Five seasons later, that question has become much bigger.

Artificial intelligence is changing firms, work and public institutions. Europe is trying to decarbonise while worrying about energy prices and industrial competitiveness. Globalisation is becoming more selective and geopolitical. Defence spending is reshaping budgets and industrial policy. Housing is squeezing the middle class and the workforce on which every other transition depends.

So InnovEU evolved too.

What began as a podcast about EU-funded projects became a place to ask a more demanding question:

Is Europe actually ready for the transitions it is trying to finance?

This post is designed as a permanent guide to the archive. If you are new to InnovEU, start with the short selection below. If you already know the podcast, browse by theme. And if you are looking for a particular guest or episode, the complete season-by-season directory is further down.

And there is another reason to publish this now.

Season 6 begins on 9 September. This time, the format changes again: fewer isolated interviews, more recurring voices, more conversations that develop over time, and more room to react to what is actually happening in Europe as the season unfolds.

If you are new to InnovEU, start here

Twelve entry points, deliberately balanced across the six territories that will shape Season 6. You can read or watch where available, or listen on your preferred podcast platform.

S5E2 — Platform capitalism — Nick Srnicek

A foundational conversation about platform capitalism and why digital markets concentrate power and value.

Substack · YouTube · Spotify · Apple Podcasts

S5E11 — Algorithmic liability — Gemma Galdon-Clavell

Gemma Galdon-Clavell on algorithmic accountability, auditing and who carries responsibility when AI systems fail.

Substack · YouTube · Spotify · Apple Podcasts

S4E15 — Cheap energy — Fernando C. Gaspar

Why cheap and reliable energy is not a side issue but a condition for European competitiveness and the green transition.

Substack · YouTube · Spotify · Apple Podcasts

S5E16 — Can a Small City Beat Europe’s Capitals? — Tauno Kekäle

Tauno Kekäle on how a smaller European city can build an innovation ecosystem capable of competing far beyond its size.

Substack · YouTube · Spotify · Apple Podcasts

S3E13 — Dead Globalization — Josh Dorfman

Josh Dorfman on what changes when the old model of globalisation stops being a safe assumption.

Substack · YouTube · Spotify · Apple Podcasts

S3E12 — Tariffs + Funding — Annetta Benzar

Annetta Benzar on tariffs, innovation funding and what a more geopolitical global economy means for Europe.

Substack · YouTube · Spotify · Apple Podcasts

S3E8 — War Economy — Fernando C. Gaspar

A concise introduction to the war economy as an economic transition, not simply a rise in military expenditure.

Substack · YouTube · Spotify · Apple Podcasts

S3E22 — War economy index — Lily Bethencourt

Lily Bethencourt on measuring Europe’s war-economy transition and linking security choices to wider economic capacity.

Substack · YouTube · Spotify · Apple Podcasts

S4E9 — Housing transition — Pedro Pinheiro

Pedro Pinheiro on housing as a constraint that spills over into mobility, labour markets and every other transition.

Substack · YouTube · Spotify · Apple Podcasts

S4E10 — Middle class squeeze — Jason Fishman

Jason Fishman on the middle-class squeeze and the cumulative pressure created by Europe’s simultaneous transitions.

Substack · YouTube · Spotify · Apple Podcasts

S5E19 — Do EU Funded Projects Leave No Trace? — Christine Lester

Christine Lester on the uncomfortable question of whether EU-funded projects leave capabilities and change behind after the grant ends.

Substack · YouTube · Spotify · Apple Podcasts

S3E21 — Quality EU projects — Paola D’Elia

Paola D’Elia on what quality in EU-funded projects really means beyond completing activities and outputs.

Substack · YouTube · Spotify · Apple Podcasts

Browse the archive by theme

You do not need to remember which season an episode belongs to. Start with the transition or problem you care about.

