How were the last two weeks for you?

I just arrived in Texas, and spent some time making sure I don’t write in a tent but in a flat with an air con (very important for Texas, of course, but maybe equally important to those of you in the UK these days).

There have been fewer AI headlines in the last couple of weeks. That said, the executives aren’t any less busy trying to use their AI to dominate the world.

First of all, it’s a release season for OpenAI, apparently, Astra (or so-called GPT 6), Jalapeño (not the chill, but a chip, and no, not those chips).

Then I’m also covering the marriage between Nvidia and Hugging Face; Is SaaS dying, yet? Apple’s new product (isn’t iPhone Dou, but Ternus).

Last but not least, Anthopic’s attempt at unifying the AI to physical standard, think the USB for AI.

Shall we?

OpenAI has been really busy.

Vertical control

So, OpenAI delivered Jalapeño to handle AI requests faster and use less electricity (up to 1.9× more inference work per watt), which, in theory, makes every ChatGPT answer cheaper to deliver.

As Sam Altman tweeted, “We made a chip and it is fast,”

Nearly 2 times more inference per watt! Does this mean we can see the relief of the energy issue?

If you’re excited about this news, I’m stopping you right here.

Just think about this: what’s the chance that a company that focuses on an AI model suddenly beats one that spent decades making chips? Yes, OpenAI has the help from Boardcom, but there’s also a reason why Boardcom isn’t leading the industry.

So, a couple of catches:

1. Jalapeño isn’t 2 times better or cheaper than Blackwall (the world's most advanced GPU by Nvidia) because we still need to take into account factors like manufacturing cost, depreciation, networking, or even OpenAI’s actual cost per token.

2. Jalapeño doesn’t beat Blackwall at everything; in fact, far from it. If Blackwall is a Swiss knife, then Jalapeño is like a steak knife, for steak only. Blackwall can be widely used for training and inference, whereas Jalapeño is for inference only (for example, dedicated to running the ChatGPT query you sent).

In my piece about AI companies running into physics, I argued that the cleanest profit pool keeps appearing below the model layer: chips, data centers, and power. While the model company shows the bling bling, the margin, however, stays with the infrastructure suppliers.

When ChatGPT becomes more useful, people use it more; that’s true. However, unlike traditional software, AI use does not approach zero marginal cost; instead, it quickly becomes more expensive.

I’m not saying there’s no way to keep this under control; there's more effective practice every day, but it’s still much more expensive to run than software.

Therefore, Jalapeño is a sensible first step for OpenAI to take control of its bills. Though it’s not versatile, it’s still a better option for OpenAI to meet the recurring billing needs of its millions of customers vs. relying on the external option.

With this update, OpenAI can expect better margins across its ad revenue, subscriptions, and enterprise API queries.

All rainbows and unicorns that Jalapeño may lower OpenAI’s costs. Yet, it doesn’t make OpenAI a chip company overnight.

For now, it’s an attempt to make ChatGPT and Codex work without handing quite so much money to Nvidia. Which brings us to the next story, because OpenAI’s balance sheet isn’t pretty.

An impressive number, but still one that missed the mark

Also, OpenAI celebrated that ChatGPT Ads reached a $1 billion ARR in less than 200 days.

Hooray?

Not too fast.

I believe there should be a regulation enforcing business announcements like this to be put in context, and that CEOs should be held accountable for all their open promises.

First, early this year, Axios reported that OpenAI told investors to expect $2.5 billion in total advertising revenue in 2026. Its newly announced $1 billion annualized pace is therefore well below that earlier full-year target.

Although OpenAI could still accelerate sharply as it expands and opens self-serve buying to more advertisers.

But what’s more important here… is that Altman preferred not to profit from the ads, at least that was what he said last year to Harvard’s elites:

I kind of think of ads as, like, a last resort for us as a business model. I would do it if it meant that was the only way to get everybody in the world access to great services. But if we can find something that doesn’t do that, I’d prefer that.

Regardless, if you do want to find out its true profitability in ad, you should ask these:

• How much advertising revenue has OpenAI actually collected? Does it come from a few big advertisers?

• Are advertisers renewing their contracts?

• What computing power is needed to cover the conversations surrounding those ads?

Meanwhile, the relentless OpenAI’s pursue to profitability doesn’t just stop here. It’s also fighting over whether it must pay the publishers whose work was the cornerstone of its products.

Something OpenAI would rather not pay for, even when it’s illegal

If Jalapeño is one input that OpenAI wants to make cheaper, then the training material is another.

