---
title: "Temu-grade intelligence: is AI just a bubble? | Petri Kajander | Negotiator 392"
summary: "App developer Petri Kajander and Sami Miettinen work through what AI actually is. Kajander’s term is \"temuäly\" — Temu-grade intelligence: it guesses superbly and understands nothing. The weightiest part is not the bubble argument but what happens once moderation and customer support are automated — an account taken over by getting an AI support agent to bypass two-factor authentication, and an entire cloud account deleted over private drawings. Plus local models, the realities of the App Store, Gell-Mann amnesia, and two opposed views on whether inference pricing is a bubble. Both speakers are committed parties here, and that is disclosed."
datePublished: 2026-07-02
dateModified: 2026-07-02
originalLang: en
section: tools
sections: ["tools","economy","society"]
authors: ["Sami Miettinen"]
tags: ["Neuvottelija","EP392","Tekoäly","Paikalliset mallit","Alustavalta","Tietoturva","Moderointi","Tekoälykupla","Sovelluskehitys","Petri Kajander"]
canonical: https://www.neuvottelija.com/ai/ep392-onko-tekoaly-pelkkaa-kuplaa-petri-kajander/
---
# Temu-grade intelligence: is AI just a bubble? | Petri Kajander | Negotiator 392

# Temu-grade intelligence: is AI just a bubble? | Petri Kajander | Negotiator 392

> **Summary:**
> App developer Petri Kajander and Sami Miettinen work through what AI actually is. Kajander’s term is "temuäly" — Temu-grade intelligence: it guesses superbly and understands nothing. The weightiest part is not the bubble argument but what happens once moderation and customer support are automated — an account taken over by getting an AI support agent to bypass two-factor authentication, and an entire cloud account deleted over private drawings. Plus local models, the realities of the App Store, Gell-Mann amnesia, and two opposed views on whether inference pricing is a bubble. Both speakers are committed parties here, and that is disclosed.

## The guest — and a connection worth stating up front

**Petri Kajander** is an app developer who builds on-device software with no cloud and no
advertising. His apps come up repeatedly during the hour. **Sami Miettinen**, for his part,
describes having gone from a middling Python coder to Head of AI Finland at Translink
Corporate Finance.

Readers should know this before the content: **neither man is an outside observer.** One
sells apps, the other advises on AI for a living, and they are friends who have followed each
other's work for months. That makes the episode unusually concrete — these are things they
have done themselves — and it also means every position should be read as an interested
party's position. Neither of them claims otherwise at any point.

## 1. "Temu-grade intelligence" — why the word is better than it first sounds

Kajander's term carries the episode, and it is worth separating from the joke.

> **"Everything that's called AI is already in the word: it's pure Temu, and it has never
> seen intelligence."**

The pun is cheap. The claim behind it is not, and it recurs in every section:

> **The model guesses superbly and does not understand what it is doing.** With a vast body
> of statistical material behind it, the guess lands extremely well. But there is no
> awareness, and nothing that knows *what* it is doing.

Kajander draws a practical line from this, and it comes from his own work:

- **In marketing and sales copy** it does not matter, because you can read the output and
  judge it yourself.
- **In factual material** — he gives competitor comparison pages as the example — it does
  matter, because a false claim is false however well it is written.

The difference is not in the model's quality but in **who checks**. From which comes the most
usable instruction in the episode: when a tool produces a lot of material quickly, you need
**predictable safety nets and cross-checks** to find the errors — and that, in Kajander's
words, is "the painful work".

Miettinen adds his own practice: **a second model checks the first one's sources.**
Adversarial checking is, on his account, a baseline requirement when the material is legal.

### The counter-argument the episode makes against itself

Towards the end of the section the opposite view arrives, and it is not reconciled:

**Miettinen** argues that reasoning models, and the knowledge they generate themselves, are a
different thing from mere next-word prediction, and that AI is beginning to solve problems
that have been impossible for humanity. His examples are the classic trick questions —
counting letters in a word — which a reasoning model now answers correctly, and cases needing
ordinary common sense: ask whether you should drive the car to the car wash and the model
tells you to walk, because the distance is short.

**Kajander does not concede.** His answer is structural rather than anecdotal: more data
produces better predictions about what already exists, **but it produces nothing new.** It is
not creative.

The disagreement is left open, and it is the most honest moment in the hour.

## 2. The weightiest section: what happens when there is nobody on the other end

This part is worth reading even if the rest of AI discussion bores you. It is not a forecast
but three cases, and together they form an argument.

### Case 1 — two-factor authentication bypassed through AI support

Kajander's description is precise, and he marks part of it himself as not yet confirmed.

Well-known, short and therefore valuable usernames had been taken over like this: the
attacker signed in from **the same VPN or IP address** as the real user, went into the
customer support chat — which has no humans in it, only AI support — and got **the password
reset without two-factor authentication**.

What makes the case significant is not the attack but the absence of a defence:

> **The user had no recourse at all.** Their security was in order, two-factor was on,
> everything was right — and none of it mattered, because there was no intelligence on the
> other end to explain anything to.