AI, digital power and platform capitalism

· S5E2 — Platform capitalism — Nick Srnicek — Substack · YouTube · Spotify · Apple Podcasts

· S5E4 — AI regulation — Brando Benifei — Substack · YouTube · Spotify · Apple Podcasts

· S5E6 — Platform extraction — Cory Doctorow — Substack · YouTube · Spotify · Apple Podcasts

· S5E7 — AI funding Europe — Luc Julia — Substack · YouTube · Spotify · Apple Podcasts

· S5E8 — Platform future — Ethan Zuckerman — Substack · YouTube · Spotify · Apple Podcasts

· S5E9 — Big tech trap — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S5E11 — Algorithmic liability — Gemma Galdon-Clavell — Substack · YouTube · Spotify · Apple Podcasts

· S5E12 — Wikipedia and AI — Jan Gerlach — Substack · YouTube · Spotify · Apple Podcasts

· S4E13 — AI platforms — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S3E25 — Digital transition index — Jean-Michel Viola — Substack · YouTube · Spotify · Apple Podcasts

Green transition, energy and industrial transformation

· S4E15 — Cheap energy — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S5E3 — Green power innovation — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S5E16 — Can a Small City Beat Europe’s Capitals? — Tauno Kekäle — Substack · YouTube · Spotify · Apple Podcasts

· S5E17 — What Makes Innovation Ecosystems Last? — AnnaMaria Annicchiarico — Substack · YouTube · Spotify · Apple Podcasts

· S3E4 — Proposal + Hydrogen — Luis Esteban — Substack · YouTube · Spotify · Apple Podcasts

· S3E16 — Green tech tariffs — Cristina Pérez Simón — Substack · YouTube · Spotify · Apple Podcasts

· S3E17 — Smart cities — Antonio Álvarez Romero — Substack · YouTube · Spotify · Apple Podcasts

· S3E18 — Green claims — Leandro Cartelli — Substack · YouTube · Spotify · Apple Podcasts

· S3E23 — Circular innovation — Pablo Frieiro — Substack · YouTube · Spotify · Apple Podcasts

· S2E7 — Green Transition, Vlore University and SME support call — Eva Cipi — Substack · YouTube · Spotify · Apple Podcasts

New globalisation, trade and competitiveness

· S3E12 — Tariffs + Funding — Annetta Benzar — Substack · YouTube · Spotify · Apple Podcasts

· S3E13 — Dead Globalization — Josh Dorfman — Substack · YouTube · Spotify · Apple Podcasts

· S3E14 — Tariffs & Innovation — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S3E15 — Ricardo outdated — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S3E16 — Green tech tariffs — Cristina Pérez Simón — Substack · YouTube · Spotify · Apple Podcasts

· S4E5 — Europe two wars — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S2E2 — Draghi’s Report — Christof Thöny — Substack · YouTube · Spotify · Apple Podcasts

· S2E3 — Global Innovation Index vs. Draghi Report — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

War economy, security and strategic autonomy

· S3E8 — War Economy — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S3E10 — ReArm impact — Anna Contu — Substack · YouTube · Spotify · Apple Podcasts

· S3E22 — War economy index — Lily Bethencourt — Substack · YouTube · Spotify · Apple Podcasts

· S4E5 — Europe two wars — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S5E5 — Autocracies — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

Housing, inequality and social pressure

· S4E9 — Housing transition — Pedro Pinheiro — Substack · YouTube · Spotify · Apple Podcasts

· S4E10 — Middle class squeeze — Jason Fishman — Substack · YouTube · Spotify · Apple Podcasts

· S4E14 — Housing crisis — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S5E14 — Beyond GDP: Are We Measuring the Wrong Future? — Steve MacFeely (OECD) — Substack · YouTube · Spotify · Apple Podcasts

EU programmes, proposals and innovation ecosystems

· S2E1 — Erasmus Evaluation Mysteries — Igor Razbornik — Substack · YouTube · Spotify · Apple Podcasts

· S2E8 — Is the application selection process rigged? — Fernando Mota — Substack · YouTube · Spotify · Apple Podcasts