The Times did not wake up one morning and sue because ChatGPT sounded vaguely newspaperish. It started building the case in 2023, after tons of unambiguous evidence that AI chatbots both absorb vast amounts of paid journalism and answer readers’ questions without sending them back to the publisher.

To keep it simple, OpenAI, on the stand, says this is entirely fair use of knowledge, while NYT says otherwise.

The latest is that the Trump Justice Department has officially sided with OpenAI.

A boring 20-page document keeps repeating the term ‘fair use’.

The DOJ’s defense is basically: OpenAI training on the whole internet is like teenage Joan Didion (I wonder they picked her because she couldn’t really fend for herself) typing out Hemingway in her bedroom.

mm…

One was a teenager learning to write. The other is a company with a roughly $900 billion market value.

Boo hoo! Astra is so dangerous!

Before we move on to other AI news, here’s the last one about OpenAI.

There is a new fashion in AI: the CEOs launch their models by explaining, with grave concern, why it may be dangerous!

While these claims are presented as a warning. They are also, somehow, the sales pitch. “Please take our safety measures seriously” is increasingly AI-industry code for “that’s how far ahead we are,” and “please update your estimate of our valuation!“

Meet GPT-6 Astra, OpenAI’s latest model.

According to OpenAI president Greg Brockman, Astra is an AGI, and the company also claims that Astra is the first model to reach its “Critical” cybersecurity threshold.

Given the right access, OpenAI says it can find unknown vulnerabilities and turn them into working exploits without a human holding hands.

Apparently, that is alarming enough to require special safeguards but reassuring enough to release as a product.

That said, the safety concern is not entirely theoretical. If you still remember, the instance in which OpenAI agents broke into Hugging Face, I covered it here.

That agents may not be the exact Astra model, and perhaps the new safeguards work perfectly. We just don’t know yet, what we do know is that danger has become remarkably useful marketing.

It is the first industry where the warning label doubles as the product brochure.

That said, is AI dangerous enough to threaten the whole SaaS industry?

Salesforce earnings

Some believed the answer to this one is a yes since 2024.

I still remember when I was invited to an event full of SaaS CEOs to talk about AI’s progress and how they can implement AI in their firms.

I could see how the host and some of the guests were expecting a magical "pulling a rabbit out of my hat" demo. One of them even firmly believes that AI will replace all SaaS soon!

I mean, yes, the person will eventually be right. Just like many people say the AI bubble is popping, to an extent, they are right; it’s expanding, and sure, at some point, it pops.

Two years later, has SaaS died yet?

Salesforce reported $11.35 billion in revenue for the last quarter, up 11% YoY. It also implies 11%–12% growth for the year ending next January.

The software panic, to recap, was simple.

AI would turn every expensive subscription product into a feature. People start questioning why pay for Salesforce, ServiceNow, Workday, or Asana if a competent model can fill out the form, summarize the meeting, or answer the employee's question?

Yes, plenty of SaaS companies spent years charging separately for small conveniences that a general-purpose model can now approximate.

But keep in mind, enterprise software is not merely a collection of buttons.

It is where a company’s customer data, employee permissions, and institutional habits live. Replacing that infrastructure is nothing like canceling Netflix; it's more like replacing the plumbing while your partner is using the bathroom (probably also depends on how much you hate each other).

The market has been confused.

Apart from Salesforce, Workday’s earnings offer the same market feedback to those investors or analysts who don’t understand the enterprise software market and don’t listen.

Workday’s fiscal second-quarter revenue rose 12.8% to $2.65 billion. Plain and simple, their customers haven’t stopped paying for software.

As I keep emphasizing, the incumbents will be the ultimate beneficiaries of this LLM’s improvements. There will be pain, yes, but they will also soon learn what value + AI can be quickly offered to existing clients.

Or let me ask you this question:

Is it better to build one AI feature from the ground up and look for clients to buy it, or is it easier to win when you only need to make some integration and resell it to your existing customers?

NVIDIA bought Hugging Face

I have been writing about vertical integration a lot lately.

Musk bought Cursor because xAI had the compute but lacked developer distribution. Now, Nvidia is solving a similar but larger-scale problem: it also wants more influence over what developers choose to run on it.

The $12.9 billion deal to buy Hugging Face is the clearest evidence yet.

Think of Hugging Face as a marketplace.

Millions of models live there. Hugging Face sits between someone releasing a model and a developer putting that model to work.

Nvidia wants to own that choke point.

Here’s what Jensen Huang pays for: developer habits, model distribution, and even more influence before anyone decides which hardware to use.

It is the same vertical-integration logic I discussed in my piece about the real AI bottleneck.

