THE SCARIEST CHART IN AI JUST GOT SCARIER

The scariest chart in AI just got scarier. Your job has a timer on it. And I don't say that to scare you. I say it because the data is sitting right in front of us.

And the people building this technology are no longer hiding what they see. Sam Altman just said out loud on stage this week, the world is not prepared. He runs OpenAI. He knows what's in the lab and he's scared.

So let me start with a chart, just a chart, no flashy but launch, no new company, no billion dollar funding round, just a chart that non-profit research group called METR quietly updated on February the 20th, 2026. And when they updated it, the AI world collectively held its breath because the new data point on the chart, one single dot is the most terrifying and the most exciting thing I've seen in years covering this space. The dot represents called Opus 4.6 and it sits at 14 and a half hours. I'm gonna explain what that means in a second.

And when I do, I need you to pay close attention because most people who share this chart online get it wrong, they misread it. And when you misread it, you actually undersell how scary it is. So let me walk you through exactly what this chart is, what it measures and why Claude Opus 4.6 is new number jarred researchers and lab leaders across the industry, what it means for you, your job, your business and the next few years of your life. Strap in.

So first of all, what is METR and why does this chart matter so much? So METR, it stands for Model Evaluation and Threat Research. They are a nonprofit. They don't work for OpenAI, they don't work for Anthropic, they don't work for Google.

They have no financial interest in making AI look better or worse than it is. Their whole job is to figure out how capable these AI models actually are. And a little over a year ago in March, 2025, METR published something that quietly changed the conversation in AI research circles. They published what they call the Time Horizon Chart.

Here is what the Time Horizon Chart actually measures. METR took hundreds of complex tasks, coding tasks, machine learning tasks, cybersecurity tasks, software engineering problems, and they handed those tasks to human experts, professional people who do this work for a living. And they measured how long it took each expert to complete each task. Then they handled and handed over those same tasks to AI agents.

And they measured whether the AI could complete the task successfully. The y-axis on the chart, the vertical line, shows the length of tasks in terms of how long a human expert would take to complete them. Here is where most people get confused. The chart is not measuring how long the AI takes to do the task.

Read that again. The chart is measuring how long it takes a human to do the task that the AI can now complete. This is a subtle but massive difference. The AI might finish in minutes what a human takes eight hours to do.

Or it might take all day. That's not what the chart is tracking. The chart is tracking the difficulty of the work measured in human effort. And the 50% time horizon, which is the main number people talk about, means at this task length, the AI succeeds about half the time.

So when METR says Claude Opus 4.6 has a 50% time horizon of 14 1⁄2 hours, what they're saying is give Opus Claude, sorry, give Opus tasks that would take a trained, experienced, professional human expert 14 1⁄2 hours to complete, nearly two full working days, and that AI will finish it successfully about half the time. That is extraordinary. That is not a chatbot answering a question. That is an AI agent completing two days of expert professional work from start to finish autonomously with no human guiding it step-by-step.

Half the time. And the reason everyone in the AI world is losing their minds, it's not just the number itself, it's the trend line behind it. So let's talk about the trend line that is breaking every prediction. Let me take you back through the progression of this chart because the speed of what has happened is almost impossible to believe unless you see it laid out.

In mid-2024, less than two years ago, frontier AI models like GPT-40 had time horizons measured in single digits, right? We're talking tasks that a human expert could complete in five or 10 minutes. So that was the ceiling. By early 2025, Claude 3.7 Sonnet hit roughly 59 minutes.

That felt like a big deal at the time. By December 2025, nine months later, Claude Opus 4.5 jumped to four hours and 49 minutes. The AI community freaked out. Articles were written, researchers issued statements, and then two months after that, on February the 20th of 2026, METR added Claude Opus 4.6, 14.5 hours.

In two months, the number nearly tripled. That jump from four hours and 49 minutes to 14 and a half hours is a 2.2x increase over the previous best model, which was GPT 5.2. Claude Opus 4.6 did not just beat the previous record, it nearly doubled it. Now here is the trend line underneath all of this.

METR originally found, when they published the chart in March 2025, that AI agent capabilities had been doubling roughly every seven months for the past six years. Every seven months, the AI could handle tasks twice as complex. That alone would have been shocking. Seven month doublings are fast.