· S3E1 — Readiness Pyramid — Marco Amici — Substack · YouTube · Spotify · Apple Podcasts

· S3E3 — Partnership Building — Erçan Küçükarslan — Substack · YouTube · Spotify · Apple Podcasts

· S3E9 — Evaluators Score — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S3E21 — Quality EU projects — Paola D’Elia — Substack · YouTube · Spotify · Apple Podcasts

· S3E24 — Why proposals fail — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S4E1 — Turning EU Calls into Wins — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

· S4E4 — EU innovation funding — Julia K. Dietmar — Substack · YouTube · Spotify · Apple Podcasts

· S5E15 — Patras Science Park — Takis Konstantinopoulos — Substack · YouTube · Spotify · Apple Podcasts

· S5E17 — What Makes Innovation Ecosystems Last? — AnnaMaria Annicchiarico — Substack · YouTube · Spotify · Apple Podcasts

· S5E18 — Is Your EU Project Territorially Blind? — Francesco Molica — Substack · YouTube · Spotify · Apple Podcasts

· S5E19 — Do EU Funded Projects Leave No Trace? — Christine Lester — Substack · YouTube · Spotify · Apple Podcasts

· S5E21 — Is Your EU Proposal Already Losing Points? — Fernando C. Gaspar — Substack · YouTube · Spotify · Apple Podcasts

And now: Season 6

Season 6 will run from September to the Christmas break, with a new episode published every Wednesday from 9 September.

The editorial map is built around six connected territories:

· Digital Transition & AI

· Green Transition & Energy

· New Globalisation

· War Economy

· Housing Crisis

· EU Programmes, Governance and How Europe Works

The biggest change is the format.

Instead of asking every guest to appear once, a small group of recurring voices will return throughout the season. Gemma Galdon-Clavell, Christine Lester and Polina Stavrou have already agreed to take part, alongside specialist guests invited for particular questions.

The point is not to ask somebody for one prediction about Europe’s future.

It is to bring people back, compare what changed, test earlier assumptions and follow the transitions while they happen.

Season 6 starts on 9 September. Subscribe now if you want to follow it from the beginning.

The complete InnovEU archive

For each episode, use the platform links available: Substack · YouTube · Spotify · Apple Podcasts.

A note on Season 1: most of the original 2024 episode titles were simply “Episode 1”, “Episode 2”, and so on. The short topic labels below are therefore editorial navigation tags based on the guests, organisations and archive information preserved in the master playlist; they are not retroactive episode titles. Where the archive does not support a more precise description, I have deliberately kept the label broad.

Season 1

S1E1 — Episode 1 — Fernando C. Gaspar

Podcast launch · EU projects · innovation

Substack · YouTube · Spotify · Apple Podcasts

S1E2 — Episode 2 — AHU NGO, Jane Enkhbaatar, Mongolia CIAPE, Eleonora Perotti, Italy

Civil society · education · European partnerships

Substack · YouTube · Spotify · Apple Podcasts

S1E3 — Episode 3 — Studio GDAC Avvocati Associati, Danilo Castrolla, Italy IECE, Angelina Taneva-Veshoska, North Macedonia

Entrepreneurship · project development · legal support

Substack · YouTube · Spotify · Apple Podcasts

S1E4 — Episode 4 — Office of the Prime Minister of Herzegbosnian County, Ivana Mišković, Bosnia Herezegovina ETCC, Gabriel Vartopeanu, Denmark

Regional governance · cross-border cooperation · EU projects

Substack · YouTube · Spotify · Apple Podcasts

S1E5 — Episode 5 — CUMEDIAE, Stefanie Thomas, Belgium; Croatian Education Development Institute, Gordana Radonić, Croatia

Culture · education · EU project development

Substack (full video) · YouTube (audio only) · Spotify · Apple Podcasts

S1E6 — Interviews + EIE call — Polina Stavrou; Anna Lisa Trono

European Innovation Ecosystems · community organisations · EU calls

Substack · YouTube · Spotify · Apple Podcasts

S1E7 — Episode 7 — Fatma Trabelsi, NCP from Tunisia Armend Berisha, from UNI, Kosovo