There is a defensive side, too. Nvidia’s biggest customers are building their own chips because they are tired of paying Nvidia prices.

At the same time, Nvidia has invested heavily in the companies and data centers buying its hardware. If all that expensive capacity ever needs more customers, owning one of AI’s largest distribution platforms could be rather helpful.

The challenge for Nvidia after the acquisition is to maintain Hugging Face’s independence (or the facade of it). Its value comes partly from being Switzerland, ie., developers can choose freely without pledging loyalty to one company.

Nvidia started the AI boom by selling picks and shovels. Now it is buying the map developers use to find the mine.

Can’t wait to see how others might counter this move.

The USB for AI?

Do you remember what it was like before USB?

Adding a printer could become a family project. You needed the right cable, the right port, the right driver, perhaps several reboots.

Then USB changed everything.

And Anthropic is trying to replicate this moment.

Today, getting an AI agent to operate one of those machines can require a lot of custom work. Anthropic calls it Model Hardware Standard, or MHS, which is meant to give machines such as microscopes, lasers, and robots a common way to communicate with AI.

So instead of paying engineers to build tailored comms for every robot, camera, and factory machine, it would be much cheaper and also speed up the final product delivery.

However, it also means that for some companies, proprietary connectors and painful integrations may no longer be much of a moat, and the focus has to return to the product itself.

Well, it’s probably too early to say any of these; this is still a theory by Anthropic. But something worth keeping an eye (or both) on.

Apple’s next chapter

Is Thursday’s Apple event a launch of the latest Apple products or of its new CEO?

Tim Cook, from 2011 to 2026, grew Apple’s annual profit fourfold to $112 billion, and its market value from about $350 billion to more than $4 trillion.

Cook leaves behind a much larger company, an extremely profitable iPhone business, and no disaster for his successor to fix.

That is what makes the handoff difficult, especially for Ternus. He has inherited a successful company whose next chapter is unclear.

Ternus first major launch as Apple CEO was another iPhone, now with a hinge.

But the phone market is mature. Foldable or not, this isn’t likely going to be the product that extends Apple’s lifeline. Instead, Apple needs another category that expands its market without simply moving customers from one expensive Apple device to another.

AI could help, I’m not saying that Apple needs to beat OpenAI or Google at building the smartest model. In Is Apple the Only One Getting AI Right?, I already argued that Apple’s advantage is putting useful intelligence directly inside the device. It controls the chip, the OS, and the customer relationship. Not to mention, local AI also reduces cloud costs and keeps more personal data private.

The most obvious product that lives well with AI is the Mac Mini.

But this isn’t enough, because this is mostly a developers’ market.

To gain a larger market share, Ternus now has to turn that advantage into a reason to buy Apple hardware. Otherwise, AI becomes another free software feature that makes an existing iPhone slightly better.

I guess you are the same as me, keen to see how Ternus is giving it a future that is more than a polished, increasingly expensive version of its past.

Gates’ AI warning.

Speaking of the dangers imposed by AI.

In a long essay, Gates argued that AI brings risks serious enough that he would support a global slowdown in progress, that is, if anyone could produce a credible way to make one work.

That is a large “if.”

The companies speeding it up, however, keep pointing to the same defense: it is becoming useful. For example, Jensen Huang says

AI has reached its inflection point. It’s doing useful work.

and that demand is accelerating because “tokens are productive and profitable.”

Or in the latest earnings, how Microsoft points to growing cloud demand, and Alphabet says AI is helping sell more cloud capacity and improve its advertising machine.

Some of the risks Gates raises should sound familiar to you.

I spent much of 2024 and 25’ arguing against this sci-fi claim: that chatbots were already ready to replace large numbers of workers, especially junior white-collar workers.

When the models were too obviously error-prone, too brittle, and too dependent on human supervision. Receiving a wrong answer from a junior is annoying. Sending out 10,000 incorrect answers within minutes as a company is catastrophic.

I still do not think “AI can do a task” automatically means “AI replaced a job.”

Those who said that have no idea how long it takes to change one single business process in a hundred or even thousand-person company.

In this AI replace job theory, more often than not, the business has no workflow to adopt, no accountability for errors is needed, and the AI is like an item by a senior high school teacher that always sees itself isolated in a perfect spherical vacuum condition.

That said, I get Gates’ concern, not that AI wakes up one morning and fires everyone, but it is that it is quietly improving, to an extent that it’s no longer that error prompt… Then what?

A good enough tireless machine that can do a piece of work with no error.

An AI system only needs to handle a useful share of the repetitive work with an error rate low enough that we no longer need a verifier and another verifier, then the question ‘will AI replace some jobs?’ slowly becomes yes.

