The Week the AI Story Shifted

Today on the AI Daily Brief, we're discussing a week in which the AI story shifted or at least started to fork. The

AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. Now, last week I told you about an experiment that I was going to be trying where if Friday happened to be a comparatively slow day in AI,

nothing is actually slow, I was going to start experimenting with some sort of weekly recap. The goal of the weekly

weekly recap. The goal of the weekly recap is not just to rehash the same stories we talked about, but to put them in an overarching context that helps you understand in just 20 or 25 minutes what

the big point of that week was. For

people who aren't able to listen as much, it's a way to in a single episode have the broad brushstrokes of what happened. And for folks who are daily

happened. And for folks who are daily listeners, it's a chance to reinforce the themes that you've been hearing all week. Now, I was very positively pleased

week. Now, I was very positively pleased with the response. A lot of you provided great feedback and the numbers also suggest that this is a valuable type of episode to at least consider. I'm not

sure that it'll be every week and I think probably on some weeks I will need to use the open slot on Saturday for this given that there will often be news that we need to cover in a normal form.

But for now, we're going to do another weekly recap. And if last week was the

weekly recap. And if last week was the week that AI grew up with the thesis of that episode being that we were starting to see a real maturation of the way that people were engaging with AI on a usage

basis in markets and more. This is

almost a part two in some ways where that new maturity started to diffuse into the stories that we were telling about AI as well as the type of product priorities we have in the launches. The

week kicked off on Sunday with this opinion post from Ezra Klene about why the AI job apocalypse probably won't happen at least in the way that the most fearful folks have been suggesting for

some time now. The main inspiration for Ezra in that post was the Alex Ess essay what will be scarce that we read a few weeks ago on long read Sunday. Now, Alex

is a Chicago Booth economist, and the point that he's making in this piece is that when one sector of the economy gets disrupted, new surplus usually flows somewhere. It doesn't just dissipate.

somewhere. It doesn't just dissipate.

The big thing that Alex focuses on in his piece is the idea of the relational sector, where the value of a good or a service that we're consuming is not just based on the good or service itself, but

in its particular mode of creation or transmission. In other words, where it

transmission. In other words, where it matters who made the thing or who's providing the service in what way. Alex

argues that the relational sector definitionally will not be affected by AI in the same way as other parts of the economy and will indeed be the recipient and will see a proportional increase in

its demand. Now, as an aside, if you're

its demand. Now, as an aside, if you're interested in blowing out this argument even more, you are going to want to come back for LRS. It is my most fullthroated exploration ever of not only why I don't think there's going to be a job

apocalypse, but the specific shape and texture of the type of jobs that I think are going to be created in the wake of AI. So, come look back for that. But the

AI. So, come look back for that. But the

point coming back to Ezra Klene is that Alex Emas's essay was not just floating around Twitter, but actually found its way to mainstream discourse. And

frankly, mainstream discourse that was not of the group that has been predisposed to actually being sympathetic to AI or tech in general.

Ezra Klein is someone who had AI Doomer and Chief Eleazer Yudkowski on his show last October. In February, he

last October. In February, he interviewed anthropics Jack Clark in a show that he titled, "How fast Will AI agents Rip Through the Economy?" Which

is what I mean when I say that this essay represents a shift in the story and a shift in the vibe. Now, a much more inside baseball type of source exploring similar themes came from

A16Z's David. And why I say of course

A16Z's David. And why I say of course that this one is much more inside is that A16Z chief Mark Andre has been one of the loudest and most vocal AI accelerationists. And so it's not

accelerationists. And so it's not surprising to see this particular essay titled the AI job apocalypse is a complete fantasy coming from someone in his organization. Still, what David

his organization. Still, what David provides in the piece and why it got resonance beyond just died in the wool accelerationists is that in addition to a lot of opinion, there's also a lot of data in here. One of the charts he

shows, which if you're listening is very worth going and looking at, is a chart of US employment by sector since 1850.