Horizon Europe / NCPs · universities · international cooperation

Substack · YouTube · Spotify · Apple Podcasts

S1E8 — Episode 8 — Alfonso Santaniello, Conform, Italy

Vocational education and training · skills · EU projects

Substack · YouTube · Spotify · Apple Podcasts

S1E9 — Episode 9 — Alessio Lupi. Maria Angeles Sobrero

EU project partnerships · organisational cooperation

Substack · YouTube · Spotify · Apple Podcasts

S1E10 — Episode 10 — Mariafranca Caruso, from GTechnologies, Italy. Pablo Morales

Technology · entrepreneurship · European cooperation

Substack · YouTube · Spotify · Apple Podcasts

S1E11 — Episode 11 — Stefanos Panakoulias

EU project practice · partnership building

Substack · YouTube · Spotify · Apple Podcasts

S1E12 — Episode 12 — Rexhina Marku. Henrik Haubro

EU project partnerships · European cooperation

Substack · YouTube · Spotify · Apple Podcasts

S1E13 — Entrepreneurship, Democracy & Gender — Estephany Guillermo. Özge Zençir Akçakmak

Entrepreneurship · democracy · gender

Substack · YouTube · Spotify · Apple Podcasts

S1E14 — Season Balance and Hate Speech — Fernando C. Gaspar

Season review · hate speech · democracy

Substack · YouTube · Spotify · Apple Podcasts

Season 2

S2E1 — Erasmus Evaluation Mysteries — Igor Razbornik

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S2E2 — Draghi’s Report — Christof Thöny

New globalisation / competitiveness

Substack · YouTube · Spotify · Apple Podcasts

S2E3 — Global Innovation Index vs. Draghi Report — Fernando C. Gaspar

New globalisation / competitiveness · EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S2E4 — Digital Transition in EU and USA — Walaa Farid

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S2E5 — The AWTY Index – Digital Transition — Fernando C. Gaspar

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S2E6 — AWTY Index – Digital Transition in the EU and USA — Fernando C. Gaspar

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S2E7 — Green Transition, Vlore University and SME support call — Eva Cipi

Green transition / energy · EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S2E8 — Is the application selection process rigged? — Fernando Mota

EU projects / European cooperation

Substack · YouTube · Spotify · Apple Podcasts

S2E9 — Open calls — Fernando C. Gaspar

EU programmes / innovation

Substack · Spotify · Apple Podcasts

S2E10 — Relevance, One Health and Media call — Ricardo Assunção

New globalisation / competitiveness · EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

Season 3

S3E1 — Readiness Pyramid — Marco Amici

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S3E2 — U!REKA IDEA selection — Ana Luísa Raposo; Mona Roman

EU projects / European cooperation

Substack · YouTube · Spotify · Apple Podcasts

S3E3 — Partnership Building — Erçan Küçükarslan

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S3E4 — Proposal + Hydrogen — Luis Esteban

Green transition / energy · EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S3E5 — EU Startup Funding — Fernando C. Gaspar

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S3E6 — Tech Power + AI — Davide Balbi

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S3E7 — Dev Traps — Bilal Almobarak

Development / cohesion

Substack · YouTube · Spotify · Apple Podcasts

S3E8 — War Economy — Fernando C. Gaspar

War economy / strategic autonomy

Substack · YouTube · Spotify · Apple Podcasts

S3E9 — Evaluators Score — Fernando C. Gaspar

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S3E10 — ReArm impact — Anna Contu

War economy / strategic autonomy

Substack · YouTube · Spotify · Apple Podcasts

S3E11 — Communication problem — Fernando C. Gaspar

Communication / impact

Substack · YouTube · Spotify · Apple Podcasts

S3E12 — Tariffs + Funding — Annetta Benzar

New globalisation / competitiveness · EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S3E13 — Dead Globalization — Josh Dorfman

