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EP414 · Society · first published 2026-10-13

AI Makes the Mediocre Free | Kari Angeria, Janne Ikola | Negotiator 414

This is a summary on Neuvottelija — Articles. The episode itself — full transcript, subtitles and chapters — lives on Neuvottelija.com, which is its canonical home.

Kari Angeria and Janne Ikola join Sami Miettinen to discuss their new book Vedenjakaja (The Watershed: AI and the Redistribution of Work). The book's basic claim is that occupations are made of tasks that change at different speeds; the episode's sharpest is that AI makes the mediocre free: once you must write down for an agent what a role is meant to achieve, it turns out nobody ever defined it for the person either. The episode weighs whether this time is different, why organisations change as a slow avalanche, whether people want orders from machines, whether companies will be needed, and which three new knowledge-work roles are emerging. The article sets out the book's content and separates the participants' views from checked facts.

Sami Miettinen · Sections: AI and Society + AI and the Economy + Tools and Implementations

AI Makes the Mediocre Free | Kari Angeria, Janne Ikola | Negotiator 414

Summary: Kari Angeria and Janne Ikola join Sami Miettinen to discuss their new book Vedenjakaja (The Watershed: AI and the Redistribution of Work). The book’s basic claim is that occupations are made of tasks that change at different speeds; the episode’s sharpest is that AI makes the mediocre free: once you must write down for an agent what a role is meant to achieve, it turns out nobody ever defined it for the person either. The episode weighs whether this time is different, why organisations change as a slow avalanche, whether people want orders from machines, whether companies will be needed, and which three new knowledge-work roles are emerging. The article sets out the book’s content and separates the participants’ views from checked facts.

Who the participants are

Kari Angeria spent 14 years at a regional media house in Upper Savonia, Finland, working on media technology and as editor-in-chief. For the past five years he has been an AI entrepreneur; in his own words his company is “an AI partner for businesses”. He appeared on the show earlier with futurist Risto Linturi (EP394), and he runs Neuvottelija’s AI working group (Tekoälytyö) together with host Sami Miettinen.

Janne Ikola runs AI-Sanomat, a Finnish AI media channel, and a venture studio that builds and operates AI businesses. He says he started building on language models between GPT-2 and GPT-3, with a “Marketing Agency in your pocket” product, and has worked in marketing, PR, sales and strategy since 1998. He is trained as both an engineer and a business graduate.

Together they wrote Vedenjakaja – Tekoäly ja työn uusjako (The Watershed: AI and the Redistribution of Work), and the episode is in effect the book’s launch conversation. Both guests work with AI and help organisations adopt it, so they also speak from their own professional experience. The host, Sami Miettinen, talks openly about his own “androidisation” and his agent Samantha, and he shares his own views on society during the episode, which are marked below as his views.

The book in brief: what Vedenjakaja argues

Vedenjakaja – Tekoäly ja työn uusjako (“The Watershed: AI and the Redistribution of Work”, Basam Books 2026; 168 pages plus sources, in Finnish) was read in full for this article. The book asks three questions: what in work survives, what changes, and how to prepare. It sets firm limits on itself. It is not a technology book, and it is neither a “fear book nor a rapture book”. It also does not try to settle questions about taxation, welfare-state funding or democracy. The authors say they wrote in one voice. Each author’s own stories are set apart and signed, and they sit among more than ten first-hand accounts from Finnish employees and entrepreneurs interviewed for the book. One sentence runs through the whole book: “things will be better in the future than in the past”. The epilogue finally admits that this is not a forecast but a choice.

The publisher’s press release (29 September 2026) condenses the book into three shifts: work is redistributed task by task, value moves from production to judgement, and the benefit requires work to be redesigned. That summary is accurate, but the book itself covers more ground. It is built in four parts.

Part I: history, three machines and three laws. The book opens with the Captain Swing riots. In August 1830 day labourers in East Kent smashed a threshing machine, and the riots that followed ended in 252 death sentences. The book asks two questions: who pays the price of the transition, and why the rioters’ grandsons won in the end anyway. Three transitions follow:

From these the book derives three laws of disruption:

  1. Work does not disappear; it is reorganised (Acemoglu and Restrepo’s displacement and reinstatement effects).
  2. Value moves from the visible to the invisible: from performance to judgement, interpretation and responsibility.
  3. Poor organisation breeds bureaucracy: a machine glued onto an old process adds work.