Kajander also proposes a fix, and it is concrete: **a hardened handshake** in situations where
the request comes from the same address or otherwise looks like credential reuse. The episode
does not assess it as an expert would, but it is a proposal rather than a complaint.

### Case 2 — a word in a video

Kajander says he published a video of his own to a platform through a browser. The video
concerned a feature of his product, and it probably mentioned one particular word. **The
account was suspended immediately.** He says he published the whole sequence of events
openly, and asks aloud in the episode that if anyone on the platform's team hears this, the
account be reinstated.

### Case 3 — private drawings in cloud storage

The heaviest example, and Kajander tells it about somebody else. That person had uploaded
their own manga-style drawings to **personal cloud storage**. Nothing had been published. An
automated interpretation flagged one of the images, and **the entire account was deleted at
once**.

### What the three cases say together

Kajander does not leave the examples unconnected, and the conclusion is the most important
thing in the episode:

> **When both enforcement and appeal are automated, the burden of proof reverses.** You are
> expected to prove what you did not do — which is practically impossible, because you may not
> even know what you are suspected of.

He applies the same structure to the Finnish personal identity code: the old code was
**irreplaceable**, so once it leaks the situation cannot be repaired. He notes that a
renewable identifier is now arriving.

The argument does not depend on what you think of AI. It is about **what happens to due
process when the decision is made by a system you cannot explain anything to.**

### A regulatory side-path

The section also contains an observation that is checkable and rarely heard: Kajander says his
app is not available in France because of **encryption regulation** — going by the book,
using strong protection requires official permission. The other blocked country is Russia. He
notes that **a key pair is already encryption**: using public and private keys is enough to
bring an app inside the rules.

This is not verified here, but it is a specific claim that can be answered.

## 3. The bubble — two views that do not fit together

The title question, and the answer is two answers.

### Kajander: yes, and tokens are too cheap

His chain of reasoning is clear:

1. **Everyone is rushing to list.** He cites a report in which a small block of shares was
   priced at a valuation absurd in its order of magnitude.
2. **Inference is subsidised.** Token prices are artificially low, and he expects a
   multiple-fold rise — fivefold, five hundredfold, a thousandfold; he does not claim to know
   the multiplier.
3. **Data centre projects are being cancelled.** He says he has heard that a significant share
   of the projects planned for this and coming years will not be built, because the generators
   or other inputs simply cannot be had.
4. **The public internet has already been eaten.** He refers to an industry chief's statement
   that the public web has been worked through — the cream is off, the easy part is done.

All four are unsourced claims or second-hand information, and are marked as such here. As a
whole they still form a coherent thesis: **the price will not hold, because it is not the real
price.**

### Miettinen: no, and the reason is the wage bill

The counter is the only calculated argument in the episode, and it is worth reading because it
says nothing at all about model quality:

> The global wage bill is, on his figures, of the order of **€54,000 billion**, and Finland's
> around **€135 billion**. An AI worker costs tens of euros a month. **Even if substitution is
> only partial, the comparison is so large that current pricing is not self-evidently too
> high.**

The figures are given from memory and are not verified here; the order of magnitude is
plausible. The weak point in the argument is the one the episode does not address: **the size
of the wage bill says nothing about how much of it is substitutable** — and that is exactly
the question on which the bubble turns.

Neither gives way, and the episode does not pretend to a consensus.

## 4. Tools do not make a master

The easiest section to carry away, and it rests on Kajander's own experiment.

Before the pandemic he ran a **hundred-day challenge**: one video a day. The finding ran
against expectation:

> **The more carefully scripted and produced the video, the worse it usually did.** The ones
> made on a whim took off.

He later repeated the same thing with generative video tools. The conclusion is identical and
he states it plainly: **a tool does not make anyone Christopher Nolan.** It makes doing things
easier; it does not produce original ideas. He applies the observation to the present too:
looking at content made with generative tools, original ideas are thin on the ground.

The same theme produces the sharpest observation about public discourse in the episode:

> **The slop loop.** What used to be a tweet or a text message is now an A4-length piece
> written by an AI. The recipient cannot face reading it, so he feeds it to an AI to
> summarise. **Neither end touches the text any more.**

Miettinen gives his own example, and it is honest: he says he tries to recall the substance of
a conversation from his own memory rather than running the recording through transcription.

Attached is an observation worth sitting with: **virtual assistants working in the Philippines
now use the same models.** Both ends of the chain have automated, and nobody in the chain
necessarily reads anything.

Kajander's cultural note on this is that it is not a technology problem but **the
disappearance of public space**: phoning has fallen away, and there is no longer anywhere to
talk without an agenda.

## 5. Gell-Mann amnesia — the best single concept in the episode

Kajander names the phenomenon directly, and it is the most usable thinking tool here:

> **You read a piece about your own field and see immediately that it is nonsense.** Then you
> read the same source on a subject you know nothing about, and treat the writer as an expert.