New globalisation / competitiveness

Substack · YouTube · Spotify · Apple Podcasts

S3E14 — Tariffs & Innovation — Fernando C. Gaspar

New globalisation / competitiveness · EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S3E15 — Ricardo outdated — Fernando C. Gaspar

New globalisation / competitiveness

Substack · YouTube · Spotify · Apple Podcasts

S3E16 — Green tech tariffs — Cristina Pérez Simón

Green transition / energy · New globalisation / competitiveness

Substack · YouTube · Spotify · Apple Podcasts

S3E17 — Smart cities — Antonio Álvarez Romero

Green transition / energy

Substack · YouTube · Spotify · Apple Podcasts

S3E18 — Green claims — Leandro Cartelli

Green transition / energy

Substack · YouTube · Spotify · Apple Podcasts

S3E19 — Melanie Das — Melanie Das

EU projects / European cooperation

Substack · YouTube · Spotify · Apple Podcasts

S3E20 — Digital push lacks — Efthymios Serpetzoglou

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S3E21 — Quality EU projects — Paola D’Elia

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S3E22 — War economy index — Lily Bethencourt

War economy / strategic autonomy

Substack · YouTube · Spotify · Apple Podcasts

S3E23 — Circular innovation — Pablo Frieiro

Green transition / energy · EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S3E24 — Why proposals fail — Fernando C. Gaspar

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S3E25 — Digital transition index — Jean-Michel Viola

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S3E26 — Digital transitions ready — Ricardo Pinheiro

Digital transition / AI · New globalisation / competitiveness

Substack · YouTube · Spotify · Apple Podcasts

Season 4

S4E1 — Turning EU Calls into Wins — Fernando C. Gaspar

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S4E2 — AWTY future EU projects — Gero Federkeil

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S4E3 — Failing transitions — Christina Simou

EU projects / European cooperation

Substack · YouTube · Spotify · Apple Podcasts

S4E4 — EU innovation funding — Julia K. Dietmar

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S4E5 — Europe two wars — Fernando C. Gaspar

War economy / strategic autonomy

Substack · YouTube · Spotify · Apple Podcasts

S4E6 — Readiness transitions — Ioannis Nikolaidis

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S4E7 — Beyond GDP — António Serrador

Measurement / productivity

Substack · YouTube · Spotify · Apple Podcasts

S4E8 — Transparency — Jaden Gomes

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S4E9 — Housing transition — Pedro Pinheiro

Housing / inequality

Substack · YouTube · Spotify · Apple Podcasts

S4E10 — Middle class squeeze — Jason Fishman

Housing / inequality

Substack · YouTube · Spotify · Apple Podcasts

S4E11 — What success looks like — Fernando C. Gaspar

EU programmes / innovation

Substack · YouTube

S4E12 — Book presentation — Fernando C. Gaspar

EU projects / European cooperation

Substack · YouTube · Spotify · Apple Podcasts

S4E13 — AI platforms — Fernando C. Gaspar

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S4E14 — Housing crisis — Fernando C. Gaspar

Housing / inequality

Substack · YouTube · Spotify · Apple Podcasts

S4E15 — Cheap energy — Fernando C. Gaspar

Green transition / energy

Substack · YouTube · Spotify · Apple Podcasts

Season 5

S5E1 — Economics in AI age — Fernando C. Gaspar

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S5E2 — Platform capitalism — Nick Srnicek

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S5E3 — Green power innovation — Fernando C. Gaspar

Green transition / energy · EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S5E4 — AI regulation — Brando Benifei

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S5E5 — Autocracies — Fernando C. Gaspar

War economy / strategic autonomy

Substack · YouTube · Spotify · Apple Podcasts

S5E6 — Platform extraction — Cory Doctorow

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S5E7 — AI funding Europe — Luc Julia

Digital transition / AI · EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S5E8 — Platform future — Ethan Zuckerman

