai/zenflowOptimizely Opal - The agent orchestration platform build for marketers - https://www.optimizely.com/theaidailybriefAssemblyAI - The best way to build Voice AI apps - https://www.assemblyai.com/briefSection - Build an AI workforce at scale - https://www.sectionai.com/LandfallIP - AI to Navigate the Patent Process - https://landfallip.com/Robots & Pencils - Cloud-native AI solutions that power results https://robotsandpencils.com/The Agent Readiness Audit from Superintelligent - Go to https://besuper.ai/ to request your company's agent readiness score.The AI Daily Brief helps you understand the most important news and discussions in AI. Subscribe to the podcast version of The AI Daily Brief wherever you listen: https://pod.link/1680633614Interested in sponsoring the show? sponsors@aidailybrief.ai
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Today on the AI Daily Brief, the skills we need to develop for the Code H.E.I. Daily Brief is a daily
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let's dive in. Today we are talking about the skills necessary for the new Code AGI era.
Now, if you've been following along, you'll know that my sense is that we have made a fundamental
shift recently, that the combination of the set of models that were released at the end of last year,
Gemini 3, GPD 5.2, and especially Opus 4.5, in combination with tools like ClaudeCode
and the Vibe coding platforms like Replit and Lovable, have put us into a fundamentally new place
when it comes to AI. Someone who's been thinking about this a lot is Nathan Lambert. A couple of weeks
ago, he wrote an essay called Claude Code Hits Different. He writes, having used coding agents
extensively for the past six to nine months, there was some meaningful jump over the last few weeks.
He points to a tweet from Sergei Karayev that in his estimation captures the shift.
Sergei tweeted, Claude Code with Opus 4.5 is a watershed moment,
moving software creation from an artisanal craftsman activity to a true industrial process.
It's the Gutenberg Press, the sewing machine, the photo camera.
Nathan, for his part, writes,
The joy and excitement I feel when using this latest model in Claude Code is so simple
that it necessitates writing about.
It feels right in line with trying Chatcheebt for the first time,
or realizing O3 could find any information I was looking for, but in an entirely new direction.
This time, it is the commodification of building.
I type and outputs are constructed directly.
The fact that Claude code makes people want to go back to it is going to create new ways of working with these models,
and software engineering is going to look very different by the end of 2026.
Right now, Claude and other models can replicate the most use software fairly easily.
We're in a weird spot where I'd guess they can add features to fairly complex applications like Slack,
but there are a lot of hoops to jump through and landing the feature.
So the models are way easier to use when building from scratch than in production codebases.
This dynamic amplifies the transition and power shift of software,
where countless people who have never fully built something with code before can get more value out of it.
It will rebalance the software and tech industry to favor small organizations and startups,
like Nathan says his startup interconnects, that have flexibility and can build from scratch
in new repositories designed for AI agents.
It's an era to be first defined by bespoke software,
rather than a handful of mega products used across the world.
The list of what's commoditized is growing in scope and complexity fast.
Website front-ends, many applications on any platform, data analysis tools,
all without having to know how to write code.
I expect mental barriers people have about Claude's ability to handle complex codebases
to come crashing down throughout the year,
as more and more Claude-pilled engineers just tell their friends' skill issue.
There are things Claude can't do well and will take longer to solve,
but these are more like corner cases,
and for most people, immense value can be built around these blockers.
So that was his initial essay.
However, he's gone back to the well to get out what I think isn't even more important
questions with his most recent, which he called Get Good at Agents.
Earlier this week, I did a presentation for one of the world's largest asset managers.
It's a company that has tens of thousands of employees, tens of billions of revenue,
and trillions in assets under management.
I called the presentation AGI Incorporated, and the theme of it was trying to articulate
and ground this change that Nathan was writing about and that we've all been experiencing.
The big question that the leadership in the room had was what are the necessary skills for
this new shift? How much is it technical and how much is it something else? So what we're going to
do with the rest of this episode is read Nathan's latest essay, Get Good at Agents, and talk about
the skill shift that I feel is coming right now. Nathan is recognizing, I think, something that many
people are feeling, which is that without anyone asking, many of us are finding ourselves naturally
trying to adapt to the capabilities of agents rather than trying to adapt them to ourselves.
In his essay called Get Good at Agents, Nathan writes,
two weeks ago I wrote a review of how Claude Code is taking the AI world by storm,
saying that software engineering is going to look very different by the end of 2026.
That article captured the power of Clod as a tool on a product,
but it undersold the changes that are coming in how we use these products in careers that
interface with software.
The more personal angle was how I'd rather do my work if it fits the Claude Form Factor,
and soon I'll modify my approaches so that you'll modify my approaches,
so that Claude will be able to help. Since writing that, I'm stuck with a growing sense that
taking my approach to work from the last few years and applying it to working with agents
is fundamentally wrong. Today's habits in the age of agents would limit the uplift I'd get by
micromanaging them too much, tiring myself out, and setting the agents on too small of tasks.