It shows the decrease in agriculture employment from just under 70% back in 1850 down to less than 5% today. And

more than that, it shows that since 1950, while a couple of areas, most notably manufacturing and construction have gone consistently down, the real story is a diversification of the labor market into lots of different sectors.

leisure and hospitality, private education and health, professional and business services, a lot of things that didn't really or barely existed back in the middle of last century. He also

shows a bunch of Javvon's paradoxy type of charts where a thing that you think would have a negative impact actually had a very similar and opposite positive impact. More productive farming, for

impact. More productive farming, for example, he pointed out, didn't lead to more farmers, but it did lead to more workers as the world was able to take advantage of more productive farming to get a population boom as the world could

simply support more people. Another

example he points to is the shift in the number of bookkeepers and accounting clerks in the wake of the introduction of the spreadsheet. While those two particular jobs would see a steady decline for the next 30 years, other related areas that were enabled by

spreadsheets took off, including financial analysts and accountants and auditors. He also points out that

auditors. He also points out that productivity gains don't just make existing services cheaper and more accessible to a different set of people, but also that they lead to entirely new categories of services that take

advantage of that labor surplus. nail

salons, pet care, exam prep and tutoring, and athletes, coaches, umpires, and related work all had less than 100,000 workers in 1990, and now each have between 150 and 350,000.

He points to the charts that we've started to see that suggest that the demand for software engineers is rising and I think in an important chart notes that in mentions of AI workforce impact on public market earnings calls,

augmentation outment substitution by a ratio of 8:1. Now, the real exciting thing to me and the thing that I will be exploring in the LRS episode I was just mentioning are the jobs that don't exist yet that get enabled by these changes.

But you can see why people latched on to David's piece and why it came at a perfect time following Ezra's piece and Alex's piece before that. Now, what's

interesting is that one of the things that I think is happening is not just blind optimism and all of a sudden people who weren't excited about AI before being excited about it now, but a maturation in our understanding of how

the diffusion is actually likely to take place. I think there are a couple parts

place. I think there are a couple parts of that. Another big story from the

of that. Another big story from the beginning of the week was that both Anthropic and OpenAI were launching massive joint ventures to deliver and deploy enterprise AI services. And when

I say massive, I'm talking about a $10 billion starting valuation and a $4 billion investment for OpenAI and a $ 1.5 billion investment from Anthropic.

With each of those endeavors bringing in just a who's who of financial and operational partners like Blackstone, Goldman Sachs, and more. These are

companies whose fiercest battle is to be oneupping one another and pushing the pace of innovation ever forward. And yet

they are taking time to frankly distract themselves on painful, boring day-to-day deployment issues because that's what it's going to take for even incredibly powerful technology or perhaps especially incredible powerful

technology to diffuse and actually have the impact that it could in the workplace. This maturation in our

workplace. This maturation in our understanding of just how hard it is going to be to actually close the capability overhang and help AI do what it can do inside the enterprise is I

think shifting people's timelines. And

when it comes to the pain of disruption, timelines really matter. The world looks a lot different if there's a decade or two to adapt to changes as opposed to a year or two. When you're talking about a

decade, things that wouldn't be possible in a year or two, like actual reskilling and redesign of different types of work and roles, start to look a lot more viable. Now, on top of the news that the

viable. Now, on top of the news that the biggest labs were willing to distract themselves with painful enterprise deployment issues, the other thing that I think has helped shifted people's narratives, which was the main subject

of the weekly show last week, is the loss of the fantasy that somehow human level AI is going to be massively cheaper than humans, at least in the short term. Last week was all about the

short term. Last week was all about the shift in business model to usage based instead of seatbased which is a recognition that we are dealing with a world where there are far fewer tokens available than we would ideally consume

which of course brings us to the second area and place in which the AI story is shifting which is on Wall Street. Now

this started last week and has really continued this week where we are seeing both talk and behavior suggesting that Wall Street is not treating AI as a bubble that is inevitably going to pop as it might have been last fall. This

week, both JP Morgan CEO Jaime Diamond and Black Rockck CEO Larry Frink made comments affirming that the AI boom is real. Jaime Diamond said that he

real. Jaime Diamond said that he believed the trillion dollar investment in data centers will make sense. His

words. And Larry Frink went even farther saying, "Not only is there not an AI bubble, but quote, there is the opposite. We have supply shortages.

opposite. We have supply shortages.

Demand is growing much faster than anyone has anticipated. We have not begun exploring the opportunities of AI around the world."