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S5E9 — Big tech trap — Fernando C. Gaspar

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S5E10 — Energy of intelligence — Fernando C. Gaspar

Digital transition / AI · Green transition / energy

Substack · YouTube · Spotify · Apple Podcasts

S5E11 — Algorithmic liability — Gemma Galdon-Clavell

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S5E12 — Wikipedia and AI — Jan Gerlach

Digital transition / AI

Substack · YouTube · Spotify · Apple Podcasts

S5E13 — Episode 100 — Fernando C. Gaspar

EU projects / European cooperation

Substack · YouTube · Spotify · Apple Podcasts

S5E14 — Beyond GDP: Are We Measuring the Wrong Future? — Steve MacFeely (OECD)

Measurement / productivity

Substack · YouTube · Spotify · Apple Podcasts

S5E15 — Patras Science Park — Takis Konstantinopoulos

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S5E16 — Can a Small City Beat Europe’s Capitals? — Tauno Kekäle

EU projects / European cooperation

Substack · YouTube · Spotify · Apple Podcasts

S5E17 — What Makes Innovation Ecosystems Last? — AnnaMaria Annicchiarico

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S5E18 — Is Your EU Project Territorially Blind? — Francesco Molica

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S5E19 — Do EU Funded Projects Leave No Trace? — Christine Lester

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

S5E20 — What Profit Signal Are You Missing? — Fernando C. Gaspar

EU projects / European cooperation

Substack · YouTube · Spotify · Apple Podcasts

S5E21 — Is Your EU Proposal Already Losing Points? — Fernando C. Gaspar

EU programmes / innovation

Substack · YouTube · Spotify · Apple Podcasts

One last thing

Archives are only useful if people can find — and use — what they need.

If this library is useful to you, subscribe to Polis Doxa and InnovEU, Like and Restack this post, and share one episode with someone who would benefit from it.

If there is a question, transition or guest you think Season 6 should address, leave it in the comments or send me a message.

Season 6 starts on Wednesday, 9 September. Follow it from the beginning — and come back to this library whenever you need to find an earlier conversation.

Imagine getting the job you wanted, in the city where the opportunities are, and discovering that taking it makes little economic sense because you cannot afford to live close enough to work.

The economy records another job created.

You experience a closed door.

Between 2020 and 2025, 148 of the 194 countries in AWTY 2026 recorded a decline in Housing Crisis Management.

More than three quarters moved backwards in five years.

That is why housing crisis is not simply another social problem.

It can become the bottleneck that stops the other four transitions from producing prosperity.

Your paper, proposal or landing page is excellent. Then the LLM evaluating it can’t extract the value you put into it... You’re gone.

The same can happen to your marketing copy and legal texts: the evidence is there, but the AI misses it or downgrades it.

How to Write for LLM Evaluation helps you learn how I fix this.

Make AI see the strongest version of your work before it decides you are not good enough.

Learn more

The mistake is believing one housing policy is a housing strategy

The accepted debate usually offers two answers.

Build more homes.

Or control prices and rents.

Both identify real mechanisms. Neither is enough in isolation.

A city cannot solve persistent scarcity without adding supply. Households hit by sudden cost shocks cannot wait a decade for planning reform and construction pipelines to work.

The mistake is not building.

The mistake is believing that building alone is a housing strategy.

My contrarian thesis is simple:

Thirty years forward, five years backward

The reconstructed HCI 2.0 global mean rose from 4.671 in 1990 to 5.594 in 2020.

Then it reversed.

By 2025, the world mean had fallen to 5.209.

And 52 countries were below their own 1990 Housing Crisis Management score.

So the long-run story is not collapse. The world still performs better than in 1990.

The warning is that part of three decades of progress has been lost since 2020, and the reversal is broad.

If this is your first Polis Doxa essay, subscribe free.

Subscribe now

Polis Doxa connects AI, energy shocks, trade wars, war economy and housing crisis on the same map.

