The AI Daily Brief: Artificial Intelligence News and Analysis
Code AGI is Functional AGI (And It's Here)
18 Jan 2026
Chapter 1: What is the main topic discussed in this episode?
Today on the AI Daily Brief, why Code AGI is functional AGI and why functional AGI is here. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in. First of all, thank you to today's sponsors, Zencoder, Robots and Pencils, Section, and Superintelligent.
To get an ad-free version of the show, go to patreon.com slash AI Daily Brief. And if you are interested in sponsoring the show, send us a note at sponsors at ai-dailybrief.ai. So we are back now with another long read slash big think episode. And this week, we're getting into a topic that I have been kind of obsessing about for the last several weeks.
It feels to me quite clear that something dramatic has shifted. Obviously, I don't mean some new model that changes everything, but more, it feels as though we've digested what the latest round of models is actually capable of. We've had enough time with them for them to start to shift our behaviors.
And the implication of all of that is, fundamentally speaking, some different new era in the story of AI and more broadly in the story of work. It is a shift which I am still trying to figure out how to put words around, but one that I am convinced has profound implications for how companies do what they do.
To some extent, the shift is starting to come home to roost in a concerted conversation around whether we are finally at AGI. I will argue that we are with some nuance.
But what I'm going to do first is read some excerpts from a recent piece by Sequoia's Pat Grady called 2026, This is AGI, follow it up with a more skeptical piece by Every's Dan Shipper called Toward a Definition of AGI, and then I'm going to add my own thoughts, steelmanning both perspectives and trying to end with where I think is the most useful place to be. Let's start with Pat's piece.
It's actually by Pat Grady and Sonya Huang, and begins, Years ago, some leading researchers told us that their objective was AGI. Eager to hear a coherent definition, we naively asked, How do you define AGI? They paused, looked at each other tentatively, and then offered up what's become something of a mantra in the field of AI.
Well, we each kind of have our own definitions, but we'll know it when we see it. The vignette typifies our quest for a concrete definition of AGI. It has proven elusive. While the definition is elusive, the reality is not. AGI is here now. Coding agents are the first example. There are more on the way. Long horizon agents are functionally AGI, and 2026 will be their year.
Now in the next section, Pat and Sonja make sure to qualify that they do not have any sort of scientific authority to propose this definition. And yet, with that said, they offer what they call a functional definition of AGI. AGI, they write, is the ability to figure things out. That's it.
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Chapter 2: How have recent AI models changed our understanding of AGI?
So what's an example of this new capability that they're talking about? They provide an example of a founder telling his agent that he needs a developer relations lead. He gives a set of qualifications, including the fact that this person needs to enjoy being on Twitter. The agent starts in an obvious place. LinkedIn searches for developer advocate, for example.
Unfortunately, it finds hundreds of examples, so it has to iterate. It pivots they write to signal over credentials. It searches YouTube for conference talks. From there, it finds 50-plus speakers and filters for those with talks that have strong engagement. Next, because of that Twitter qualification, it cross-references those speakers with Twitter.
The total number is now whittled down to a dozen, with real followings and posting real opinions. Honing in even further for who's been most engaged in the last few months, that total list, which was hundreds and then 50 and then dozen, is now down to three. Now it can hone in on those three. One just announced a new role. One is the founder of a company that just raised funding.
The third was a senior dev rel at a Series D company that just did layoffs and marketing. The agent they write drafts an email acknowledging her recent talk, the overlap with the startup's ICP, and a specific note about the creative freedom a smaller team offers. It suggests a casual conversation, not a pitch. Total time, 31 minutes.
The founder has a shortlist of one instead of a JD posted to a job board. This, patents on your right, is what it means to figure things out. Navigating ambiguity to accomplish a goal, forming hypotheses, testing them, hitting dead ends, and pivoting until something clicks. The agent didn't follow a script.
It ran the same loop a great recruiter runs in their head, except it did it tirelessly in 31 minutes without being told how. To be clear, agents still fail. They hallucinate, lose context, and sometimes charge confidently down exactly the wrong path. But the trajectory is unmistakable, and the failures are increasingly fixable. So what?
Well, soon, they say, you'll be able to hire an agent, which, with a hat tip to Sarah Guo, they call one litmus test for AGI. You can hire GPT-5.2 or Claude or Grok or Gemini today. More examples are on the way. In medicine, open evidences deep consult functions as a specialist. In law, Harvey's agents function as an associate.
They go through examples in cybersecurity, DevOps, go-to-market, recruiting, math, semiconductor design, and AI research. All of this, they say, has profound implications for founders. The AI applications of 23 and 24 were talkers. Some were very sophisticated conversationalists, but their impact was limited. The AI applications of 26 and 27 will be doers. They will feel like colleagues.
Usage will go from a few times a day to all day every day, with multiple instances running in parallel. Users won't save a few hours here and there. They'll go from working as an IC to managing a team of agents. Remember all that talk of selling work? Now it's possible. What work can you accomplish?