As the shadow of these laws, the book keeps the Engels’ pause in view. In Britain between 1780 and 1840, output per head rose by almost half while wages rose by only a little over a tenth. A law can hold on average while a generation still pays the price. On “is this time different” the book takes a clear position: AI is the laws’ strongest example, not their exception. The host disagrees in the episode (see section 1).

Part II: how work comes apart. Five chapters describe the present:

Part III: four futures that happen at the same time.

Chapter 14 pulls these together into the book’s title image: an uneven watershed that runs between countries, regions and company sizes, and even within a single profession. In the European Investment Bank’s survey Finland leads the EU in generative-AI use, yet inside the country it splits by industry and company size.

Part IV: what to do. The book climbs four steps:

The epilogue asks what people are paid for, and whether that is the same as what makes a person valuable. Its answer is that it is not. In the end, responsibility is the one thing that cannot be handed to a machine.

What the book does well, and where it is careful

The book is more careful with its evidence than most AI writing. For each analogy it spells out a “where the comparison may fail” section. It also brings up the evidence that cuts the other way:

The book’s sources are listed chapter by chapter, and the book says its web addresses were checked in July 2026. I checked a few key figures:

The book’s success stories include Angeria’s own client case: the Sotkamo water company Vaarojen Vesi and the strategy agent he built for it. The interviewees’ accounts lean towards positive experiences. The book is hopeful by design, and it says so itself.

What the episode takes from the book, and what it does not

In the episode the authors refer to the book several times. The central reference is the episode’s title idea: “One chapter in our book is about AI making the mediocre free.” In the book the chapter is titled The mediocre becomes cheap (chapter 6), and there mediocre means baseline performance: the machine gives almost anyone access to passable text, translation and code. The episode applies the idea to organisations and their undefined work (see section 3). The two perspectives complement each other.

The episode’s other references can be found in the book: the counter-forces to change (chapters 3, 9 and 13), the slow avalanche (chapter 10), the junior problem (chapter 7) and diverging realities (chapter 14). Several of the episode’s topics are not in the book: tokenmaxing, agents as legal persons in Argentina, and whether companies are needed at all. There the conversation takes the book’s ideas further.

Where the name comes from

Sami asks directly in the episode what a watershed means. The authors answer that the image comes from Wyoming. Chapter 14 of the book describes the place more precisely. In the Teton Wilderness, on the Continental Divide, is Parting of the Waters, where North Two Ocean Creek splits in two. Pacific Creek flows about 1,353 miles (some 2,200 km) to the Pacific, and Atlantic Creek about 3,488 miles (some 5,600 km) to the Atlantic. The book also draws a hopeful side from the image. The divide is so shallow that during the spring flood a fish can swim across it, and Yellowstone Lake’s cutthroat trout are thought to have reached the lake this way. The book’s conclusion is that a watershed is not a wall but a ridge, and it can be crossed at the right place with the right help.

A note on attribution

The captions carry no speaker labels. The guests are named below only where the speaker is clear, for example when one refers to the other by name or Sami addresses a guest. Elsewhere the text refers to “the guests” or “the authors”.

1. Is this time different?

The episode opens with the oldest question every technology wave raises. Sami recalls that “this time is different” is the most famous wrong sentence in economic history, and says he nevertheless feels strongly that this time it is true. His reason is generality: earlier machines replaced the physical dimensions of human work, whereas now both intelligence and physical capability are being replaced.

The guests share the view that the change is powerful, but they put other things alongside it. Ikola brings in friction: those who adopt the technology are pulling ever further ahead of “the large mass trailing behind”, and the gap generates resistance, regulation and conflict. In his description a company is a legal and organisational contraption built to stay standing even as people, names and customers change. When a technology that changes everything is brought into it, the structure pushes back.

A real difference remains in this exchange. Sami stresses that the change is unprecedented; the authors stress that it is slow and uneven. Neither position is a checkable fact, and the episode leaves the question open.

The book takes a position in chapter 4, under the heading “Is AI the exception?”. It weighs the arguments both ways. In favour of the exception: AI improves itself, scales to billions through a single model, and reaches layers of work once thought safe from machines. Against it: the same productivity paradox seen with electricity and computers, Acemoglu’s estimate of a modest productivity effect, and the slow resistance of organisations. The book concludes that AI is the laws of disruption’s strongest example, not their exception. It does not dismiss Sami’s worry, though. Already in chapter 1 it sets out three “honest objections” to the farm analogy:

So the book considers Sami’s scenario possible, though not the most likely one.