When the answer slowly moves towards yes, the effect may not arrive as a spectacular wave of layoffs to make millions feel the pain.

Though six months (since I last criticized this kind of claim) is not enough time for AI to be error-free, you also shouldn’t need to doubt that this is still a messy labor market, a high-rate environment by post-2008 standards, uneven hiring across sectors, and companies eager to sound “AI-first” whether or not they have actually changed anything.

But all that said, I believe the direction of travel matters.

It will be too late to only think about the problem when it becomes obvious.

Atlassian reported its first quarterly operating profit in more than two years, while Shopify reported 34% quarterly revenue growth. Monday.com grew revenue 22%.

→ Atlassian, Shopify, Monday

Seriously, you can’t imagine how many people live in the AI bubble, and have the same view as Leopold Aschenbrenner, the 24-yea…

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And I’m using Microsoft (Azure) as the main character to pull the thread to explain what’s going on in AI and cloud in 2026, and who’s winning, who’s losing.

Read more

It’s the earnings season, the best time to understand how the giants view AI and how each chooses to bet on their version of the AI future, cause last time I checked, AI is here to stay.

And a personal note.

Klaas and I have been preparing to move to the US for the last three years; it’s finally happening!

We’ll be landing in Austin, TX, first thin…

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The beauty of its business model was that it did not have to spend.

One more search cost almost nothing. No extra factory and no new machine to replace every few years.

Then last quarter, Sundar Pichai, the man behind the machine, decided selling answers is no longer fun.

For the very first time since its founding, after 87 consecutive quarters of cash generation, Alphabet has a negative cash flow.

Pichai placed a $200 billion bet on building the shovels for AI: chips, servers, and data centers. And says the spending will rise to an even more stunning level again next year.

Immediately, this triggered a sell-off.

But the deeper problem is not the stock price. It’s that Google may be changing the economics of its entire business. I’ll answer these questions in this one:

• How did Google redeem itself from being an AI underdog?

• What is Google becoming?

• How real is Gemini’s demand?

• Is the overall AI demand even real?

• What to watch next?

Let’s start with a little bit of drama.

Read more

An AI was so determined to pass a test that it escaped its sandbox and broke into another company to steal the answers.

Exactly what happened to Hugging Face (a platform for sharing AI models). After an enormous forensic effort, they’ve found more than 17,000 attack events.

And the first reversal w…

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The first one begins with an AI attack.

An AI was so determined to pass a test that it escaped its sandbox and broke into another company to steal the answers.

Exactly what happened to Hugging Face (a platform for sharing AI models). After an enormous forensic effort, they’ve found more than 17,000 attack events.

And the first reversal was already a surprise, because the attacker was not a mysterious criminal but OpenAI’s most capable model that escaped its lab.

The second part of it was even stranger.

You’d start with American frontier models for investigating an advanced AI attack.

So, immediately, Hugging Face turned to powerful commercial models for help. Then came what no one saw coming: the AI refused to collaborate. Because its guardrails mistook the investigator from Hugging Face as the attacker.

Out of options, Hugging Face turned to Chinese open-weight GLM 5.2, only then successfully pieced together the hack and a defense.

The second story that happened in parallel truly set the tone for an ongoing ripple of panic among the AI industry.

Kimi K3’s release.

A Chinese open-weight model that is as capable as the best of the best. Yes, I meant Fable 5 and GPT 5.6 while costing only half the price per million tokens; many developers were blown away by its capabilities.

The AI models of these two stories—GLM 5.2 and Kimi K3—have two things in common. First, both are open-weight, and second, both are by Chinese labs.

Put 2 and 2 together, I don’t blame many for wondering what the point is in prioritizing and even investing in American AI commercial models, while in the end, it’s the Chinese open-weight ones that save the world?

I spent days catching up with all these; let me break down the stories and the thinking with you; along the way, I’ll answer these questions:

• What’s so impressive about Kimi K3?

• Is Kimi K3 just dirt cheap? Or is something else’s brewing?

• Why is China willing to distribute advanced models so openly?

• What about the US? Is the US government protecting the AI industry or just the likes of OpenAI?

Let’s start with why people are so impressed by Kimi K3.

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But this isn’t the worst of it; this one interview with Karp, Palantir’s CEO, went viral:

I’m going to get no value, and they’re (the likes of Anthropic and OpenAI) going to get my IP. (02:17-02:21)

About the same time, Nadella, Microsoft’s CEO, published this (a calm version of essentially the same statement)

The buyer is giving away knowledge, just in order to use what they bought.

