I also thought it would make me a better writer - AI finds grammatical mistakes, shows me where to tighten my logic, and suggests pithy turns of phrase that wouldn’t have occurred to me.

Then I started seeing this one very specific style of writing turning up everywhere, newsletters, substacks, and (shudder) LinkedIn. It’s what we now call AI slop.

Despite be being very pro-AI in general, I’m developing a revulsion to writing that looks clearly AI generated.

I don’t know why that is exactly, because I’m not opposed to automating work or sticking to the “old ways” in other contexts.

I think it has to do with feeling like the writer is cheating on the implicit bargain between writer and reader.

I don’t want to spend more time reading a piece than the writer spent writing it. That feels like you are flagrantly wasting my attention.

I’d rather read the prompt you gave the LLM, rather than the page of text it spit out. That would give me a clearer, and more concise, version of the message you wanted to convey.

In that spirit - I’ll go first. Here’s my prompt to get ChatGPT 5.3 Instant to write a cliched essay on how cliched LLM writing generally is.

Do you see these same patterns in writing out in the wild? Reader beware.

Bad LLM writing is easy to recognize. You’ve seen it before. You’ve felt it. It quietly announces itself in the first paragraph — sometimes in the first sentence.

It begins with a promise to delve deeper.

Not into a topic, exactly.
But into the essence of the topic.

And suddenly the prose becomes theatrical. Every sentence leans forward as if something profound is about to happen. Something meaningful. Something that resonates.

But nothing does.

Because bad LLM writing isn’t really about saying something.

It’s about sounding like something is being said.

It’s not clarity — it’s cadence.
Not insight — atmosphere.

The sentences come quickly. Short. Dramatic. One after another.

Almost like drumbeats.

Each one suggesting momentum.

Each one quietly building toward a revelation that never arrives.

The structure follows familiar rhythms.

“It’s not simply explanation — it’s transformation.”

“It’s not just writing — it’s storytelling.”

Bad LLM writing loves abstraction. Concepts instead of specifics. “Moments.” “Journeys.” “Ideas that shape the way we see the world.”

Everything resonates. Everything unfolds. Everything quietly reveals itself.

The language circles the point. Again. And again. And again.

Not because the writer has more to say —
but because the rhythm feels right.

And that rhythm matters.

Because bad LLM writing isn’t trying to communicate.

It’s trying to sound like communication.

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That’s a clear problem and it needs to be corrected asap.

However, the coverage of this issue looks to me more like special pleading from people who don’t like the idea of self-driving cars than an overall concern for safety.

The numbers, as reported in the linked article:

• Human violations: 6,976 (2% or 140 of which are repeat offenders)

• Waymo violations: 24

Yes, there are far fewer Waymos than human drivers in Austin, so the chart needs to be read with that in mind. However, if we are going to treat 24 violations as a crisis, why do we accept 6,976 violations as the cost of doing business?

From the article: “The data we collected from the beginning of the school year to the end of the semester shows that about 98% of people that receive one violation do not receive another,” Assistant Chief Travis Pickford said. “That tells us that the person is learning but it does not appear the Waymo automated driver system is learning”

A key distinction here is whether we should be comparing Waymo to individual human drivers, or the collective human driving system consisting of all human drivers.

If we care about safety, I argue that we should care about the collective human driver because that’s what’s out on the roads each day. When an individual gets a ticket, only that person gets an update. But the next day about 39 new people (6,976 violations / ~180 days the ticket system has been in place) will break the same law. Furthermore, that next day we can also expect people to kill about 110 other people and put another 1,000 in the hospital with life altering injuries across the US.

As I walk through my Austin neighborhood, more days than not I see at least one person running a stop sign. People breaking traffic laws is a completely commonplace occurrence.

Playing whack-a-mole trying to retrain human drivers is a losing battle. We are never going to eliminate this particular issue by fining individual people for safety violations.

If we want to actually stop cars from passing unloading school buses, automated drivers are the only realistic path. Waymo is currently struggling to fix this issue, but it is a solvable engineering problem. Human nature is not.