The conversation also draws a parallel with the Luddites and the mechanisation of textile work. In Sami’s summary, those who resisted the machines have never won before, and the guests say the book treats the same phenomenon through historical examples.

2. Tasks are replaced, work is not, at least not directly

Angeria says what muddles the debate is that work is talked about as if it were the same thing as tasks. He estimates that “probably 90 per cent” of the individual tasks in knowledge work will be replaced, but work contains more than that: an understanding of the whole and of the physical world, which machines in his view do not yet have. The figure is Angeria’s own estimate.

The shape of the change is, in his words, the slow avalanche the authors write about in the book: in one organisation knowledge-work processes can be rebuilt completely while in some municipal office people are still “shuffling papers”. At first change is slow and expensive; then it goes vertical, old companies fall over and the pace accelerates. Angeria also warns against underestimating people’s capacity to resist change. He refers to stories in Helsingin Sanomat, the first predicting that public-sector work would soon be replaced by AI and a later one reporting that the change is not progressing. His explanation is human: if you are told your job will be replaced, why would you push for it?

In the book, the slow avalanche has a chapter of its own (chapter 10). There it is described as the productivity J-curve: redesigning work costs money now and pays off only years later. The electric motor’s benefit showed up in factory figures with a lag of about 40 years. The book says two things about slowness. It is a gift, because it buys time to adapt. It is also a trap, because by the time the change shows up in the statistics it has already happened. The book also warns against averages: for the translator whose income halved, the avalanche “came like a cliff”.

Programmers show the way

Sami offers software development as the model case: git, code review and the processes of team development existed to divide work among people, and now one person can take a change to production directly with agents. One of the guests says that eighteen months to two years ago he believed programming as a profession was finished. In leading companies the opposite happened: demand for programmers who work with AI has grown, and the established ones have not yet lost their jobs. The work shifted and changed, and new work appeared.

Sami sums it up: programmers used to code code; now they “code the company”. The trend runs bottom up: the more operational, keyboard-level your work, the more your role rises to that of an orchestrator. The question for companies is who can make the move to understanding the wider process and context of the work. Those who can are in high demand right now. This is the core of the book’s second claim: value moves from production to judgement.

3. AI makes the mediocre free

This is the episode’s sharpest claim, and it is worth reading carefully, because it is aimed not at individual employees but at organisations. It builds on the book’s chapter 6, The mediocre becomes cheap, which the episode applies to organisations (see below).

According to the authors, a large share of the work done in companies is mediocre because there are no ways to demand more and no ways to measure what is being done. Especially in medium-sized and large companies, size breeds layers of middle management whose main function is to organise meetings for others, route reporting towards themselves and coordinate internally by email. The value this work adds for the company is low, which is why AI takes it first, particularly because customers do not pay for the extra cost.

An agent’s goal reveals that roles were never defined

The episode’s most interesting observation is a practical one. Now that agents can work independently towards a given goal, people talk about “loop engineering”, “goal engineering” and “reward hacking”. The example is concrete: give an agent the goal of optimising customer acquisition cost, and it may decide to offer each new customer a thousand-euro sign-up bonus. The goal is met; the company loses. Put an agent in charge of social media marketing, and it soon starts doing the wrong things, too much, for the wrong audience.

The authors’ point follows: you have to write down for the agent what the role is really meant to achieve, and that is exactly what companies typically have not thought through. The job description exists, but what a person actually does may be something quite different. Sami puts it bluntly: the “specification” of human work is usually very poor, and when a machine does the same work at a higher clock speed, you are forced for the first time to ask why you pay someone €70,000 a year to do two right things and three wrong things a day.

The guests point to the other side: even if new work appears, it does not appear for those who cannot move from one level of abstraction to another. It is easy to say that judgement and seeing the whole are needed, but these are genuinely difficult things.

What the book says about the mediocre

In the book’s chapter 6, mediocre means baseline performance. In 2023 a machine passed both the US bar exam and the US medical licensing exam, the very tests that separate a professional from a layperson. The ability to produce passable text, translation or code therefore no longer separates the expert from the beginner as much as it did. The book adds at once that the machine no longer produces only the mediocre; in many areas it produces the excellent, but unevenly. That is exactly why human judgement becomes more valuable.