When METR updated their methodology in January 2026 and released what they call Time Horizon 1.1, they revised the doubling rate. And the doubling time since 2024 is now 89 days. Not seven months, 89 days. That is roughly every three months.

So let me say that differently. Every three months, these AI systems are doubling in their ability to handle complex work. Every three months. When the METR member, Sydney Von Arx, was asked about this, she said, and I want to be fair here and give you her full quote, you should absolutely not tie your life to this graph, but I bet this trend is going to hold.

That is a researcher who helped build the benchmark, telling you, I wouldn't bet everything on these exact numbers, but I believe the direction is real and I believe it continues. And every single time a new model has dropped, the trend has not just continued, it's continued and recently accelerated. So the benchmark is breaking from the weight of its own results. Here is something METR is very open about and it's important context.

We've called Opus 4.6 at 14 and a half hours. The confidence interval, the range of where the true number might actually be, goes from six hours all the way up to 98 hours. That is a huge range. And the reason the range is so wide is telling.

METR says their task suite is now nearly saturated. That means Claude Opus 4.6 is completing so many of the tasks successfully that METR is running out of hard enough tasks to measure where the ceiling actually is. Think about it. The benchmark designed to measure how capable these AI systems are is struggling to keep up with how capable the systems are.

METR is actually building harder, longer tasks to try to get a tighter measurement. They doubled the number of tasks that takes humans eight or more hours from 14 tasks to 31 tasks, specifically to try to find Opus 4.6's real ceiling. What that means in plain English is this. 14 and a half hours might be an underestimate.

The real capability could be significantly higher. And the measurement instrument isn't powerful enough to tell us yet. That is not a reason to relax. If anything, it's a reason to pay closer attention.

Now, what the leaders of the AI labs are actually saying is this. I want to give you the words directly from the people running the companies that are building the systems because what they are saying right now in the last two weeks is more direct and more alarming than anything I've ever heard from this community before. Let's start with Sam Altman. Sam Altman is the CEO of OpenAI.

He was speaking at the India AI Impact Summit in New Delhi on February the 23rd, 2026, just days ago. And he said, and I'm paraphrasing closely here, from the inside, looking at what's going to happen, the world is not prepared. He said, AGI, which stands for Artificial General Intelligence, an AI that can do basically anything a human professional can do, feels pretty close at this point. He said, it's going to be a faster takeoff than I originally thought, and that's stressful and anxiety-inducing.

The CEO of OpenAI just told a crowd of thousands of people that he's stressed and anxious about how fast this is moving. He also said, OpenAI already has models internally that go beyond what is publicly available. He said, they plan to have an intern-level AI research assistant built by September, 2026, and a fully autonomous AI researcher by March, 2028. Just two years from now, right?

And then Altman said something that should stop you in your tracks. He said that by the end of 2028, more of the world's intellectual capacity could be sitting inside data centers than outside them. More intelligence inside computers than walking around in human heads. That is the CEO of the most powerful AI company in the world saying this, out loud, in public, last week.

Now let's go to Dario Amodi. Dario runs Anthropic, the company that makes Claude. He sat down for a three-hour interview with journalist Dakresh Patel roughly 10 days ago. The title of the interview was, We Are Near the End of the Exponential.

When people heard that title, a lot of them thought, oh, good, the pace is slowing down. The exponential is ending. That is the opposite of what Dario means. He's not saying the curve is flattening.

He's saying we are approaching the end game. We are getting close to the point where all the benchmarks that measure AI against human performers will be fully saturated. AI will be at or above human level on everything we know how to measure. He's saying the exponential is ending because the AI is running out of human benchmarks to surpass.

In that same interview, Dario shared Anthropic's financial in a way that he has never done before. He said Anthropic went from $100 million in revenue in 2023 to $1 billion in 2024 to roughly $10 billion in 2025. And then he said they added another few billion in January of 2026 alone. That's a 10X revenue growth year after year for three years straight.

And he credited a huge portion of that to Claude Code, Anthropic's coding agent, which started as- internal experiment that his own agents and engineers became obsessed with and which is now one of the fastest growing products in the company's history. Dario also said something that should reframe how you think about the METR chart entirely. He said that at Anthropic, near 100% of today's software engineering tasks are done by AI models. At the company that is building the most advanced AI systems in the world, the human engineers are not writing most of the code anymore.