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This is the central idea of “Main Character Syndrome.” It is the tendency to view oneself as the protagonist of the world’s narrative, while everyone else is merely a supporting character or an extra. You see it when someone treats a busy barista like a background NPC, or when a tourist blocks a subway exit to record a TikTok.

In the West, particularly for those living under governments that are semi-attentive to public opinion, this syndrome is easy to contract. We are used to the world bending to our preferences. We are the consumers, the voters, the users. We drive the plot.

This mindset bleeds into how we talk about Artificial Intelligence. I often hear dismissals along the lines of: “People won’t want to use AI for that,” or “I prefer human connection. AI is just a fad.”

These statements suffer from Main Character Syndrome. They assume that “people” are the only ones with agency. They assume that technology requires our enthusiastic consent to reshape society.

But effective technology has a momentum independent of individual desire. Advanced AI has the potential to concentrate power in the hands of the few entities capable of training it: a handful of tech firms and the national governments standing behind them.

When a technology offers this much strategic and economic dominance, the preferences of the average person become secondary. The system will adopt AI because the system competes on speed and scale, not on whether we find it charming.

This leads to a precarious economic reality. The “Intelligence Curse” essay series explores what happens when the majority of the population loses its economic leverage. In the 20th century, labor unions and strikes were effective because workers were essential gears in the machine. If the machine can run itself, the workforce loses the ability to say “no.” We are left hoping the machine decides to be generous.

Unexamined Main Character Syndrome blinds us to the reality of the coming decade. As main characters, we naturally assume that the story cannot proceed without us at the center. But the world is not a movie, and it is under no obligation to keep the protagonist safe. None of us are the main character, and we need to act accordingly.

Kevin: “I just wanted to say that I appreciate the work that [ ] is doing on AI safety. I think you are the good guys in this race.”

Lab leader: “There are no good guys in this race.”

The remark left me a little stunned in the moment but it makes sense now that I’ve had some time to reflect on it.

Based on other comments he made at the conference, I think he meant that all the major labs are locked in a race where safety is taking a back seat to capabilities and being first to ship. He considers his company caught in the same bind - go slowly and (more) safely and lose to the least cautious group.

I don’t know how we get there, but we need some international cooperation and guardrails to give the AGI contenders the option of being good guys.

The conversation and trends I’m seeing favor hyperscale (1 GW+) data center campuses or lots of small edge (~1-10 MW) data centers sprinkled close to population centers.

Reasons: Access to power, latency doesn’t matter for AI inference, economies of scale, fewer angry neighbors to fight.

Does anyone have a bull case for edge data centers capturing a meaningful share of AI related workloads over the next ~5 years? What are the use cases?

One I’m thinking about is robotics. I spoke with a Google DeepMind robotics engineer working on Gemini Robotics 1.5 at The Curve conference last week and she said it was an open question about whether the AI models would be small enough to live on device or need to reside in low latency data centers when robots roll out more widely in human spaces.

For now I’m thinking the big sites will be the natural winners of the AI compute build out.

TL;DR

• Need an answer in 5 seconds and don’t care too much if it’s wrong? Pick Instant.

• Need deep strategy from first principles? Choose GPT-5 Pro.

• Want one model with the best balance of what matters? Pick GPT-5 Thinking.

• Lazy? Leave it on Auto and get what you get (likely Instant).

My current go-tos

• OpenAI for almost everything except coding.

• For coding, I’m using Claude Sonnet-4.5 on Cursor and really liking it.

Speed

• 5-Thinking usually replies in about a minute.

• 5-Pro often takes much longer, more like 10 minutes. The outputs are good, but it’s difficult to have much back-and-forth at that speed.

• 5-Instant is very fast, but outputs are noticeably worse.

Internet access

• 5-Thinking can browse; 5-Pro cannot. If your question depends on today’s reality, that’s decisive.

Quality

• I sense that 5-Pro is smarter and gives better answers than 5-Thinking, but I humbly submit that I’m probably not smart enough to meaningfully tell the difference at this point. Both are excellent.

• The “help Kevin think through any question he can come up with” benchmark (HKTTAQHCCUW Bench, for short) has been saturated.