Refer a friend

A housing system is not one number

HCI 2.0 measures five outcomes, each with a 20 percent weight: affordability burden, housing cost pressure, housing services availability, supply responsiveness, and exclusion or inadequacy.

That changes the policy question.

A country can build quickly and still leave households under severe cost pressure.

• It can cap rents while failing to expand supply.

• It can provide strong housing services while younger or lower-income households remain locked out of the places where opportunity is growing.

The mistake is treating one visible symptom as the whole system.

This is the same measurement problem I discussed in Are We Measuring the Wrong Things?: a headline number is useful only when we understand what it compresses.

And it echoes The Digital Divide Is No Longer About Internet Access: access is not the same thing as transformation.

• Units matter.

• Prices matter.

But outcomes depend on the system around them.

A methodological break worth making

One warning is essential.

HCI 2.0 is a methodological break from the previously published Housing Crisis Index.

Every historical comparison in this essay uses the reconstructed 1990-2025 HCI 2.0 series. These scores must not be compared directly with the old Housing Crisis Index.

The 2025 endpoint is also an operational estimate.

Housing is where the other transitions hit the ground

The first essay in this series argued that The World Does Not Have Five Separate Crises.

Housing shows why.

AI creates new clusters of high-productivity work. But if the workers needed by those firms cannot live near them, digital progress becomes a labour-market constraint.

Energy shocks affect household bills, construction costs and building standards. As I argued in The Green Transition Is Not a Renewable Energy Race, success requires a system, not one visible indicator. A green transition that ignores housing affordability can also lose political support.

Trade wars reorganize materials, investment and supply chains. Firms cannot successfully relocate activity if receiving cities cannot absorb their workers.

A war economy shifts budgets, industrial priorities and labour demand. Conflict also creates displacement. Mobilisation capacity means less if the places receiving people cannot house them.

That is why The World Is Rearming. But Spending Is Not Readiness matters here too.

Inputs are not systems.

The five transitions collide in physical space.

I might be wrong

Perhaps I am making housing crisis management too complicated.

In many high-demand cities, restrictive planning and insufficient construction clearly constrain supply. There is also a legitimate case for temporary price protections when housing costs move much faster than incomes.

I do not think either argument is wrong.

HCI 2.0 does not prove that a five-part policy package causes better outcomes. It is a diagnostic, not an experiment.

What it shows is why success on one dimension cannot be assumed to solve the others.

The strategic question is therefore not simply:

Build or regulate?

It is:

Share

Why this matters

The common narrative treats housing crisis as one more item on the policy agenda.

I think that gets the hierarchy wrong.

Seven essays ago, we started with a claim:

The world does not have five separate crises.

We can now make it more precise.

• AI determines what work becomes possible.

• Energy shocks determine what it costs.

• Trade wars determine where it can happen.

• A war economy determines what states prioritize.

Housing crisis determines who can actually participate.

A country does not manage transition by winning one race. It manages transition by preventing one bottleneck from stopping the others.

And housing is the door.

If this series changed how you see the five transitions, share this final essay with someone who still sees five separate crises.

And tell me in the comments: which housing bottleneck is most serious in your country right now: affordability, cost pressure, supply, housing services or exclusion?

Leave a comment

The seven essays end here.

The next step is the 24 September AWTY 2026 international launch, where the question becomes practical:

Which bottleneck should each country tackle first?

Research Corner: one source free

Gaspar, Fernando C. (2026), AWTY 2026 Housing Crisis Management Index 2.0, Are We There Yet? / Transition Science, 5 August 2026.

The report provides the reconstructed 1990-2025 series, the five HCI 2.0 dimensions, country rankings, confidence classes, methodological limitations and the evidence behind this essay.

Below the paywall

Free readers get the argument. Paid readers get the map.

Below: the Portugal housing diagnostic, a test of how much Housing changes the overall AWTY map, the methodology note and the full Research Corner.

Subscribe now

Read more

A government can increase its defence budget in one afternoon.