The AI is writing the code. The humans are supervising, the humans are making high-level decisions, the humans are steering, but the coding itself, well that task, that entire generations of people built careers around is automated. And Dario made a careful distinction here. He said a few months ago he predicted that 90% of code would be written by models within three to six months and people thought he meant that 90% of software engineers would lose their jobs.

He said no, these are very different things. The spectrum he's tracking goes from 90% of code written by AI to 100% of end-to-end software engineering tasks done by AI, right. And he said we're approaching that second number soon. His timeline for AI matching Nobel Prize level scientists and research, one to two years.

His confidence that we will have what he calls a country of geniuses in a data center within 10 years, 90%. 90% confident in 10 years. Now let's talk about what this chart is actually predicting. METR published a separate research note this month that I think deserves a lot more attention than it got.

They ran a model, a simplified mathematical model of what happens to AI if the current trend in compute growth and algorithmic progress continues. Their median prediction greater than 99% of AI research and development will be automated by late 2032, six years from now. And when they ran simulations across thousands of different scenarios most of those simulations showed a thousand x to 10 million x increase in AI efficiency by 2035. A thousand times more efficient at minimum in nine years.

The most conservative end of that range, 1,000 times. The most optimistic end, 10 million times. If you're trying to wrap your head around what that means for human productivity, for the economy, for what work looks like, I don't think our brains have the right software to process those numbers, not yet. And on the METR time horizon chart itself, if the doubling trend holds we will hit the point where AI agents can independently complete tasks to take humans one full month of work by early 2027.

One month of professional human work done by an AI autonomously by the beginning of next year. That is not science fiction, that's the METR trendline extended 12 months forward from where we are right now. Now before we go further, join the AI Profit Boardroom. I want to pause here for a second because what I'm sharing with you in this episode is exactly the kind of analysis we go deeper on every single week inside the AI Profit Boardroom.

The boardroom is our private community for people who want to stay ahead of AI. Not just read about it, but actually use it to build better businesses, make smarter decisions, and understand what's coming before it hits. Every week we break down the most important AI developments in plain language, we share the tools that are actually working right now, we have live sessions where you can ask questions, and we have a community of people who are thinking about this stuff seriously, not just scrolling past headlines. If what I'm sharing today resonates with you, if you want to go deeper and be part of a group that's actually figuring out how to position for this shift, come join us.

The link is in the description or you can go directly to AI Profit Boardroom. Okay, back to the chart. What Claude Opus 4.6 can actually do right now? I don't want this to all be theory and trend lines.

Let me give you some concrete numbers from Claude Opus 4.6's benchmarks because they tell a very specific story about what these systems are capable of right now. On GPQA Diamond benchmark, the test PhD level science questions, biology, chemistry, and physics, Claude Opus 4.6 scored 91.3. Human experts who hold PhDs in those fields score about 69.7% on the same test. The AI is outperforming domain experts with doctorates by more than 21 percentage points on their own field.

On SWE Bench Verified, which is real GitHub issue resolution, real software bugs from real open source projects, Claude Opus 4.6 solves 80.8% of them autonomously. Four out of five real software problems, so no human writing the fix. On ARC AGI-2, a benchmark that tests abstract reasoning on puzzles the AI has never seen before, specifically designed to be resistant to memorization, Claude Opus 4.6 scored 68.8%. The previous version, Opus 4.5, scored 37.6%.

That is not incremental progress. That is nearly doubling the score on the hardest reasoning benchmark we have. On OS World, autonomous computer use, meaning the AI is navigating a computer interface, clicking buttons, filling forms, completing multi-step workflows, Claude Opus 4.6 scored 72.7%, near human performance for screen-based tasks. On BrowseComp, agentic web search, finding obscuration that requires chaining multiple searches together, Claude Opus 4.6 scored 84%.

And these numbers are not coming from a lab test that is disconnected from reality. These are tests of things that knowledge workers do every day. Research, coding, reasoning through hard problems, navigating software interfaces, finding information and fixing bugs. And the AI is doing all of them or above the level of trained human professionals.

I want to address the skeptics here because there are real legitimate criticisms of the METR chart and I think it's important to engage with them honestly before I explain why I think the direction of travel is still unmistakable. MIT Technology Review published a piece calling the METR chart the most understood misunderstood graph in AI. And their core criticism is fair, right? The chart does not mean an AI can replace one hour of human work just because it has a one-hour time horizon.