• 5-Pro: Strategy formation, blank-sheet problem framing, multi-step plans (“create a business plan to do X”).

• 5-Thinking: Fast iteration, refinement, pulling in fresh facts, turning strategy into shippable details.

If I’m using both, I’ll start with 5-Pro to outline the architecture of the solution, then switch to 5-Thinking to refine the details.

Cost

• 5-Thinking ($20/month) is 10× cheaper than 5-Pro ($200/month).

Bottom line

5-Thinking and 5-Pro both give impressive, helpful answers to my questions. 5-Thinking is 10× faster, can search the web, and costs one-tenth the price of 5-Pro. For my use cases, 5-Thinking clearly delivers more value than 5-Pro.

A final suggestion

When you prompt ChatGPT (or Claude, or Gemini), be aware of which model is actually responding, even if it’s just understanding what the default model is. That one piece of information will make you much better calibrated on current capabilities.

I also wanted to try out OpenAI’s new coding model GPT-5-Codex so here’s a video tribute I made in an hour using Codex. Codex wrote all the code, composed the music, and decided on the aesthetics with me giving it some guidance. I could have continued to spruce it up but an hour seemed like a good point to ship :)

Based on this experience, I think we are going to have a longer In Memoriam video next year.

Thanks to R0bk for providing most of the benchmark data:

https://r0bk.github.io/killedbyllm/

AI itself will simply naturally drive humans off of social media, by generating infinite amounts of slop...

As X and other social media companies lose the battle against the bot swarms, human users may stop relying on those feeds for their window on the world. The psychopaths and attention-seekers might simply get drowned out in the automated cacophony.

That would be a weird end to the age of mass social media, but honestly it’s not the worst ending I could think of.

If you played the original Halo game on XBox you’ll remember the scene when, in the midst of battling the Covenant, both human and Covenant groups are overrun with a 3rd alien species called The Flood. What The Flood lacks in intelligence it makes up for in numbers. Now the Covenant are fighting The Flood and humans and the whole situation becomes more chaotic.

This feels like a good allegory for our current problems with social media. We are wrestling with how to handle social media for ourselves, our kids, our politics, etc. The relationship is often adversarial between the users and the purveyors of social media.

Now in the midst of that unresolved fight, AI generated content is swarming the playing field and changing the game. It has become extremely easy to generate low quality or misleading content - aka “AI slop”.

For me, the exemplar of the initial wave of AI Slop was the “kid makes amazing statue out of plastic bottles” post, which debuted in 2024. You might have seen you aunt resharing this on Facebook last year:

My first reaction is to think the flood of AI slop is a bad thing, and certainly there will be negative effects. One direct impact will be to drown out real human voices on social media platforms.

Upon reflection, I’m not sure if that’s a good or a bad thing. Can we really say that social media platforms and the leading voices on them are serving us well? They didn’t exist ~15 years ago, so they aren’t fundamental to civilization.

I’m not sure if we’ll like what comes afterwards better but in the oncoming battle between social media influencers and the AI Flood I say - let them fight.

PS: To understand the AI scene using more numbers and fewer words, see my live dashboard: takeofftracker.com

Zvi Mowshowitz has the mood/reality gap nailed in his Reverse DeepSeek Moment post. Worth your time.

The real step change already happened

OpenAI’s o3 model, released in April, marked the shift to reasoning models that spend compute thinking before answering. It didn’t just spout off the top of its head, it worked problems, step by step, to a (more often) correct answer. OpenAI’s own release bragged about SOTA wins across coding, math, science, and visual tasks. That’s the stuff you rename the franchise for.

But o3 shipped like a side branch: not the default model (4o was) and without 4o’s friendly (manipulative?) persona. Power users found o3 and loved it, the broader audience didn’t know it existed. I was gobsmacked to learn this week only a few percent of users were clicking the drop down to select o3 instead of the default 4o. I’m sorry o3, you didn’t fail us, we failed you.

Bad naming bent the narrative arc the wrong way

Then GPT-5 arrived carrying the mainline name but not a major step change in ability over o3. OpenAI’s hype-posting, embodied by Sam Altman’s Death Star post, didn’t help and was the entirely wrong marketing approach. Of course the reaction felt flat. Expectations were calibrated to “this changes everything,” and they got “solid incremental gains.”