Now, it's one thing for Wall Street leaders to say this, but markets are going to do what markets are going to do. And this week, they seem to be on

do. And this week, they seem to be on the boom train in a big way. Now, last

month, we got the announcement of a 5 gawatt deal between Anthropic and Google. We also knew that that deal had

Google. We also knew that that deal had contributed to the $462 billion backlog for Google Cloud that was announced in last week's earnings. The information

this week put a number on the amount contributed by that deal, however, reporting that it was worth 200 billion over 5 years. Now given that Google has also made an investment of up to 40 billion in anthropic, this brought up

for some of course the old arguments about whether this is all circular funding. And yet reaction to the Google

funding. And yet reaction to the Google Anthropic deal has been very different than the last time these circular narratives were discussed. Google was

already up 10% after the giant backlog was announced during earnings and spiked another 1.5% during the overnight session after the $200 billion number was reported. And in the back half of

was reported. And in the back half of the week, Google not only didn't give up these gains, but firmed it up, adding another 0.5% as of the Thursday close.

In other words, there doesn't appear to be any sign of the market worrying about Anthropic's ability to pay. Analysts see

the strong and growing revenue allowing them to meet massive new commitments, and all of a sudden, these numbers that seemed just so extraordinary before don't seem so out of reach now. Now, I

wonder how much of this is also driven by the fact that of the big frontier labs, Anthropic was comparatively conservative in their infrastructure deals and are paying the price now, having to race to catch up with better

resource competitors in the form of open AI. Signal writes, "Just around 6 months

AI. Signal writes, "Just around 6 months ago, many people thought everything was a bubble. Too much compute, too much

a bubble. Too much compute, too much capex, and demand that couldn't possibly absorb the bailout. But it turns out the ceiling on demand for intelligence is literally nowhere in sight." Carmen Lee wrote, "Everyone's worried about a

compute overbuild, but it's actually really hard to overbuild compute.

Capital is the easy part. Money shows up fast, but money does not equal compute.

You need GPUs, power, substations, colo, cooling, and operators. Each link has its own lead time. A capital bubble is a financing phenomenon. A comput bubble

financing phenomenon. A comput bubble requires every physical bottleneck to clear at once.

Now, the best place that I can show where this understanding of this particular story really came to bear this week is of course what will for sure go down as the biggest story of the week, which is the SpaceX team up with

Anthropic. On Wednesday, Anthropic and

Anthropic. On Wednesday, Anthropic and Elon Musk announced a new partnership where Anthropic will basically take over the entire capacity of the Colossus 1 data center. I talked about this

data center. I talked about this extensively in yesterday's episode, but my thinking about this comes in a couple different parts. First of all, I think

different parts. First of all, I think it just makes very obvious sense on an operational business level. XAI, now

part of SpaceX, has struggled to produce a model that can keep up with the leaders, but has not so quietly built incredible capacity in compute and seemingly the ability to add more.

Indeed, one of the things that we got with this anthropic announcement was Anthropic throwing their weight behind the idea that data centers in space might not just be an Elon fever dream.

So XAI now a part of SpaceX has a bunch of compute but no great models while Enthropic of course has great models and real challenges with access to compute.

On that level the partnership makes complete sense. Why this is so exemplary

complete sense. Why this is so exemplary though of the AI story shifting is mostly in the embodiment of Elon Musk himself being willing to shift his story visa vi. While of course he has not

visa vi. While of course he has not given up on Grock and is continuing to train future models in Colossus 2 with it. The fact that XAI is ceasing to

it. The fact that XAI is ceasing to exist as a separate company and is being completely folded into SpaceX, plus these big moves, suggests that Elon has gotten comfortable with the idea that his part of the story and his way to

shape the future of the AI race might be more in infrastructure than in model development. To put a fine point on

development. To put a fine point on that, in follow-up reporting to the deal, the New York Times discussed Terraab, which is Elon's chip manufacturing project in Texas. The new

information comes from a legal filing in Grimes County where the project is based and says the project will cost at least 55 billion and possibly as much as 119 billion, which is way higher than the

previous estimates of 20 to 25 billion.

If completed, it will be by far the largest chip fab on the planet. Elon

first announced Terapab way back in March, and people mostly brushed it off.

Now, people paid a little bit more attention when they added Intel as a partner in April, but you still saw folks like Nvidia analyst Tay Kim having a bit of skepticism on TBPN. He said,

"I'm not that optimistic. I mean, it's so hard to build that. It's almost like cooking where it takes a lot of trial and error accumulated over decades. It's

not something you could just jump right in and do." And you're seeing this in the coverage now. The anthropic deal adds a new level of credibility to the project. back in March. Even if you did

project. back in March. Even if you did believe that Elon could make the world's largest fab, it certainly didn't seem like he was going to have demand to justify it from just Tesla and Optimus.