The tasks are software tasks, mostly coding, and they don't perfectly reflect the full complexity of real-world work. The confidence intervals are wide, a point that METR themselves acknowledge openly. Researchers from Berkeley have pointed out that measuring task difficulty by human time might not be the right metric. Some things that are hard for humans are easy for machines.

Some things that are easy for humans are hard for machines. And a METR study published earlier this year found some fascinating and somewhat humbling. When they had AI assist software developers in their controlled study, the developers' tasks actually took 20% longer, not shorter. The AI slowed them down, not because it was bad at the work, but because the overhead of working with it ate into the time savings.

And these are real data points. They deserve to be in the conversation. But here's what the critics are not saying. Not a single critic is arguing that AI is not getting better.

Not one. They are arguing about the rate, they're arguing about what the metrics mean, but they're arguing about whether the real world matches the benchmarks. But nobody credible is standing up and saying this progress is an illusion. And the critics who point to the 20% slower result, well, that's a study of developers using AI as an assistant, not as an autonomous agent.

The METR chart is measuring autonomous completion of tasks. Those are different things. The honest answer is that both things can be true at the same time. But it's a tool you work alongside might slow you down in some contexts.

AI as an autonomous agent running independently whilst you sleep might complete your two-day project before you wake up. The guy running natural20.com, a news aggregator and AI tool site, shared his own experience with this publicly. He gave Claude Opus a massive task. Build a working news aggregator website, deploy it, set up the hosting connected to RSS feeds, build a scoring algorithm that ranks stories by Google Trends and Twitter performance on a scale of 1 to 100, a full stack development project.

He went to sleep, he woke up, it was done in four hours. Whilst he slept and then it kept running 24 hours a day, seven days a week, pulling stories, ranking them, updating the site automatically, forever, indefinitely, without him touching it again. He said that same project would take a professional developer at least a day or two, maybe more, to complete. And that was a one-time delivery.

The AI built it and then automated it forever. And that is what the METR point is pointing at. Not a chatbot helping you draft emails slightly faster, an autonomous system that can take a multi-day professional project, complete it whilst you're asleep, and then run the results indefinitely without stopping. I want to talk directly about jobs now because I think a lot of people in the AI conversation are either catastrophizing or dismissing, and neither of those is useful.

So let me give you the data that is actually right in front of us now. In the first six months of 2025, 77,999 tech jobs were directly attributed to AI-driven cuts. That is about 427 layoffs every single day that were specifically caused by AI automation. Entry-level job postings have dropped 15% year over year.

The Stanford Digital Economy Lab found that entry-level hiring in AI-exposed jobs has dropped 13% since large language models started proliferating. Junior software engineering roles specifically saw a 45% drop in hiring volume in the first six weeks of 2026 compared to 2023. Goldman Sachs estimates 6-7% of US workers could lose their jobs due to AI adoption. Klarna went from 7,000 employees to 3,000 employees.

Their CEO said they expect to be below 2,000 by 2030. The company deployed an AI assistant that handles the workload of 700 full-time customer service employees. Microsoft's CEO of AI, Mustafa Süleyman, said publicly in February 2026, white-collar work, where you're sitting down at a computer, being a lawyer, an accountant, a project manager, a marketing person, most of those tasks will be fully automated by an AI within the next 12 to 18 months. 12 to 18 months.

And then the SaaS-pocalypse happened. In early February 2026, Anthropic launched Claude Cowork, an AI agent system that can autonomously handle legal document review, contract negotiation, and regulatory compliance. In a single trading session after that launch, roughly $285 billion in enterprise software market cap evaporated. By mid-February, the total was approaching $1 to $2 trillion in destroyed value from the software sector.

Because investors realized something fundamental. The entire software-as-a-business model, which is built on selling seats, meaning you pay per human using the software, is threatened by a world where you no longer need the humans. If Salesforce charges you $150 per user per month and you go from 100 to 10 AI agents doing the same work, you just cut your Salesforce bill by 90%. Not because the software got worse, but because you have fewer humans.

That is what is called seat compression. And it's terrifying the investors who own the legacy software industry. To be fair, I want to be real with you here. Because not everything about this story is doom and gloom for workers.

Not everything the bulls are saying is right either. Klarna famously replaced their customer service team with AI. And then the CEO admitted they weren't too far. Quality degraded on complex queries.