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Chapter 3: What defines functional AGI according to Pat Grady and Sonya Huang?
Where sustained attention is the bottleneck? Saddle up, they say. It's time to ride the long horizon agent exponential. Today, your agents can probably work reliably for around 30 minutes, but they'll be able to perform a day's worth of work very soon and a century's worth of work eventually. Ultimately, they write, the ambitious version of your roadmap just became the realistic one.
Let's move over to Dan Shippers toward a definition of AGI. Dan writes, when an infant is born, they are completely dependent on their caregivers to survive. They can't eat, move, or play on their own. As they grow, they learn to tolerate increasingly longer separations. Gradually, the caregiver occasionally and intentionally fails to meet their needs.
The baby cries in their crib at night, but the parent waits to see if they'll self-soothe. The toddler wants attention, but the parent is on the phone. These small, manageable disappointments, what the psychologist D.W. Winnicott called good-enough parenting, teach the child that they can survive brief periods of independence.
Over months and years, these periods extend from seconds to minutes to hours, until eventually the child is able to function independently. AI is following the same pattern. Today, we treat AI like a static tool we pick up when needed and set aside when done. We turn it on for specific tasks, writing an email, analyzing data, answering questions, then close the tab.
But as these systems become more capable, we'll find ourselves returning to them more frequently, keeping sessions open longer and trusting them with more continuous workflows. We already are. So here's my definition of AGI. Artificial general intelligence is achieved when it makes economic sense to keep your agent running continuously.
In other words, we'll have AGI when we have persistent agents that continue thinking, learning, and acting autonomously between your interactions with them, like a human being does. I like this definition because it's empirically observable. Either people decide it's better to never turn off their agents or they don't.
It avoids the philosophical rigmarole inherent to trying to define what true general intelligence is. And it avoids the problems of the Turing test and OpenAI's definition of AGI. In the Turing test, a system is AGI when it can fool a human judge into thinking it's human. The problem with the Turing test is that it sets up movable goalposts.
If I interacted with GPT-4 10 years ago, I would have thought it was human. Today, I'd simply ask it to build a website for me from scratch and I'd instantly know it was not human. OpenAI's definition of AGI, which is AI that can outperform humans at most economically valuable work, suffers from the same problem. What constitutes economically valuable work constantly changes.
We will invent new economically valuable work that we can perform in conjunction with AI. These hybrid roles then become the new benchmark that AI will need to learn to do before it counts as AGI. So the definition is an ever-receding target.
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Chapter 4: What examples illustrate the capabilities of long horizon agents?
Dan isn't really disagreeing with that, although he doesn't get into timelines. Instead, he's pointing out all of these things that need to happen to get to a certain point of indispensability, which he is arguing is the key thing. But what about what we've seen over the last couple of weeks?
The sense among some of the most enfranchised and powerful users of AI that we really are in a fundamentally different moment. To take one example of a type of testimony we've seen lots of, Midjourney founder David S. Holtz tweeted on January 3rd, I've done more personal coding projects over Christmas break than I have in the last 10 years. It's crazy.
I can sense the limitations, but I know nothing is going to be the same anymore. And honestly, this brings up a more interesting and nuanced take on It's Not AGI Yet. That argument would go something like, yes, cloud code and similar tools have crossed the threshold for coding specifically, but generality is the whole point of general intelligence.
There's still so much that current AI fails at, like novel reasoning, multi-step planning in unfamiliar domains. These new big breakthroughs that everyone is sensing happened in a domain that's really well-suited to LLMs. Well-documented, pattern-rich, verifiable outputs. That's not, the argument would go, evidence of general intelligence. It's evidence of domain fit.
this would in some ways be an argument about the jaggedness of AI, the idea that it can be superhuman in one area and infantile in another. And indeed it is the case that this sense of what has shifted is about AI's capacity to code. But I keep coming back to this essay from Sean Wang, aka Swix, when he decided to join Cognition.
This line, which absolutely wins the award for the couple of sentences that have lived most rent-free in my head since they were written, Sean wrote, The central realization I had was this. Code AGI will be achieved in 20% of the time of full AGI and capture 80% of the value of AGI. Now for him, this was an argument to simply do code AI now rather than later.
But I think what I would argue is that code AGI doesn't quote unquote capture 80% of the value of AGI. I think code AGI is more or less just functional AGI. The argument here is that coding is effectively a universal lever in the modern world. Most economically valuable work, to reference OpenAI's terminology, has been computer-shaped for a long time.
If your job touches a screen, an API, a database, a spreadsheet, a ticketing system, a CRM, a repo, a dashboard, or a docs tool, then in principle it's addressable by software.
So if an AI can understand intent, translate intent into procedures, write and modify code, run tools, inspect outputs, and iterate until it meets acceptance criteria, then it has a meta skill that can simulate competence in many domains by building the missing tool. And in that framing, coding isn't one domain. It instead is closer to instrumental generality. Want data analysis?
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