If o3 had been called GPT-5, the brand would have matched the breakthrough, and the breakthrough would have been recognized more widely when it actually occurred - back in April. That would have reset the GPT clock and given OpenAI more breathing space.

OpenAI knows the names are a mess

This isn’t armchair branding. OpenAI leaders have publicly conceded the confusion. Sam Altman said they “deserve to be mocked” for the model names and promised a fix. Kevin Weil called the naming “atrocious.”

The usual cost of bad naming is a chuckle and a meme.

It’s also taken as a sign of their nerdy earnestness. This time it bent the entire release narrative. The label didn’t match the leap and user penalized the new model with vibes it didn’t deserve. Names don’t change capabilities, but they do shape how we think about those capabilities.

Where this leaves us

• Reality: GPT-5 is a meaningful, broad improvement. If you use it, you’ll notice gains.

• Perception: The vibes suffer because the order was wrong. They underhyped the leap (o3) and overhyped the increment (GPT-5).

• Trajectory: The curve is still going up. There’s no wall in clear sight, but GPT-5 isn’t going to be building Dyson Spheres for us next year either.

PS: If you are more of a number person than a word person, see my AGI analytics dashboard to track AGI progress: takeofftracker.com

The closer you are to something, the more precise your words need to be.

The Three-Bucket Problem

For decades our AI map had three labels:

1. Narrow AI – great at one task, useless at everything else (think google maps)

2. AGI – matches humans on nearly every intellectual metric (think Samantha from Her at the beginning of the movie)

3. ASI – outclasses humans on all dimensions by a large margin (think Samantha from Her at the end of the movie)

In n-dimensional capability-space, we have something like this:

That three-part scheme worked when AGI sat on a fifty-year horizon. But now we are closer and can see finer details.

Today models write code, plan road trips, generate lifelike movies, discover new science, and develop government policy. Everything smarter than a spam filter is subject to the same debate about whether it is “really TRUE AGI” or “just […] on steroids” (l’m looking at you r/singularity). The AGI term is overloaded at this point and it’s tearing at the seams.

The result looks like two drunks in a bar yelling about which quarterback is the GOAT. Same word, zero shared meaning.

Ability versus Skill

François Chollet’s foundational 2019 paper On the Measure of Intelligence that forms the basis for the ARC-AGI benchmark separates intelligence, the ability to learn new skills, from the skills themselves. In his framework, a system can be skilled at an arbitrary number of tasks without being intelligent if it cannot generalize to learn new tasks.

Skill without ability is inherently limited. Ability without skill is useless in practice. Keeping this distinction in mind points to some missing labels that can help clean up our arguments, so we can have new, more interesting arguments.

Two terms to fill the gap:

1. APC — Artificial Practical Competence

Definition: A non-human system that can accept a plain-language goal and complete the real-world steps with human-level reliability across many everyday domains.

The focus here is on useful skills rather than raw learning ability, sidestepping the general intelligence debate entirely. Is it “really thinking”, does it have “true understanding”? For the purposes of APC we don’t care. The questions here are “can it schedule my kids summer activities?” and “can it clean my bathroom?”. Achieving APC would change how we do almost everything we currently do, but would not create fundamentally new things as a first order result.

2. AEDI — Artificial Economically Disruptive Intelligence

Definition: A non-human system with the ability to learn and carry out revenue-generating tasks with sufficient speed, breadth, and accuracy to reshape existing markets, labor demand, and price structures across many industries and at a global scale.

AEDI need not exhibit broad human-level cognition. Its intelligence may be confined to a limited set of commercial functions so long as those functions produce significant economic disruption. Compared with an APC system, AEDI can acquire new profit-oriented skills without human intervention or a long lead time. However, along non-economic dimensions (tying shoelaces, for example) it may be less competent than APC.

Here’s what the enhanced n-dimensional capability-space diagram looks like now with the new terms added:

The Upshot

Cramming too many concepts into the term AGI leaves us arguing past one another. Adding APC for broad, practical skill and AEDI for “took our jobs” shock to the taxonomy of AI will bring the debates into clearer focus and let AGI sit at a higher level of intelligence AND competence.