But now he has a basically unquenchable source of demand in the form of anthropic. And so what you're seeing is

anthropic. And so what you're seeing is not only Elon's story about himself shifting, but people remembering what was impressive about Elon in the first place. Peak Elon was scaling up Tesla

place. Peak Elon was scaling up Tesla production when he famously slept on the factory floor in 2018. And maybe the closest example we've had recently was him standing up the first Colossus data center in record time at the end of

2024. People are starting to remember

2024. People are starting to remember that if there's one person who can pull off an insane construction and supply chain project like Terrafab, it's probably Elon Musk. Now staying in infrastructure, at the end of the week,

Nvidia announced a new partnership with Corning Glass, which manufactures the fiber optics, which are the backbone of data center networking. Corning is close to a monopoly supplier for this type of glass, holding more than a 70% market

share. The new deal will see Corning

share. The new deal will see Corning build three new facilities in Texas and North Carolina, adding 3,000 manufacturing jobs. Nvidia CEO Jensen

manufacturing jobs. Nvidia CEO Jensen Hong said, "We're going through the single largest infrastructure buildout in human history. Artificial

intelligence is going to become fundamental infrastructure all over the world and surely here in the United States." He also used a language which

States." He also used a language which we're starting to see come up a little bit more when he said this is such an extraordinary opportunity because we can use these market dynamics to reinvest revitalize American manufacturing for

the first time in several generations.

And here's the point that I want to make in the context of the AI story shifting.

If you follow the logical chain from demand for tokens is insatiable, way more than our supply two, that demand represents a tiny sliver of overall

total demand because as we've discussed, a vanishingly small portion of the enterprise is actually fully onboarded into this agentic world. How much

compute are we going to demand when it's not 5 to 20% of enterprises that are wired up and using agents, which by the way, I'm being generous, but 60 or 70%.

The compute shortage starts to look monumental at that point. And of course, that is exactly what investors are playing out, which is why they're back on board and excited once again about this data center buildout. But the next

step after you recognize that the data center buildout that's been discussed so far is likely not a bubble in that it's going to have the token demand and as we discussed last week, the usagebased business model to justify that token

demand. Then you start to realize that

demand. Then you start to realize that all of these new jobs that are being created around the data center boom are maybe not just some super temporary 2 to 5year burst of construction activity. In

other words, it's one thing when you have a job shortage that you need to quickly fill because you got a half decade of buildout after which the buildout is done and the jobs boom is over. But that's not what we're talking

over. But that's not what we're talking about here. We are talking about a

about here. We are talking about a sustained likely decadesl long project to get access to the compute that we are going to need for the next phase of the global economy. And what that means is

global economy. And what that means is that that financial optimism that's showing up on Wall Street is going to pretty quickly diffuse into real enthusiasm in bluecollar sectors as the benefits of this buildout hit home in

the immediate term. Now, as much as the data center conversation is fraught in terms of politics and has real issues and very legitimate issues from local citizens who have questions about data centers in their community, you're

starting to see very clearly that when it comes to unions, especially constructions unions, not only are they fully on board, they are leading the charge in trying to align the differences between data centers and

communities so these projects can proceed. Craig Fuller tried to capture

proceed. Craig Fuller tried to capture this on Twitter, writing, "AI is driving an American manufacturing renaissance and will continue to do so in coming years." AI data center construction is

years." AI data center construction is the largest infrastructure investment in history. And most exciting, it's not

history. And most exciting, it's not coming from the federal government, but rather from private cash flushed enterprises. The AI buildout requires

enterprises. The AI buildout requires massive capital spending, but not just on chips, on construction, power generation, equipment, etc. It's all infrastructure. AI data centers are

infrastructure. AI data centers are massive. A 500 megawatt data center is

massive. A 500 megawatt data center is the size of a midsize city airport and requires substantial concrete, steel, copper, fiber, piping, and huge cooling, transmission, and generators. It takes

30,000 truckloads to build one of these out. And that doesn't include the power

out. And that doesn't include the power plant that's required to run the thing.

That is a massive project in itself.