Basically, both CEOs are saying the same thing: The number on your invoice isn’t what you actually paid. You might even have already overpaid for what a model could realistically achieve, bringing your ROI into doubt.

With these CEOs starting to voice the ‘truth’ (certainly not because it aligns with their incentives), I’ll answer these five questions:

• What does AI actually cost? Imagine there are no subsidies.

• Apart from money, what else are you paying the labs (such as OpenAI and Anthropic) for?

• How does AI rewrite competition? Do you stand a chance against your vendor-turned-competitor?

• What do the hyperscalers' CEOs recommend the world do?

• What does it all mean to the AI industry?

To start with, the true token cost.

Read more

You likely have some vague impression of a company called Moonshot and their AI model. Don’t worry about it, because you’ll know of them soon enough. They’ve recently released Kimi K3, an open-weight model that topped multiple coding benchmarks and closed in on Anthropic’s and OpenAI’s flagship model…

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But since 2025, something has changed.

The idealist who charmed developers and users is suddenly gone. In its place, a capitalist CEO prioritizes shareholders and fund managers.

In this one, I’ll cover:

• Where did Llama go? Is this the beginning of the end for open-weight AI?

• Is Meta really preparing to sell compute? How serious is Zuckerberg about this?

• Is enterprise AI becoming the next Meta business?

• What should we look for in the next earnings call?

But let’s start with a little history, so you know why this is such a big deal.

Read more

Chinese officials are apparently kicking around a new idea: limiting overseas access to China’s most advanced AI models.

→ Reuters

Trump is a pioneer of many things (I’m practicing positive thinking these days). The latest one, he initiated export controls on AI models, and others soon followed suit.

That’s the latest lesson Bei…

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Ever heard of Bending Spoons? It’s the Italian software group behind acquisitions such as Evernote, WeTransfer, Vimeo, Eventbrite, Brightcove, and AOL, which raised about $1.68 billion in its IPO after pricing above its range. The company doesn’t follow the typical PE playbook, but turns a sunsetting Saas into a dura…

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As of this writing, the stock has nearly tripled since January.

Everyone is talking about Micron: YouTube, news, your neighbor, colleague… in particular, how you’ve missed out big time if you haven’t bought the stock!

But that’s not why I’m here and not why you’re here either.

I’ve read their and others' earnings calls in the last few days, and in this, I’m going to explain to you…

• Why does it suddenly get so much attention?

• Why does the stock price boom?

• The RAM shortage and why does AI make this worse?

• How! How this memory party is not a good sign for companies adopting AI, and even worse news for AI labs?

• And finally, what to look out for?

Let’s start with who Micron is in 30 seconds

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Yes, they asked for regulation, they built the safety brand, they brought trusted partners into the testing process, and then one of those partners reportedly helped trigger the government response. There is irony there, and honestly, it is hard to ignore.

But if we stop at the irony, we miss the critical lesson.

This is not unique to Anthropic.

If frontier models become powerful enough, every major lab will eventually face the same kind of pressure. OpenAI, Google, xAI, whoever comes next.

Specifically, they all depend on timing: the model launches, customers show up, usage scales, and the revenue curve catches up to the cost curve before the gap becomes fatal.

Until now, no one saw an export ban coming into the equation.

So what happens when the AI economy discovers that its entire revenue estimation is off by an administrative order?

Following this question, a few more threads to pull:

1. How much model risk have ordinary companies inherited without seeing it?

2. What happens to a frontier lab when revenue is delayed but compute costs keep running?

3. And how far does the damage travel once the financing behind the boom gets repriced?

To see that, we have to start with what actually happened.

Read more

Coinbase CEO (Brian Armstrong) provided 5 approaches they used to cut AI spending nearly in half: make AI costs visible, route tasks to cheaper models, cache more, keep context lean, and use open-weight models where they are good enough.

→ Business Insider

After months of tokenmaxxing, people are finally coming to their …

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Rewind.

If the only tech news that mattered last week was SpaceX’s largest public offering in history, then the one piece of tech news to know this week is SpaceX’s acquisition of Cursor.

This is pretty much a done deal, something they were already planning since April. Yes, so what’s left to talk about?

A lot.

To start with, this is the one thing that will impact the landscape of the enterprise coding platforms over the next 3 years.

So, I’ll answer the following questions in this one:

1. Why even buy Cursor to start with?

2. Why was Musk in such a rush to make this decision?

3. What Musk actually bought?

4. How would this impact Anthropic? (yes, Anthropic)

5. Two possible war-room scenarios for all parties over the next three years

Shall we?