An argument against AI expansion that doesn't sit right with me is: “AI won’t change much because it will struggle to interact with the real world outside the data center”.

Autonomous robots are the obvious long-term solution, but there’s another option that is faster and easier in the short run - you.

Would you be willing to work for an AI? I don’t mean Google or OpenAI, I mean an autonomous AI agent that sets its own goals.

Sounds odd to be sure, but how different would this be from working for a traditional corporation? A corporation is a non-human legal person that owns assets, signs contracts, and survives every employee who comes and goes.

Most employees of large corporations never meet the key decision makers (CEO, CFO, etc.). Your tasks often pop up via faceless emails and Jira tickets. Swap those human decision makers for an AI and it’s not obvious your workday would feel any different.

If your firm replaced the C-suite last year with a set of effective AI models, how would you even know?

Here’s a concrete example of how I think the shift to AI-as-employer could go:

The year is 2031 and AI is turning the economy upside down. Temendous growth for a few corporations, while unemployment climbs to 20%.

You work as a driver at UPS on a contract worth $150K a year. You like your job but parcel volume is falling as consumer spending pulls back and your wife already lost her job as graphic designer due to automation.

One evening, you receive a call - the self-assured voice on the other end reminds you of Morgan Freeman. The caller introduces itself as an AI named Auggie and offers you $700K a year to work for its company, Augment. Augment currently has 300 human employees and has $8 billion in annual revenue.

Main duties of the job consist of helping to assemble robots, interacting with the government, banks, etc., and performing other physical jobs, as needed. Basically a jack-of-all-trades fixer.

Would you take the job? If not, why specifically would you turn it down?

What if instead of an employed UPS driver, you were a software engineer who was laid off a year ago? Not everyone will jump at the opportunity but only a small percentage of the labor force is needed to to kickstart the AI-robotics economy.

As AI-run corporations expand, the question may invert to “would you be willing to work for a human-run corporation?” Human-led companies could find themselves unable to attract qualified human workers due to slower decision making and lower pay.

It’s bizarre to contemplate, but if AIs start winning the economy, they are going to find a lot of human defectors to work with.

AI won’t burst out of the data centers as a mob of red-eyed robots, it will sprout as dozens and then hundreds of smiling humans, just doing their jobs.

Waymo’s new Teen Accounts in metro Phoenix let 14-year-olds summon a driverless car, no adult onboard.

Kids who are ~5-10 today will treat a driver’s license the way you treat a saddle. Riding a horse is charming, and a hobby for a few enthusiasts, yet it is no longer a tool for daily mobility. The same path is set to unfold for human driving: fun as nostalgia, optional as a life skill.

If that sounds far-fetched, ask yourself whether you’ve become the grandparent who once dismissed personal computers as pointless.

Children absorb new tech faster than anyone else. Ninety-six percent of Americans aged 18-29 own a smartphone, versus just 61 percent of those over 65. The same gap is emerging with cars: only about 40 percent of 16- to 19-year-olds held a license in 2021, down from 64 percent in the mid-1990s.

Driverless rides flip errands and commutes from wasted time into free time. For kids, that means safe independence and an on-demand chauffeur who doesn’t nose in on their conversations. For parents, it means no more twice-daily runs to soccer practice, no arguments over radio stations, and a calendar suddenly cleared of shuttle duty. Even Waymo’s own marketing leans into that promise of “free time for parents.”

There will be a momentary pang the first time you buckle your eight-year-old into a car with no grown-up up front. It will pass about as quickly as the guilt you felt when you first handed them an iPad on a long flight. Convenience has a way of shifting our sense of acceptability.

So yes, you will keep the family minivan in the driveway for weekend trips and perhaps the nostalgic joyride, just as stables keep horses for trail rides. But expect driving lessons to become like saddle lessons: optional, quaint, and destined to be a hobby, not a necessity.

--

Curious where AI is headed next? Track every milestone at TakeoffTracker.com - my AI early-warning radar.