This will require massive capital investment for years to come. Thanks to

tax incentives, almost all of these materials are being constructed in the United States, originating in the old manufacturing heartland, the rust belt or south. Right now, I would say this

or south. Right now, I would say this view of the AI buildout as driving an American manufacturing renaissance is absolutely not dominant, but it is a bit

ascendant. And that's what I mean by the

ascendant. And that's what I mean by the story shifting. And when it comes to new

story shifting. And when it comes to new products announced this week, I think a lot of them fit within the themes that we discussed both last week and this week in terms of a maturation of the product set and a real focus on solving

the problems of these models in practice. In other words, there's a

practice. In other words, there's a reason that we are in the harness engineering era. The raw model

engineering era. The raw model capability overhang is so immense that we kind of need the harnesses. In other

words, the products that surround these models to solve some key problems or else that overhang is never going to get closed. This week at the code with cloud

closed. This week at the code with cloud event, we got features focused on memory, features focused on solving human review, and more infrastructure tools around multi-agent orchestration.

Cursor also added a skill around orchestration introducing /orgchestrate, which they call a skill that recursively spawns agents to tackle your most ambitious tasks with the cursor SDK. From OpenAI this week, the

cursor SDK. From OpenAI this week, the big set of product announcements was around voice. Sam Alman tweeted, "People

around voice. Sam Alman tweeted, "People are really starting to use voice to interact with AI, especially when they have a lot of context to dump. GPT

Realtime 2 comes to the API today, and it's a pretty big step forward. We

actually got three new voice models from OpenAI, all in the real-time API. GPT

Realtime 2, which is their voice agent model that can quote think harder, take action, handle interruptions, and keep conversations flowing. GPT Realtime

conversations flowing. GPT Realtime Translate, which is exactly like it sounds like, the ability to translate more than 70 input and 13 output languages. And GPT Realtime Whisper,

languages. And GPT Realtime Whisper, which is their streaming audio transcription. Now, I think the

transcription. Now, I think the operative phrase from Sam Alman here is a lot of context to dump. This is 100% why voice matters so much right now and why I think it fits in this broader

theme of just one by one going through the key issues of actually using agents in practice. One of the lessons that I

in practice. One of the lessons that I have in every self-directed education program we do is to have people set up whisper flow or some version of it so they can start to talk to their computer instead of just type. And the reason for

that is exactly what Sam says. You can

dump context so much faster when you're talking than when you're typing. And now

in the agent world, transmitting context from our brains alongside everywhere else into agents is kind of one of the key barriers to getting as much out of them as we could. Speaking of audio, 11

Labs also announced that it had reached a half billion in annualized revenue and added new investors including Nvidia, Black Rockck, Wellington, and Santander.

And one more fundraising announcement that I found extremely as of the moment, past AIDB partner and sponsor Blitzy raised a couple hundred million bucks at a $ 1.4 4 billion valuation. Becoming

the latest enterprise AI unicorn. Now,

I'm old enough to remember when becoming a unicorn was a really big deal. But at

this stage, it's almost more like, well, yeah, of course. It's just completely obvious that an enterprise facing agent harness company that actually does its harness engineering well should be a unicorn in the current environment.

Congrats to everyone at Blitzy, of course, and kudos to them for figuring out how important the harness was going to be before many others did. Now, as we round out, it's important to note that anytime we talk about narrative shifts,

it's very easy to take it too far. One

who wasn't looking for it could certainly see a lot of the same sort of discourse around AI this week that we've had in the past. Certainly, the best example of this was the response of mainstream media to layoffs at Coinbase and then later in the week layoffs at

Cloudflare. Both of those companies

Cloudflare. Both of those companies prominently pointed to AI as a culprit, which most media outlets were completely comfortable running without question.

And yet, more so than we've seen in the past, there were a fair number of people who jumped in to look at the specifics of the companies before just accepting the AI layoff narrative at face value.

Cloudflare, for example, was laying off 1,100 despite hiring 2,000 new people just a few months ago. Which of course makes the curious observer wonder if this wasn't more about correcting and overhiring. Meanwhile, I think Coinbase

overhiring. Meanwhile, I think Coinbase was even more transparent. The layoffs

were announced before the quarterly earnings report, whereas anyone who has paid even the tiniest little bit of attention to markets in crypto for the last 6 months could have guessed what we were going to hear. Sure enough, in the last quarter, Coinbase transaction

revenue fell 40% year-over-year, but as I said on Twitter, yeah, the layoffs were definitely about AI. The point is not to deny that AI had some role in these layoffs. We are in the midst of a

these layoffs. We are in the midst of a shift in recalibration and I think on average companies are going to be smaller in 5 years than they are today even if they're producing more. What's

different and where I think the story is shifting is with a bit of a rejection of the blind acceptance as AI is the default story behind any layoff that comes to the market's attention. Now, as

we wrap up, let's talk about one, what to watch for next week and two, what you should play with this weekend. For sure,

the big story that I am watching for next week is what the White House does, if anything, around this notion that they might be vetting AI models before they get released. It is very clear that there are very different sides battling

it out in this White House when it comes to what they should do around advanced AI models. At the beginning of the week,