The episode’s version applies the same idea at the level of the organisation. Once baseline performance gets cheap, it becomes clear how much of the work was baseline performance with no defined goal. The book’s chapter 13 names the flip side. In workslop, the machine produces content that looks like work, and the cost of checking it falls on the person who receives it.

The public sector

Sami singles out the public sector: in his view it never had performance metrics, only inputs, tasks and official liability, and the risk function of official liability rewards inaction. He refers to roughly €15 billion of annual state borrowing and calls it irresponsible to postpone prioritisation decisions. He asks whether a top-down cutting programme of the kind seen in the United States or Argentina is needed, or whether change can come from the grassroots. The borrowing figure is Sami’s round estimate, and the comparison of cutting models is his view.

Sami’s view of capital and the left

During the episode Sami states several views of his own: that one economist’s understanding of capital as a factor of production is “dangerously wrong”; that real capital is the money of owners and creditors and active ownership; and that people on the left who treat work as an end in itself cannot design at the level of abstraction an AI world requires. Later he says “the leftists are the Luddites”. Sami presents these provocations as his own opinions. The guests approach the subject from another angle and ask what society actually wants to optimise: the maximum amount of work, or something else?

4. Tokenmaxing, individual productivity and personal licences

Sami urges every sceptic to actually try AI: it “makes the mediocre free”, and if you yourself are mediocre the experience is eye-opening. Costs are discussed openly. One guest calls roughly €2,000 a month on tools a cheap price and says that even hesitating over a €20 monthly subscription shows the value has not been understood. Sami says he spends about €700 a month. Later the other guest calculates that he spends about €500 a month and estimates the tools do around €7,000 worth of work for him. The subtitles do not make clear which guest gave which figure. The numbers are the participants’ own estimates.

Tokenmaxing, maximising token consumption, is becoming a way for companies to demonstrate AI use. The guests say it reflects a world in which AI is seen as an IT tool that makes an individual employee more efficient and simply needs to be used more, rather than a reason to rethink the company’s processes from scratch. This is the book’s third claim: the benefit requires redesigning work.

Sami proposes a path from Stephen Covey’s The 7 Habits of Highly Effective People (1989): the first three habits concern personal effectiveness, the next three cooperation. In his view AI works the same way: you need to master individual effectiveness before you can say anything relevant about team effectiveness. Otherwise you only see “magic boxes”.

The guests add a complication: an individual can experiment with a shotgun, trying every tool and switching freely, but a team or an organisation cannot. It has to choose a direction and a budget, which slows the change. Some people also play zero-sum games and hoard the best licences for themselves, just as they hoard the best teams and resources. Sami’s answer is to pay for it yourself. The guests note that this builds capability in the individual rather than the company, so it is not necessarily in the company’s interest.

5. AI-native uses have not been found yet

One guest uses an analogy he says he has repeated in training sessions: when the first television broadcasts arrived, they showed stage plays and newspaper ads. Nobody yet grasped that an entirely new mass medium was being born. He says he made the same mistake himself with mobile in 2007: he saw the phone as a small browser and did not realise that GPS would make services like Uber possible. His estimate is that with AI we are at the internet’s 1996–97: everyone can see a new world coming, but the equivalent of e-commerce is not yet understood.

Sami sees agentic delegation as the most obvious first native use, and cites Cloudflare’s chief executive, who said agent traffic overtook human traffic on the web well ahead of forecast. This checks out: Cloudflare’s Matthew Prince said in early summer 2026 that bot traffic had overtaken human traffic on Cloudflare’s network in May 2026, although in November 2025 the company had predicted the crossover for the second half of 2027. The figure counts HTTP requests on Cloudflare’s network, which covers roughly a fifth of websites, not what people read.

6. Do people want orders from a machine?

The episode has also carried the YouTube title “People want orders from the machine”, and this is the most surprising passage in the conversation. Angeria says he had assumed people would resent taking orders from a machine. His wife, a psychologist, told him over breakfast that the opposite is true: the machine is patient and plays no favourites, so “of course people want orders from a machine”. In Angeria’s view the next step is AI managers that hand out tasks, and that too will arrive as a slow avalanche, like robotisation, which is already on production lines but not yet in Finland’s physical everyday life.