In his recent Uncapped podcast interview, Sam Altman recounted a story of a woman thanking him for ChatGPT, saying it is the only app that leaves her feeling better, rather than worse, after using it.

Same.

I consistently have the same experience - finishing chat sessions with more energy than when I started.

Why the boost? ChatGPT¹ invites me to lob half-formed thoughts/questions/ideas into the void and get something sharper back. A few loops back and forth I arrive at better ideas, faster than I could on my own or in discussions with others.

Scroll the usual social feeds and the contrast is stark. Rage bait, humble-brags, and a steady stream of catastrophizing. You leave that arena tired, wired, and vaguely disappointed in humanity and yourself.

Working with the current crop of LLMs feels different. The bot does not dunk on typos or one-up personal wins. It asks a clarifying question, gives positive and negative feedback, and nudges an idea into a new lane. The loop rewards curiosity instead of outrage.

Yes, alignment issues need to be addressed. I am not glossing over the risk that AIs could feed us exactly what we want to hear or steer us somewhere dark. But really with X, Facebook, etc. that’s where we currently are and ChatGPT/Claude/Gemini are already better than those dumpster fires.

It’s a weird situation: people are discovering it is possible to talk to a machine and walk away happier, smarter, and more motivated to build than from talking to the assembled mass of humanity on the internet.

Less shouting into the void. More pulling ideas out of it.

¹ I’m using o3, but Claude and Gemini are on the same level

The Cope

AGI will usher in an age of abundance that will be widely and willingly shared

The Reality

You will either get enough to survive, or less than that

First, a reality check: US government policy is that UBI (universal basic income) is not coming. Washington is very very far away from Venmo-ing you each month just for being an American.

But if we keep our rose-tinted glasses on, the pro-UBI story sounds plausible. AGI could generate so much new wealth that today’s winners hose tomorrow’s losers down with cash because it feels good and they can’t think of anything to do with all this extra money.

Trouble is, we’ve done this experiment already and the results are in.

Since the Industrial Revolution took off, per capita income has grown roughly 20× since 1700.

If radical abundance was happy and widely shared we’d already see it.

Instead, here’s what we see:

• Hamptons Pilates classes packed with Cartier-clad CEOs wsj.com

• Jeff Bezos dropping ±$46 million on a three-day Venetian wedding wsj.com

• And yes, you too. A net worth of about $140 k puts you in the global top 10% today advisors.ubs.com. If you have that much, are you feeling flush enough to hand most of it away? Why not?
  

The pattern is pretty clear at this point. When new wealth is created:

A. The elite

Capture the biggest slice to fund status contests with other elites

B. The comfortable middle

Get better gadgets, cheaper services, mild anxiety

C. The precarious

Receive just enough transfers to avoid open revolt

D. The invisible
Live outside the winners’ social graph, think garment workers in far-off countries or populations left behind when elites retreat to gated enclaves. They get nothing.

Expect more of the same. In the AI era, Group B and C will be lucky to score cool VR headsets and free synthetic meat. Many will move from Group B and C to Group D. Group A will buy extra centuries of lifespan and summer homes on the rings of Saturn.

Fair? Not really, but it’s already the world we are living in and the default one we can expect going forward.

If you disagree, explain to me in the comments below how you would explain to an Irish tenant farmer from 1850 or a refugee in Sudan in 2025 what’s going on here in the Hamptons:

Otherwise, plan for a future where “just enough to survive” is the ceiling on what most people get...and for some not even that.

For a primer on the current state of AI and AGI see my data dashboard:

https://www.takeofftracker.com/

I often hear the argument that AI is bad because it will “steal people’s jobs.” My take is that no one is against automation of jobs - they’re against automation of their job.

Let’s take you as an example. You (probably) have a job. You’d be terribly upset if you lost it to automation. Your family would commiserate and your friends would rage-tweet.

Everyone else would shrug and proceed to enjoy the upgrade.

If you disagree, do you...

• Pay a human travel agent to book flights or do you use Expedia?

• Use EZPass on toll roads, or do you idle behind the last staffed toll booth?

• Buy hand-loomed or machine-made socks (the original issue that set off the Luddite rebellion)?