What would be better is more open-ended, more ambitious, and more asynchronous. I don't know yet
what to prescribe myself, but I know the direction to go, and I know that searching is my job.
It seems like the direction will involve working less, spending more time cultivating peace,
so the brain can do its best directing. Let the agents do most of the hard work.
Since trying Claude Code with Opus 4.5, my work life has shifted closer to trying to adapt to a new way of working with agents.
This new style of work feels like a larger shift than the era of learning to work with chat-based AI
assistants. Chat GPT let me instantly get relevant information or a potential solution to the problems I was already working on.
Claude Code has me considering what should I work on now that I know I can have AI independently solve or implement many sub-components.
Every engineer needs to learn how to design systems.
Every researcher needs to learn how to run a lab.
Agents push the humans up the org chart.
I feel like I have an advantage by being early to this wave, but no longer feel like just working hard will be a lasting edge.
When I can have multiple agents working productively in parallel on my projects,
my role is shifting more to pointing the army rather than using the power tool.
Pointing the agents more effectively is far more useful than me spending a few more hours grinding on a problem.
The feeling that I can't shake is a deep urgency to move my agents from working on toy software to doing meaningful long-term tasks.
We know Claude can do hours, days, or weeks of fun work for us, but how do we stack these bricks into coherent long-term projects?
This is the crucial skill for the next era of work.
There are no hints or guides on working with agents at the frontier.
The only way is to play with them.
Instead of using them for cleanup, give them one of your hardest tasks, and see what it gets stature.
duck on. See what you can use it for. Software is becoming free. Good decision making in research,
design, and product has never been so valuable. Being good at using AI today is a better mode than
working hard. In Nathan's essay, we can clearly see him grappling with his own shift in how he works
and the new skill sets that feel proportionally more valuable. But I wanted to expand this and make it
more generalizable. I think many of us, in fact, basically everyone who's fully taking advantage of these
tools is going to have to check ourselves against this new set of skills that's required.
And so what are the actual skills? This is probably overly reductive. But let's break them into two
categories. The agent manager and the enterprise operator. The agent manager is all about knowing
how to work with agents effectively. The enterprise operator is about knowing what to work on and why.
The superpower is of course going to be for people who have both of these. Let's talk first about
the side that Nathan was exploring, the agent manager. The goal of course is to direct agent
for maximum output. Now, in many ways, software engineers are ahead of the curve on thinking about
this shift, moving from executor to director, from wielding the tool to pointing the army. It's more
about systems, about defining the parameters, about getting leverage via direction. Specifically,
some of the skills, many of these which show up in Nathan's piece, include systems design thinking,
i.e. thinking about how to architect coherent holes rather than simply implementing individual
components, task scoping, and specifically ambitious task scoping.
How to give agents meaningful end-to-end work, not just small cleanup tasks.
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We haven't done a full show on it, but if you've been hearing about Ralph Wiggum as an AI strategy,
it's kind of all about this.
It's about breaking a big task into a bunch of small tasks in a way that agents can work for much longer when you're not there.
And indeed, that gets into some of these other key skills,
long horizon projects where you stack short-term outputs into coherent, durable long-term projects
and asynchronous work management, where you figure out how to orchestrate work that runs in the
background without real-time monitoring. One of the sentiments that you'll hear right now,
which I personally feel kind of acutely, is a particular type of anxiety of not having deployed
agents to work on something in the background while you were doing some other type of work.
I just finished this presentation I mentioned before, and if I had done a little bit more pre-work,
I could have had agents building something while I was talking to this group of leaders.
There are also some other skills.
Prompt architecture is kind of a part of that task scoping and async work management.
Validating output at scale without having to review every line manually is going to be a whole new field and discipline.
And of course, there's multimodal orchestration where you need to know which AI tool or model to deploy for specific types of tasks.
But I think that really the big ones are about async work management and system design thinking so that you can effectively deploy not an agent, but an army of agents.
This is, however, only half of the skills for the AGI code era.
The other will call the enterprise operator,
and of course this doesn't have to mean large enterprises,
but it's about the business side.
When the group asked me if I thought that the skills for this new era
were primarily technical or about something else like domain expertise,
I said that for many of them,
it is going to be about a reapplication of some key operator skills
inside the enterprise right now.
The core mindset shift from this enterprise operator perspective
is that execution used to be expensive. It is now cheap, it is now abundant, anything that I think of
I can build, and I can do it pretty darn quickly. That means selection becomes the scarce resource,
knowing what to execute is the key thing. Opportunity recognition, strategic alignment,
and outcome definitions become the core parts of the enterprise operator. Let's expand the skill set
a little bit. One area which I really don't think we should overlook is domain expertise. If,
If 2025 has shown anything, it's that the pejoratively named AI Rapper startups actually
understood something significant, which is that different industries and different functions
have particular attributes, which require modification from the core interface of the chatbot.