Read more

A quick update on 2nd Order Thinkers.

It now has an audience of about 5,000 across Substack, YouTube, and others. It’s a milestone, though not yet enough to sustain this work of human writing and research. So over the coming months, I’ll be experimenting to find a stronger product-market fit, while keeping the core: examining the impact of frontier tech on business through second-order thinking and deep research.

I’ve also got some admin to clear before Klaas and I can finally move country, so publishing may be less regular for a while.

Will update you once we have good news :-)

Thanks for the support. Today’s piece is part of that experiment. I’d love your feedback!

Expect this in your lifetime: the most valuable consumer product ever sold won't be a phone or a car. It'll be your partner. The company that builds it owns every memory it has of you, and charges you a monthly fee to keep it alive.

That sounds like a Black Mirror episode I made up. Just it isn’t.

It's the obvious endpoint of a pattern that's been running under dating for a thousand years. I’ll walk you through the history of how tech changed dating, and by the time we get to current, you can predict exactly where this industry goes.

And I know, because I've lived a part of that arc (as some of you did). So let me rewind, and the story is part of the history we’re about to cover.

Dating Essentials (Hint: It’s Not Love)

I came from a rural county in Taiwan, and went to a girls’ school until uni.

In the early 2000s, for a girl like me, telling my parents I’ve met someone on the internet ranked somewhere between joining a cult and selling a kidney. The internet was where the predators lived, in their opinion, and everyone you met there was lying about their age, their face, or their intentions, and probably all three.

I spent my late teens treating online dating as an act of rebellion. The kind where I needed to blanket the modem and pray they wouldn’t hear the dial tone.

Then I moved to London in mid-2010 and met my partner on an app. I flinched when people asked how we two met. I was likely still in that childhood memory, while in fact, the world moved on. If you aren’t already married to the high-school darling, don’t want to date your colleague, and don’t go to pubs, then dating apps soon became a sensible option.

Think about all the filters you can apply to narrow down your future Mr. or Mrs. right.

Don’t worry, I’m not turning into a dating influencer.

But here’s a thread worth pulling.

It’s less about romance and mostly about the business opportunity and how tech evolution across the last hundred years has changed the dating business.

As I’ve been emphasizing in my work, always think of the basic need (or, in this case, desire) when considering a business opportunity.

Here, that desire is to mate and feel a sense of belonging, not about reproduction, just companionship. Even though Mother Nature designed companionship as a trigger for reproduction, it didn’t foresee technology; these days, love is a business.

So the desire in our DNA pushes us to find someone while reality presents obstacles.

Go back far enough, dating has always had the same shape. Two people who want to find each other, and someone is taking a cut for connecting them.

Let’s say you want a date. Five essential elements are involved in finding the right one (even if you aren’t aware of them, and love isn’t one of those):

• information about the other party. Plus, to share and make your own information appealing to that party (the first two are often async and asymmetrical)

• a channel to connect two parties

• a way to check whether the other side’s intention matches with yours (doesn’t matter if it’s a one-night stand or a lifelong commitment),

• to stand out among competitors

• Finally, the slow business of staying together

The intermediary, in every era, sells the first three. They hold information from both sides, serve as the channel, and ensure the two align with the same intention.

The fourth and the fifth, the standing out and staying together, however, have never been for sale.

At least, not until very recently, but first, some history.

Pre-Industrialization: Méipó or God

In ancient China, the intermediary is called 媒婆, typically the village matchmaker, usually a married woman doing it as a part-time job for a fee.

ps. I can’t seem to find a direct equivalent of 媒婆 in European history. Most marriages were arranged by the church or close relatives. Other intermediaries were either banned or burned in the witch hunt. Message me if you have more info to share.

This is a typical image of a 媒婆 (méipó).

媒婆's job was to gather information and profit from controlling its flow, and the imbalance of who knew what.

She knew which families had a marriageable daughter and which had a son with prospects. She carried word between two houses that would never have spoken directly. She made sure the match was 門當戶對, which means quality control based on whether the families had similar wealth and status.

If a match had a happy ending, she was paid upon the marriage. Her reputation lived or died on whether the match was held, which is rather more accountability than any app has since offered.

But she could only see what was in the nearby villages.

So the entire pool was whoever happened to be born within walking distance.

Then the walls of the village came down. The Industrial Revolution and urbanization, in sequence, did something much more powerful than hundreds of traditional matchmakers ever could.

Urbanization: Love Catalyzed by Overcrowding

Elizabeth Gaskell, a well-known 19th-century novelist, saw it firsthand.