Sympathy evaporates the moment convenience enters the chat. Each of those conveniences erased entire career tracks and put thousands out of work. Do you care? Not really.

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That’s little solace to the drivers, coders, support reps, artists, designers and actors (and really everyone) now staring down the barrel of obsolescence. But we’ve been here before and we know how this plays out. History shows that the audience cheers when the curtain falls on expensive, slow, or error-prone human labor.

If AI takes a hammer to many industries at once over the next decade, as I’m expecting, and it eliminates many more jobs than it creates (more on that in a future post) then, as a society we are going to need to rethink the social compact in a big way. Maybe universal basic income, maybe universal basic compute, maybe Mad Max. I don’t think we’ve even begun, as a society, to sketch out how to make this looming change go well.

One thing I’m sure of is that we aren’t going to hold off from applying AI to automate jobs for the sake of those employed. If are unconvinced, just ask your neighborhood knocker-upper.

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Co-written by OpenAI’s o3

The Bluesky tweet above struck me this morning - the way I’ve been using AI has been changing even over the last several weeks.

OpenAI’s recent model, o3, is the first model that I find myself constantly using for advice. I mean constantly. From vetting a business decision, to deciding on a moving company, to deciding where to travel over the next holiday break, it consistently delivers advice that's as good as (or better than) the sharpest minds I know on that given topic.

Normally, accessing this level of expertise means scheduling coffee chats or Zoom calls days in advance. And that’s assuming I know a relevant person to begin with. And if I push too many follow-up questions, the goodwill balance gets dicey (though I’m always happy to reciprocate).

o3 doesn’t have those constraints. It’s always on, immediately reachable, and endlessly patient.

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This model is built for reasoning, which means there’s a brief, strangely satisfying, pause of about 10 seconds before it responds. The pause gives the impression of a thoughtful person pondering before dispensing their wisdom.

The combo of consistently good, immediately available advice on practically any topic has profound downstream implications. Right now, it feels like most people haven’t quite grasped the power of using AI this way, but I think they will soon.

The value of social networks (real ones, not facebook dumpster fires) isn’t going away, but it’s going to change. Especially, for the cases where you don’t “know a guy”.

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Vibe coding happens when you team up with an AI pair programmer, like Cursor or Windsurf (recently acquired by OpenAI for $3 billion). It’s far more than "autocomplete on steroids." These tools are genuinely agentic: you tell them what you want, and they generate the code.

Last year, I was already impressed when Microsoft’s Copilot started giving me accurate two-line code snippets just from written comments. The suggestions quickly got good enough that it became my main coding method.

Since then, newer tools like Cursor have taken this to another level. To see how much further things have advanced, I decided to test it on a real-world project, my new AI dashboard site: Takeoff Tracker.

I built the entire website in about 15 hours. Before this, I had never written JavaScript in my life (still technically haven’t). My background in R and Python helped, but not enough to fully explain how smoothly this went.

Even with my prior experience, I was amazed by the capability of the agent, which was powered by Claude 4.0 Sonnet (good job Anthropic).

Could a professional dev team build something better? Sure. They could add more polish and more sophisticated features. But it would take weeks and cost thousands, which means I wouldn’t have even attempted the project.

This isn’t a fluke. I think it’s a preview of what’s coming. As the barrier to coding drops, expect a Cambrian explosion of ideas that previously would have died on the drawing board. Many of them will be bad, but some of them will change the world.

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When DeepSeek unveiled its open-weight frontier model in January 2025, markets whipsawed and pundits shouted “peak AI.” Yet the release was just another dot on the same exponential cost-performance curve that’s driven every major leap since ENIAC.cset.georgetown.edu

2. Scaling Laws Still Pay Rent

DeepMind’s Chinchilla paper showed that shuffling the mix of parameters and data can squeeze far more capability from the same FLOPs.arxiv.org DeepSeek rode that wave, and so will the next dozen contenders. Efficiency gains don’t cap demand; they slash the effective price of computation and invite us to use more of it.