And even if you are using the same model, knowing what sort of processes AI is going to intersect
with, knowing what types of data sources it's going to need to have access to, and building interfaces
around that type of domain expertise can be extremely valuable. One need only look at the value
of a company like Harvey or Open Enterprise to understand that.
Domain expertise is, in other words, extremely valuable,
even and especially in this world of Code AGI.
Having knowledge of the way that work happens in a particular domain,
be it a function or an industry,
understanding the problems and the constraints within that specific field,
which could be anything from governance to compliance regimes to data set challenges,
is going to be absolutely key and even more key in some ways than before.
when you are having to think in systems terms,
you need that wide-ranging view
that only domain experts are going to have.
Now, this actually brings up another challenge,
which is one that could get more apparent,
especially in the medium term,
which is that the more that current domain experts
use agents to do everything,
the less of a pipeline to expanding that domain expertise
to new people in the form of mentorship and junior employees,
the less they spend time distributing that domain expertise
to younger employees in the form of mentorship.
We can't take on every problem at once, so we'll skip that one for now, but it is something
that I think organizations will start to recognize. Okay, so you've got domain expertise,
but another key skill of the enterprise operator is problem recognition. And problem recognition
is not just in understanding where there are challenges or workflow frictions. It's being
able to reinterpret those problems as solvable software problems. This is in and of itself
a major mindset shift. I started vibe coding at the beginning of last year, as these tools
all came out and we started calling it vibe coding. I dabbled with it since the very beginning of
chat GPT, although it was a lot harder then. And yet it was only at the very end of last year that I
started finding myself actively asking when I came across any problem or challenge,
could I use software to solve this? That is going to be an entirely new muscle that enterprise
operators have to develop. And so problem recognition is actually a bunch of different things
at once. Enterprise operators also need to have AI possibility awareness. They need to understand
what is actually feasible to build with current agendas capabilities.
This is an entire discipline in of itself and why we have companies that are exclusively
focused on exactly this.
Related, of course, is the problem solution fit and being able to connect AI possibility
awareness with problem recognition.
A really big skill for the enterprise operator is unstated constraints.
Part of what makes applying AI to enterprises so challenging are these unstated constraints.
Think about institutional knowledge, compliance requirements, specific.
specific stakeholder dynamics. These are things that aren't necessarily written down anywhere.
Remember, people have been exploring this new concept of the context graph, which is all about
the why instead of the what. The context graph is not about the CRM entry that shows that we gave
a company a 20% discount, but an explanation of why we gave it a 20% discount when the stated
policy is to give no more than a 10% discount. Unstated constraints are another missing
set of information and missing set of context that lives inside the enterprise operator. In parallel to
the agent manager's output verification, there is a version of that for enterprise operators as well,
where these enterprise operators need to be able to recognize whether AI output is actually correct
within the context of the particular domain. This is, of course, going to be extremely important
if we want new processes to replace the old, which, by the way, is yet one more key skill of the
enterprise operator, which is process redesign. One of the soapboxy things that you sometimes
probably hear me talk about on the show is about why I think it's a very, and I'll generously
call it an intermediate strategy to try to have AI agents watch what humans do, document that
process so they can copy it. It is quite clear, I think, that agents are going to find different
and probably more efficient ways to do things than their human counterparts, and a key skill
of the enterprise operator is going to be rethinking entire workflows from scratch and letting
new workflows replace the old. Now, one thing that's on neither of these, but is maybe just an
overarching mindset shift, is moving from seeking perfection on the front side to iterating on the
backside. In other words, one of the implications of having the cost of execution come down is simply
that we can try more solutions that puts a premium on iteration and adaptive learning as opposed
to preparation and planning. It's not a strict one-to-one shift, as you see a lot of these skills are
about planning, but overall we're going to run processes and learn from our mistakes much more
quickly than we have in the past. We've talked a lot recently about the AI capability overhang,
the gap between what AI can do and what we're getting out of it. This gap is set to absolutely
explode in the Code AGI era. And to bring adoption and capability closer together, it is going to take
not just agent management skills and not just enterprise operator skills, but a combination of both.
If you are an individual who can do both of these things, you are simply put going to be the most
in-demand individual in the world. But if you were thinking about the system of your organization,
it's about how you allow all of your people to operate more in both of these ways. At some point,
we'll do a whole separate show about how I think organizations should be thinking about upskilling
in this particular era. But for now, hopefully this is a bit of a blueprint for thinking about
skills for the AGI code era in a different way. That's going to do it for today's AI Daily Brief.
Appreciate you listening or watching, as always. And until next time, peace.