AI models. At the beginning of the week, big outlets were reporting what would effectively amount to a reversal of position, having the White House be much more involved in AI than they had ever intended. But then by the end of the

intended. But then by the end of the week, we got this piece from Politico, suggesting that the White House was trying to distance itself from tighter AI regulation. One senior White House

AI regulation. One senior White House official told Politico, "There's one or two people who are very intent on government regulations, but they're sort of the minority of the bunch." So, next week, we'll be watching to see how this

lands. It's very clear at this point

lands. It's very clear at this point that it is not a nothing story and is frankly a very dynamic and fluid situation.

In terms of what to play around with this weekend, the answer is for sure/goal.

Investor, coder, lawyer, and researcher Dan Robinson, summed it up like this.

Codex just released the SLG goal feature. Tell Codex to set a goal, and

feature. Tell Codex to set a goal, and it will keep working on that goal until complete. Philip Corey, who works on

complete. Philip Corey, who works on Codex at OpenAI, called it our take on the Ralph loop. Keep a goal alive across turns. Don't stop until it's achieved.

turns. Don't stop until it's achieved.

A6Z's Andrew Chen writes, "Trying / goal for the first time on Codeex, and it's obvious it's going to 10,000x token use.

It's amazing though. I've had it working on a low-level eGPU plus Mac device driver project overnight that I have no business doing for the past 14 hours and it's still chipping away making progress with each iteration. Naturally

unattended 24/7 LLM use will be several magnitudes bigger than me prompting actively over a normal workday. Alex

Finn wrote, "You have to try the new/goal feature in Codeex. It worked

for over an hour and built me an entire complex extraction shooter video game.

You give it a goal, then it works endlessly until the goal is complete.

It's like a Ralph loop can run for days.

If you enable the image genen skill before you run the goal, it will even generate all the assets for your game autonomously. A couple days later, he

autonomously. A couple days later, he followed up, "The biggest advancement in AI coding this year has been /goal, and it isn't even close. It allows your AI agent to quite literally work for days

without stopping. You give it a mission,

without stopping. You give it a mission, it works until the mission is complete."

However, he says / goal is useless if you don't use it properly. You need a good prompt for it. I found basically any prompt I handr write after slash goal is never good enough. It produces

results that might as well have been a normal prompt. Meta prompting is the

normal prompt. Meta prompting is the answer. Go to any AI that has context

answer. Go to any AI that has context around the project you're working on.

Say, I'm working with Codeex and I want to use their new/Gal feature. Please

research their /Gal feature. Then take a look at our project and give me three options for how we could use / goal to be maximally productive. Then give me a highly detailed / goal prompt for each.

Take one of the prompts. Then go to the codec cli and type / gold and give the new prompt. I 100% guarantee the AI does

new prompt. I 100% guarantee the AI does better work than you've ever seen before. Now, for what it's worth, this

before. Now, for what it's worth, this is exactly what I did. I went to the new GPT 5.5 model, said, "Go research the new/Gole feature and tell me what types of projects it's well suited to, and then from there slowly whittleled it

down to the projects that I am working on across the AIDB suite." Deciding

ultimately to have it work on a forthcoming thing called AIDB for Teams that basically takes the daily episodes and turns them into chunked insights that are designed for actual knowledge workers inside companies that allow them

to get the highlights without having to consume the entire 20 or 30 minutes. To

give you a sense of how slashgoal fits, when I asked if that would be a good project for slashgoal, 55 responded, "Yes, this is a real slashgoal-shaped idea, but only after you separate two things. Building the system is a normal

things. Building the system is a normal codeex project, but running the system every day against the new episode can become a/goal project. The key is can the objective be made persistent, inspectable, and verifiable." And the

idea is that the daily episode translation is exactly that. It needs a common set of formats and outputs, strong consistent asset generation, and more. So, I will be doing that

more. So, I will be doing that experiment this weekend and I will be excited to hear if any of you do as well. For now though, that is going to

well. For now though, that is going to do it for today's AI daily brief. Again,

as always, if you have any feedback on this new weekly format, please let me know. For now, just a big thank you for

know. For now, just a big thank you for listening or watching. And until next time peace.