3. Jevons Paradox, Silicon Edition

Enter Jevons: make a resource cheaper and society consumes even more. The same 19th-century logic that wrecked coal forecasts now wrecks “AI will plateau” takes.news.northeastern.edu Every token that costs less to train or serve simply fuels bigger models and entirely new workloads.

4. Watches vs. Moonshots—75 Years of Escalation

A modern smartphone outguns the Apollo Guidance Computer by roughly five orders of magnitude.independent.co.uk If raw silicon were the limiting factor, we’d have hung up our GPU spurs long ago. Instead, each hardware breakthrough widens the ambition envelope—from guidance systems to GPT-class reasoning, from Pong to photorealistic open worlds.

5. Pocket AI Won’t Kill the Data Center

Sure, your 2027 phone will host a 10-billion-parameter personal assistant offline. Fantastic. Meanwhile:

• Drug discovery teams now spin up generative chem-bio models that demand racks of H100s and Blackwells.intuitionlabs.ai

• Enterprise agents will crawl entire corporate memory graphs, running month-long simulations that dwarf anything a handset can stomach. We are not going to shut down the data centers.

6. The Build-Out Numbers Are Going Vertical

• $7 trillion: McKinsey’s estimate for global AI-compute capex this decade.mckinsey.com

• +165 % power draw: Goldman Sachs’s forecast for data-center electricity demand by 2030.goldmansachs.com

• 10 GW: new capacity breaking ground this year alone, per JLL’s 2025 outlook.jll.com
  If demand were about to taper, hyperscalers and real-estate lenders missed the memo.

7. Bigger Keeps Being Better

History’s lesson is blunt: larger compute pools unlock qualitatively new things—weather models, protein folding, autonomous supply chains. The AI era is just pushing that pattern faster. Every “DeepSeek moment” will look seismic on day one and routine by day 180, clearing runway for the next jump.

Bottom line: Betting that compute demand will flatten is effectively shorting human ambition. Good luck with that.

Then the dates started shifting.

• 2020: Metaculus—my favorite crowd-forecasting site—pegs “first AGI” at 2052.
  

• Today: Same hive mind says January 2027. That’s 18 months. If that holds you’ll have AGI before you have to change the batteries in your smoke detector.

  

The cope that “AGI is always five years away” isn’t true. If anything, we keep overshooting. GPT-4 landed faster than most exper

ts guessed; image, video, and music models followed in months (Google “Will Smith eating spaghetti”); o3 kicked off an entirely new scaling paradigm. Deadlines aren’t slipping—they’re collapsing.

Could AGI 2027 still be wrong? Absolutely. But the new window—2 to 10 years—isn’t sci-fi territory. It’s strategic-planning territory. If AGI shows up this decade, governments, companies, and regular humans are going to be wildly unprepared at the rate we are going.

So why do people still wave the topic away? I’m looking at you, Normalcy bias. Our brains are wired for incremental change. We default to “surely not yet” because yesterday looked like today—and today mostly does too… except for the part where your design new hire is now Midjourney.

Here’s the takeaway: stop treating rapid timelines as clickbait. They might be off, but they’re plausible enough to plan against.

Ask yourself what it means for your career, investments, or kids’ education, now before the smoke alarm starts going off.

1. AI-related hype is off the charts. I don’t want to hear about your company’s AI-native back massager and yes some of the bets on AI will blow up in big, messy ways.

2. The cope is real. Plenty of smart people insist that AI is a nothingburger, citing No true Scotsman fallacies and failing to see how quickly the ground is moving underneath their goalposts.

It’s a Kafkaesque moment: “Germany invades Russia. Swimming in the afternoon.”

Watching the singularity begin to unfold feels both surreal and oddly normal. I look around and things are already a good bit different than 2022 (AI images, video, and music, riding Waymos) but I’ve rapidly become accustomed to the changes and much of my life is unaffected.

I’ll be publishing short posts (under 1,000 tokens 😉) on what’s the parts of the AGI story that really matter and whether it’s time to move to a bunker in Wyoming or buy more NVIDIA stock (both?).

Takeoff Tracker, along with this substack, is a place where we can connect the dots between the wild tech side and the normie, real world side.

Glad you made it here. Buckle up because it’s going to get weird.

Kevin