They started rehiring. The AI handled the easy 60% beautifully. And the hard 40% still needed human judgment. And the HBR analysis pointed out that many companies are laying people off because of AI's potential, not its performance.

They are cutting headcount in anticipation of what they think AI will deliver. Not necessarily because they have proven it works yet. And the METR study found that AI slowed developers down 20%. That is a real result that deserves to be taken seriously.

But here's the tension. The 20% slower result is from early 2025 studies. The METR time horizon chart, which is measuring raw autonomous capability, just jumped from five hours to 14 and a half hours in two months. Those two data points are not necessarily in conflict.

But one of them is moving much faster than the other. And at 89 day doublings, the gap between AI slows you down a little and AI does your job better than you might close faster than anyone expects. There is an analogy that keeps coming up in this conversation. And I think it's the right one.

So I want to spend a few minutes on it. Because I think it actually helps people understand what's happening without either panicking or dismissing it. Think about the printing press. Before the printing press existed, there was a profession called a scribe.

Scribes wrote things down. They copied documents. They produced books. This was a real skilled profession.

Kings and lords and merchants paid scribes to do their work. Then the printing press came along. And within a generation, almost everyone became literate. Not because scribes got smarter, but because the technology made the skill available to everyone.

Nobody today calls themselves a scribe. But we also do not say that writing disappeared as a valuable skill. We have more great writers than ever. We have more people who communicate powerfully through texts than at any point in human history.

The distribution of writing ability is still enormous. Some people are exceptional at it. Most people are average. Some people struggle.

What changed is the baseline. The floor got higher for everyone. And that's what is happening with coding right now. The creator of CoreCode, the engineer who built Anthropics' coding agent, said recently in a podcast interview, coding is solved.

Not that coders will be unemployed, but that the traditional barrier to entry, needing years of deep technical expertise to write functional software, is dissolving. And here's the thing. When the barrier dissolves, what you get is not the absence of value. You get an explosion of people who can do things now that they could not do before.

The person who had a brilliant business idea could not code it. Now they can build the product. And the small business owner who couldn't afford to hire a developer, now their tools are available to them. The non-technical entrepreneur who had a vision, now the vision is implementable.

This is basically like the printing press moment, right? The skill does not disappear. The exclusive access to the skill disappears. And the people who get hurt, who genuinely get hurt, are the ones who are primarily selling access to the skill rather than the judgment behind it.

The scribe who said, I copy documents perfectly fast with beautiful penmanship, that person's market collapsed. The scribe who said, I understand what needs to be communicated and how to make it resonate with the reader, that person's value actually increased. The same split is happening in tech right now. If your value proposition is, I write code, the floor is falling down.

If your proposition is, I understand what the product needs to do, how the user thinks, what the architecture should look like, how to test whether it actually works, your leverage is expanding. Now, let's talk about the acceleration problem and why 2026 is different from every previous AI moment. Every few years for the past decade, someone has said AI is about to change everything. And every time, the prediction turned out to be partially right and dramatically early.

I understand why people are exhausted by this conversation, but I want to make the case that 2026 is genuinely different from 2018, from 2020, from 2022. Not because the hype is louder, because the trend line has broken away from the original model. Here's what METR originally predicted when they published the chart in March 2025. They predicted that at the seven month doubling rate, AI would be able to complete tasks equivalent to one month of human work by 2028.

That was a baseline extrapolation, 2028. Then the doubling time shifted from seven months to approximately three months and METR updated their model. New extrapolation, one month of human work by early 2027. Not 2028, 2027.

One year closer, because the doubling rate got faster. And if the trend continues to accelerate, which is not guaranteed, but which has happened every single time a new model has arrived, we may be dealing with scenarios that the original timeline did not contemplate at all. The AI digest team run by Adam Blinksmith, one of the first analysts to publicly point out that the trend was faster than seven month consensus, put it well when they wrote about what happens as this acceleration compounds. They described a possible flywheel scenario where AI systems become capable enough to help build better AI systems, which makes the next generation arrive faster, which makes those AI systems better at building the generation after that.

This is the scenario that makes Dario Amodi call what he is seeing near the end of the exponential. Not because the curve is ending, but because the curve is approaching something human metrics cannot capture anymore. So what should you actually do with this information? Okay, we've spent a long time on the what and the why.