He applies it to AI, and that is what makes it new: **the same mistake is easier to make with
a model**, because the text is fluent and confident across every field at once. When the
tolerance is that ninety per cent is right and the rest goes unchecked, the difference is
invisible precisely where it matters most.

From this comes his conclusion about expertise, and it runs against the usual expectation:
**the generalist's value rises.** The model goes deeper than any person, but it does not
understand. Someone who sees things broadly notices **whether this is even possible** — and
that is a check depth does not replace.

## 6. Platform dependence in a developer's daily life

A shorter but concrete section, and it concerns anyone building on top of somebody's tools.

- **Limits change without warning.** Kajander describes a development tool that began blocking
  use practically at the start of a session, and another where token limits went unset and the
  bill came as a surprise. Miettinen mentions a several-hundred-euro surprise of his own.
- **Terms of service are open to interpretation.** An account can be closed for running, on a
  monthly subscription, something the provider considers to belong in usage-based pricing.
- **Providers get switched over limits**, not over quality. That is a market observation, and
  it says more about competition than any benchmark does.
- **The app store's thirty per cent** is, for Kajander, the reason he has avoided becoming a
  publisher more broadly.

Against all of it, his own approach is consistent: **everything runs on the device, there is
no cloud, there are no ads.** That is also what makes his criticism more credible than a
complaint would be — he has paid for the choice in availability.

## 7. Local models and machines

A practical section, useful if you are considering the same. The episode mentions a laptop with
64 GB of memory and a separate small desktop machine for running agents, plus running local
agents on your own hardware. The recurring point: **memory is the constraint**, and its price
is not falling.

The section also contains a security remark dressed as a joke — that you should not send Sami
anything confidential. It is a joke, but it points at the right question, which the episode
leaves open: **when agents read the mailbox, what is the status of confidential information?**

## 8. Poisoned material and the fossil layer

A closing observation that is a forecast but a precise one:

> The models were trained on material that was **not filtered for quality**. That is why
> ideology and false information sit inside them — not because it was chosen, but because
> nothing was chosen.

And the consequence, which is uncomfortable in the context of this very series and therefore
worth marking: **content optimised for search engines and for AI sinks into the training
material.** Kajander sums it up in three words — *fake in, fake out* — and Miettinen takes it
as a question about his own publishing.

## Claims presented as claims

- The course of the account takeover and the claim that two-factor was bypassed via AI support
  (Kajander marks part of it as unconfirmed).
- The individual account suspensions and their stated causes.
- The effect of French encryption regulation on the app's availability.
- The extent of data centre project cancellations.
- The multiplier on token price rises.
- The global and Finnish wage bills (figures from memory).
- Assessments of what individual platform companies intend, or why.

## What survives the episode

1. **"Guesses superbly, understands nothing" is a usable line.** It tells you where checking is
   optional and where it is not — without requiring a position on what models are.
2. **When both enforcement and appeal are automated, the burden of proof reverses.** Three
   specific cases make it concrete, and the argument holds whatever your view of AI.
3. **Gell-Mann amnesia gets worse with models**, because the fluency is identical across every
   field — which is why the generalist's value rises.
4. **A tool does not make a master.** The hundred-day experiment says the same thing that
   generative tools are saying now.
5. **The bubble question turns on how much of the wage bill is substitutable** — not on the
   price of tokens. The episode does not answer it, and that is the most honest thing it can
   do.

## How the episode runs

- **00:00** — Petri Kajander on "Temu-grade intelligence"
- **02:10** — Local models on Mac hardware, and memory
- **03:21** — Mac Mini, OpenClaw and local agents
- **04:57** — Don't send Sami anything confidential
- **05:56** — Sora, TikTok and the hundred-day challenge
- **07:42** — Tools do not make you a master
- **09:37** — CS50, Python and the first apps
- **10:56** — The story of the nootti domain
- **12:46** — Nuotit, Läppää and the Apple tax
- **15:35** — The teleporter analogy and inference machines
- **16:54** — Why AI does not understand what it does
- **17:25** — Takeovers of well-known accounts
- **20:15** — Läppää banned on Threads over one word
- **21:25** — Google deleted an account over manga drawings
- **22:32** — EU regulation, France and the rules on encryption
- **24:20** — ID number leaks and a reversed burden of proof
- **26:45** — AI in the right hands, and safety nets
- **28:25** — A sense of proportion, and Gell-Mann amnesia
- **30:40** — A three-layer brain, with X as a source
- **32:19** — The calmer feed of 2022, and slop
- **34:32** — Skills files that train AI workers
- **36:36** — Human scraping, and the return of the phone call
- **39:27** — Anthropic's limits and the move to OpenAI
- **41:43** — The rush to list, and valuations
- **43:18** — Data centres, the bubble and the value of AI work
- **45:23** — The public internet has already been eaten
- **46:47** — Reasoning models, AGI and the examples
- **49:05** — Poisoned training data and the fossil layer
- **51:38** — Androidisation and augmenting the human