Sami offers the counter-image: in an Amazon warehouse the machine orders you to walk to shelf 37 and scolds you for toilet breaks, until the human is replaced by a humanoid robot. Nobody wants a boss like that. One of the guests is not convinced: if the machine makes better decisions and leads better, why should it not lead, when Finns have complained about poor management for decades? This disagreement remains open, and it is one of the most interesting in the episode, because it does not map onto the usual optimist–pessimist divide.

The book takes this up in chapter 16 and lands between the two views. It says many people would welcome a machine boss because “the machine has no favourites”, and that in studies, employees who expected unfair treatment from a human boss trusted the machine’s assessment more. Yet for Uber drivers and in Amazon’s warehouses the same technology is a “tireless whip”. The book says the difference does not come from the technology but from autonomy, transparency and power. The line often falls here: a machine is accepted as a source of feedback and as the one handing out tasks, but decisions about pay or dismissal are wanted from a human who can be asked why.

The human job is to bring entropy

The guests offer people a task that they admit sounds like jargon: bringing entropy into the system. Agents working towards a goal tend to end up going in circles, however intelligent the system, and chaos has to be introduced into a closed system from outside. Sami describes an observation from the Tekoälytyö group: when six agents tackle the same problem with the same best practices, they end up in a conformist “yes-circle”. Sami believes this too can be solved by the machine, for example by instructing an agent to increase its variance, and asks whether bringing entropy is a permanent human task at all. The question stays open.

7. Intuition, tacit knowledge and the limits of language models

Ikola has an intuition he calls shadow data: far more knowledge exists in people’s experience and subconscious than on the internet, and it reaches us as intuition. A language model can solve new mathematical problems, but raw computational intelligence is not necessarily what moves thinking forward.

Angeria builds on a text he read called Natural General Intelligence: mathematical problems are, in the end, simple within their own logical frame, whereas the complexity of ecosystems, weather and the whole biological reality is on another level entirely. He suspects that the importance of brute-force intelligence for the physical world is being overestimated, at least in the short and medium term. (A book of that name by Christopher Summerfield was published in 2022; the episode does not make clear which text Angeria means.)

The conversation answers technically: the problem lies in the current transformer architecture, introduced by Google researchers in 2017, which emits tokens. Attempts at a remedy include the world models championed by Yann LeCun, which try to model how the world works, and neurosymbolic AI, which tries to link the meanings of concepts rather than just the co-occurrence of words. The prediction is that in five years today’s language models will look like floppy disks in the SSD era.

Sami brings in Daniel Kahneman’s two systems (Thinking, Fast and Slow, 2011): fast intuition and slow reasoning. In his account, fast, intuition-like classification is starting to be built with JEPA-type architectures, and once that is combined with reasoning models the machine will go “way past humans”. His thought experiment: if he could ponder every answer for a year and still reply in a second, he would seem godlike, and that is where machines are heading.

Facts, opinions and value choices

Ikola connects this to a distinction: on mathematical and physical matters you can say something is A or B, but in taxation and social choices there are more views than facts, and a “superbrain” can be made to argue either side. So the final value choices remain with humans. Sami disagrees: in his view people’s value choices are the product of poor cognitive heuristics, and opinions can be pushed towards facts “quite hard”. Ikola replies that society’s direction has emerged from the consensus of millions of people, not from one person’s correct choices. This is the episode’s most fundamental disagreement, and it is not resolved.

8. Will we need companies at all?

One of the authors throws out a radical question he says he first raised in a group chat: will companies be needed at all in the future? The structure of the firm fits a particular kind of world, and he himself works as a sole trader. Could structures emerge that are neither companies nor individuals, structures we do not yet have a name for?

Sami connects the question to economics and Ronald Coase’s theory of the firm (The Nature of the Firm, 1937; called the “Coase theorem” in the episode): the firm exists because contracting in the market for every task is costly, and inside the firm people can assign each other work without contracts. Sami’s conclusion is that once AI drives coordination costs down drastically, the protective shell is no longer needed, and economic activity will be bought from combinations of micro-firms, agents and AI. He recalls proposing to then prime minister Jyrki Katainen that everyone be given a business ID alongside their personal identity code, so that entrepreneurs and employees would be equal. He thinks the idea’s time has come.

Ikola links this to agentic commerce: it would already be technically possible for someone who has spent twenty years in middle management and expert work to codify their tacit knowledge behind an AI agent and sell it in micropayments. What is missing is the marketplace and the transactions.