Let me get specific on the so what, and what do you actually do with this? I want to segment this into a few different groups because the right answer depends on who you are. If you're a knowledge worker in a white collar profession, you know, lawyer, accountant, project manager, marketer, analyst, or writer, the honest truth is that the tasks within your job that are repetitive, document heavy, research intensive, or formulaic are going to be automated faster than most people in your field are willing to admit. The place to focus your energy on is on the judgment layer above those tasks.

You do not want to be the person who is valuable because you can do the research. You want to be the person who is valuable because you know what to do with the research. You do not want to be valuable because you can draft the contract. You want to be valuable because you know which provisions matter and why.

The gap between these two things is where human value lives in an agentic world. Start using these tools now, not to replace your thinking, to remove the tasks that don't require your thinking so that you can spend more time on the ones that do. If you're a business owner or entrepreneur, the leverage available to you right now is extraordinary and most of your competitors are not taking it seriously yet. Every workflow in your business that's repetitive, lead generation, content production, customer support, data analysis, reporting is a candidate for automation using tools that cost a few hundred dollars per month.

You do not need to be technical. You do not need to hire a team of engineers. You need to understand what you want the output to look like and how to find the right tools to produce it. The people inside the AR Profit Boardroom are doing this every week.

If you want to learn from people who are actually building these systems and seeing results, just come join us. If you're a developer or technical professional, the people who thrive in this environment are the ones who not code most efficiently. I can write code for you, you know, for you. And that can be done and automated easily.

Really the coders that thrive or the developers or technical professionals are the people who can architect systems, evaluate outputs, debug edge cases, make trade-offs and understand the real business problem underneath the technical problem. Think about your role as the person who runs the AI team, not the person on the team. If you're a student or early in your career, this is the hardest group to give advice to because the honest truth is the career paths that existed five years ago may look very different by the time you're midway through them. Skills that matter most right now across almost every field are the ability to think clearly about complex problems, the ability to evaluate and verify AI outputs, and the ability to understand what good work looks like even if you're not producing it line by line yourself.

The 40% of tasks that Klarna still need the humors for, that is where your career needs to live. I want to end this with something that doesn't fit into a trend line or a benchmark number because I think we are being fully honest about where this chart is going, what it's showing, and there's a human question underneath this that deserves more than a footnote. Work is not just something people do to make money. Work is meaning.

identity work is how people feel useful and needed and connected to the world around them. When Sam Altman says the world is not prepared, I do not think he's only talking about companies not having the right software tools. I think he's talking about soft societies not having the right structures to support people through a transition this fast. When Dario Amodi writes about a country of geniuses in a data center, the upside of that scenario is anonymous.

I mean, for example, diseases cured faster, scientific problems solved, economic growth that seems impossible from where we are right now. But the transition to that world, the gap between here and there, is a place where a lot of people are going to struggle. I do not want to paper over that with optimism about productivity gains. The 55,000 jobs that were directly attributed to AI cuts in 2025, those were real people.

The 45% drop in junior software engineering hiring in early 2006, those are real people coming out of school into a market that is contracting for them. The question of how we support each other through this, as colleagues, as communities, as societies, is not a question the METR chart can answer. And it's probably the most important question of the next decade. So, wrapping up, here's the three things to remember.

Let me leave you with these three things. Number one, the METR chart is real. The trend is real, the doubling is real. You do not have to bet your life on every data point being exactly right.

But the direction of this thing, the speed at which AI agents are becoming capable of completing complex professional work, is not something you can safely ignore. Number two, the leaders of the companies building this technology are not being cagey anymore. Sam Altman is saying the world is not prepared. Dario Modi is saying coding is effectively automated and anthropic.

The creator of Claw Code is saying coding is solved. These are not marketing statements, these are people telling you what they see from the inside. Number three, the right response to this is not fear, and it's not dismissal. It is preparation.

The printing press did not destroy the value of communication, it expanded who could participate in it. The right move is to understand what the technology can do, get your hands on it and figure out where your judgment and creativity can compound on top of it rather than competing against it. Because the people who win in the next five years aren't going to be the ones who ignored AI until it was impossible to ignore. They are going to be the ones who figured out right now how to put the Bugatti to work.

You're going to be okay, but you need to start moving. Join me in the AI Profit Boarding, link in the comments description, or go to the AIProfitBoarding.com, and I'll see you in the next one. Cheers, bye-bye.