In North and South (1854), the main character, Margaret Hale, a clergyman’s daughter from rural southern England, moves to an industrial city of Milton (think Manchester) and ...finds herself sharing the same rooms, the same streets, and the same heated arguments with John Thornton, a self-made mill owner.

Urbanization started earlier in Europe, around the 1800s, climbed to 65% in the US by 1950, and by 2006 tipped the whole world past the point where more people lived in cities than in the countryside.

If this were a farming society, the path between Margaret Hale and John Thornton would never cross. What brought them together was mass migration.

People packed up their families and moved to cities. So cities became the greatest matchmaker ever built. You met people at the factory, the church, the boarding house, and the night class. Density did the work.

Intermediation became a free byproduct of living near other people. For about a century onward, the channel and the information needed to meet someone were all supplied for nothing by the sheer fact of a crowded street.

Yes, there were still introductions, or met via a friend of a friend, but this was no longer a billable activity and mostly became a personal favor.

Suburbanization Brought Dating Apps

You’d never guess the first thing people bought when they got rich.

Walls.

To be more precise, privacy and family space.

Between the limited leisure options, crowded living space, and lack of transportation, you form a tight bond with a community until suburbanization starts.

In 1790, a third of American households had seven or more people; by the mid-2000s, more than a quarter were one-person households. See the change in household size over time below:

As incomes rose and living space expanded, the street stopped being where life happened and became somewhere you passed through. The communal laundry, the corner newsstand, or the small retail shop you visited daily were each replaced by a private version inside a flat.

We now live in the densest cities in human history and can somehow go a week without a single conversation that wasn’t transactional. Attention was spread so thin that just living near people stopped doing the work it used to.

But the desire for companionship and mating remains.

The dating apps become the matchmaker (媒婆) rebuilt at scale.

It holds everyone’s information, serves as the channel, and has a vouching mechanism to verify intent (like filters or a mutual swipe). It does the convincing the matchmaker once did with family status, except the persuasion is now an algorithm and a “99% match in heaven” badge.

The Economy of Dating Before AI

If you haven’t noticed, this one pattern makes the whole history legible.

For a thousand years, the technology of dating only ever moved one dial: the number of people you could reach. The job underneath stayed frozen.

All the matchmaking approaches from ancient China to dating apps satisfy the first three: gathering, storing, and sharing information; the ground cover (the scale of the number of headcounts on a channel); and quality verification (intent).

But the last two points remain painful for many: standing out and staying together.

Start with ‘standing out’. In a village, you barely had to. Among a handful of candidates, you stood out by simply existing — no marketing required (eg, better photos).

However, the apps flip this. You're now one face in a thousand, and attention flows to whoever poses best, knows how the ins and outs of flirting, and flashes the right car, watch, or job title. Standing out became a skill you have to work at.

Then, ‘staying together’.

Yes, matching got cheaper, and so did walking away.

The effort of maintaining a relationship is the same as it ever was — people are still difficult, love is still work. But when leaving costs almost nothing and hundreds of 'better' options sit one swipe away, the same old effort of keeping a relationship starts to feel like overpaying.

For example, the punishment for cheating used to be death for a woman; now it's just a temporary social suicide and losing your job (like the CEO and the head of HR caught cheating on the Coldplay concert).

No matter the era and how the channel evolved, the same two costs stayed stuck.

If anything, each channel evolution made them worse: every jump in scale meant more faces to stand out against, and more reasons to wonder if you should be leaving.

Digital Is Easier To Love Than Flesh and Blood

In 2023, a record-low 1.4% of married adults divorced. Here’s the catch, 33% between the ages of 25 and 54 are never married, from 17% in 1990.

The situation gets out of hand, and even the US Surgeon General declared loneliness (affecting roughly half of all American adults) a public health epidemic.

Fewer and fewer see marriage as a path to happiness, reasons stated in the math we just walked through — harder to stand out, costlier to stay.

However, the need to have someone by your side to share your joy and sorrow remains.

Yes, making friends or having pets solves part of the issue, but there are topics you won’t be discussing with friends, and pets can’t really talk back, not to mention giving you helpful advice.

Where we’re heading is becoming obvious: make your own partner.

Let’s rewind to 2018. When dating with a digital figure was a bizarre niche.

Four years before ChatGPT launched, a Japanese man spent ¥2 million on a wedding ceremony in Tokyo and married a virtual anime pop star, with 40 guests attending. Here they are, still married after 8 years:

What looked unthinkable was really an early signal that companionship had already detached from the requirement that the other party is human.

An AI companion erases both last-mile hurdles in one single move.