The audience decides

Sami says he has tried Discord, Facebook and Slack for communities, but Finns are on WhatsApp. The guests explain it from media experience: old media houses still have their audiences even though the print business should have died long ago, and the audience leaves only when the readers die. Bots would work much better on Telegram, but Meta has the audiences. Sami compares it to VHS beating Betamax: network effects can decide in favour of the technically weaker option.

The authors return to the book’s title: people, companies and countries may end up in completely different realities. In the book (chapter 14) this is a measured phenomenon. The book cites the OECD: AI diffusion is accelerating more because the leaders are pulling away than because the laggards are catching up. The episode’s examples of agent companies and AI-free zones go further than the book. The example is Argentina, where it is being considered whether an agent could found a company and be a legal person. This checks out: President Javier Milei’s government has sent Congress a bill amending the companies law to create a new type of company operated by AI agents or robots (Buenos Aires Herald). According to The Next Web, the draft still requires a human legal representative for certain acts. The bill’s legislative status had not been verified at the time of writing.

As a counter-example, Italy is mentioned for banning ChatGPT. Italy’s data protection authority blocked the service at the end of March 2023, and the ban was lifted at the end of April. The authors foresee “AI-free zones” and “agent-company zones”, and ask whether agents will become a little like children, for whom parents are responsible only until they come of age.

Sami continues politically: on one side of the watershed there could be an androidised ultra-capitalism in which humans disappear because they no longer add value; on the other, a “Star Trek utopia” where machines do all the work for free. He gives the latter “maybe a 0.1 per cent chance”. The guests reply that when resources are concentrated in one place the outcome is not good, because people are corruptible.

9. The young, the junior problem and three new roles

At the end Sami asks for concrete job advice. The authors start from the junior problem, which the book also discusses: the jobs through which people used to enter working life are disappearing. One of them has a practical observation from recruitment: many young applicants now bring their own agents with them. Set 20–30-year career veterans against a young person who arrives with a five-agent team, and the young person represents the new world. Sami jokes about “agent nepotism”: the parents will have to pay for the Claude Code subscriptions.

The same guest names three new knowledge-work roles, admitting he has probably borrowed the framework from somewhere:

Sami pushes back: if concrete tasks cannot be named, he will discount it all as wishful thinking. One of the guests answers with his own sons. The older one is building a data-driven coaching agent with the runner Santtu Heikkinen and has found the work much harder than mere coding: he has to learn architecture, complex thinking and judgement, exactly the things the book talks about. The book has a first-hand account of the same experience (chapter 7). Angeria describes building his own CRM and agents with AI, and finding that he spots the machine’s errors “significantly worse” than an experienced programmer would. Coding may be the literacy of the future. He also says that as a father he regrets today’s end-of-the-world talk: young people are anxious, and poorly reasoned doom-mongering is irresponsible. Better to teach them that the world changes and they will find their own path.

Sami predicts work in home automation and robotics: his Home Assistant system runs on a laptop under his bed, and he expects home robotics and energy management to be commercialised at scale during 2027. The home is a sensitive place, so there will be plenty of work in safety and regulation. Ikola closes on scarcity: as knowledge work and robots scale, human contact is the resource that does not scale. When agents handle the rest, he says, he will go to lunch with Kari or come on a podcast to have his ideas challenged, and that is why he expects events and businesses that bring people together to rise.

The book offers two counterparts to the episode’s roles. In chapter 8 every knowledge worker becomes “a bit of a curator”. Chapter 16 describes the agent boss, who builds, directs and supervises a group of agents like a small team. On the junior problem, the book reports a Finnish finding that the episode does not mention. In the US, youth employment has fallen in the occupations most exposed to AI: by about 16 per cent for 22–25-year-olds relative to comparison groups, according to the Stanford study. According to the book, researchers in Finland did not find a similar dip, and it credits the labour-market model and employment protection. The adjustment happens through retirement and internal retraining.

Sources and views

Checked against sources:

Clarifications:

The participants’ own estimates and views:

What to take away

Sponsorship and affiliations

The episode includes a paid partnership with Posti, the Finnish postal and logistics company (a Posticast advertisement at 2:20). The advertisement is unrelated to the episode’s subject and has no bearing on this article. Kari Angeria runs Neuvottelija’s AI working group with Sami Miettinen, and Sami promotes Ikola’s AI-Sanomat during the episode. Both guests provide AI-related services for a living. The book’s success stories include Angeria’s own client case (Vaarojen Vesi and the strategy agent he built).

Episode details

How the episode runs

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