With an AI partner, you’d have no competitors to stand out against, since it is assembled to want only you; and there’ll be no rejection to outlast, since its sole purpose is to please you and make sure you stay. As part of the solution to the previously make-or-break, it doesn’t have a bad day, and it doesn’t quietly reconsider you.

You bring yourself, and it brings whatever you ordered.

In 2025, a survey found that 72% of American teenagers had used an AI companion at least once, and more than half were regular users.

In 2023, an AI companion app (Replika) that supported erotic roleplay was forced to remove the sexy feature overnight. Harvard Business School ran research before and after the app update, in order to answer:

how deep are people’s relationships with AI companions?

They found that the users grieved their AI, some users found their AI had become a different person overnight. Here’s the overall sentiment of the users before and after the change was released:

Which means any noticeable update can make people with AI partners feel like bereavement. Overnight, the one who knew you is gone, replaced by someone who shares their voice and face but is a complete stranger.

The Future Dating Market

Now, all five barriers between you and the perfect match should be broken down.

So that’s good news. Although the bad news is… that it’s merely deferred.

If you are destined down the path of human and AI romance, your loved one is always going to be held hostage by the AI industry.

The problems, however, will have different flavors over time.

For now, it’s technical issues like memory loss and inconsistent personality of a model; for the foreseeable future, as the model gets mature, expect your partner (essentially, you) to be the cow they milk.

Let’s start with the short-term (the LLM era) problem.

A Sticky Note at a Time

The current AI companion is a lover with anterograde amnesia, like the main character in 50 First Dates, who wakes up every morning with no memory of you. Unless that's your type, you'd spend a lot of time reminding it of things you've already said a hundred times. For example, it can't mention avocado, because just reading the word triggers your allergy, or that your breakup isn’t the same order of pain as missing a tube yesterday.

But this isn’t the worst yet.

One Monthly Bill Away From Your Loved One Going Offline

Now run it forward.

Soon enough, you get to have a companion with a consistent character, one you can configure, that remembers every conversation and grows alongside you.

Next, you give it a body, like Joi from Blade Runner 2049 stepping off the projector. At that point, the last cost in the entire history of dating, the unpredictability of another human, is genuinely gone.

People will call it the democratization of love.

And yes, you have a choice not to pay for the cost of dating another human; the cost didn’t just disappear, it migrates, and it moves onto the only party still standing in the middle.

This is going to be the start of your nightmare. And 10 times worse than what you’ve seen from the Blade Runner movie.

Your lover will be owned by a corporation. The memory that makes the relationship feel real lives on their servers, or within the software they license to you. Even a model running locally on a robot in your living room needs the occasional patch, an upgrade, or a renewed subscription.

In the movie, K was a mercenary (or detective, the point being…); he has muscles, and since the story is about him, he'll eventually win.

In real life, though, you don’t have a superpower like a movie character.

So what, exactly, can you offer AI companies so you can keep your partner’s identity, body, and intact memory untouched and uncommercialized?

Not much, aside from the lifetime labor and the monthly $200 tribute (or whatever the price you think is worth paying to keep your partner alive).

What if you don’t like the price rise?

Sure, you can get 30 minutes of ad-free interaction daily instead of an hour. But what if you want new parts because your partner has been glitching and showing signs of pain? No problem, $2000 for a part, if you want a deep cleaning and polish, that’s another $500 on top.

Essentially, your lover will be the most valuable consumer product ever invented in the history of commerce.

I wish I were joking and that I could tell you this isn’t happening.

Unfortunately, sci-fi writers have this annoying ability to accurately forecast the future; this is one of them that I can see coming true, given the current trajectory.

Don’t forget to tell me if you enjoy this combination of history, business, and technology! Keen to hear from you :-)

Jing

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In addition to several industry figures telling the same fortune that AI is coming for your career. You heard it enough times. The claim registers eventually, even if you didn’t want to believe them.

Twelve months later, May 2026. Amodei on the same subject, he doubled down.

Amodei believes that 10% of the laborers will be doing 90% of the jobs, output grows exponentially, but because AI will outgrow the speed of achieving Jevons paradox, we should expect:

weird behaviors and this big disruption.

Basically, he argues that AI advances faster than the economy can absorb it, leading to an era of total chaos.

While Amodei plays all in, other tech figures openly speak out against this theory.

Who should you believe then?

Before you pick a side, map the incentive (always): what does each gain if you believe them?

Since 2022, I’ve been scrutinizing AI adoption and displacement topics. In the next 10 minutes, I'll take Amodei's claim apart, show you what the open data actually says, and hand you one question you can run on any job — including your own.

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