It cannot build an ammunition plant, train thousands of technicians, repair a broken supply chain or create a functioning logistics network in one afternoon.

That gap is becoming one of the defining economic problems of the war economy.

In 2025, 121 of 194 countries have a lower AWTY War Economy score than they had in 2020. Nearly two-thirds of the countries in the dataset therefore show lower measured mobilisation capacity or intensity than five years earlier.

This sounds odd.

Governments are raising military budgets, announcing procurement programmes and talking about defence industries again. Shouldn’t readiness be rising too?

Not necessarily.

The accepted narrative says defence spending is the clearest measure of preparedness. It matters, but it measures an input.

The deeper question is whether a country can turn money into sustained capability.

Defence spending is an input. Readiness depends on the mobilisation system around it.

That is the same measurement trap I discussed in Are We Measuring the Wrong Things?. A convenient headline indicator can hide the system underneath it.

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Rearmament is not mobilisation

The AWTY 2026 Transition to a War Economy Index measures five dimensions: defence burden, arms-transfer intensity, political stability, formal alliances and material influence. Each receives a 20% weight.

It does not directly count every factory, stockpile, technician or logistics corridor. Those belong to the strategic interpretation around the index, not to hidden variables inside it.

That distinction matters.

The global mean was 1.577 in 1990. In 2025 it stands at 1.654, on a 0-10 scale. It was 1.707 in 2020.

Even Europe, the highest-scoring region, averages only 1.978.

The United States leads at 4.240, followed by Russia at 3.571, China at 3.488 and Ukraine at 3.475.

No country reaches 4.5.

Budgets can rise quickly. Capability cannot.

A country can buy aircraft without enough technicians to maintain them. It can order ammunition without enough industrial capacity to replenish stocks. It can sign alliance commitments without having the infrastructure needed to move equipment quickly.

And it can spend more today while creating fiscal commitments that become harder to sustain tomorrow.

That is why a war economy is an economic transition, not simply a military budget.

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The economy fights too

Factories need workers.

Weapons systems need semiconductors, software and energy.

Armed forces need ports, roads, maintenance, communications and finance.

Those needs interact with the other transitions.

The digital transition needs many of the same chips, skills and energy systems. As I argued in The Digital Divide Is No Longer About Internet Access, infrastructure is not enough when a country cannot convert it into productive capability.

The green transition competes for capital, minerals and industrial capacity. Trade wars change strategic suppliers and dependencies. Housing shortages can make it harder to attract workers to the industrial clusters governments are trying to expand.

The transitions do not queue politely. They compete for the same balance sheets, engineers, factories and political attention.

That does not make higher defence spending irrational.

Threats are real. Governments may rationally accept costs today to reduce larger risks tomorrow.

But it changes the question.

Not:

How much are we spending?

Instead:

What capability does the next euro create?

A democracy has a second problem

War economies reward speed, executive coordination and secrecy.

Democracies depend on scrutiny, accountability and open competition over public resources.

Those principles can collide.

A government may mobilise faster by centralising decisions. But if mobilisation erodes accountability, legitimacy can weaken.

The opposite risk also exists. Democratic procedures that cannot produce decisions under pressure can leave a country vulnerable.

AWTY does not score that democratic trade-off.

It does not measure democratic quality, political merit, welfare or approval of militarisation. A higher score means greater measured capacity or intensity in the transition towards a security-first posture.

Refer a friend

I Might Be Wrong

There is a serious measurement problem here.

Some military capabilities are classified. AWTY alliance data end in 2012 and are carried forward. Material capability data end in 2022. The security component uses 2024 data.

Arms-transfer intensity can also give small economies unusually high scores after relatively modest transactions.

So AWTY is not a real-time military-readiness dashboard.

But that limitation cuts both ways.

If readiness is difficult to observe, we should be more cautious, not less, about replacing it with the number that happens to be easiest to see: the defence budget.

The number that matters is not the budget

The common story is simple.

A country spent 1.5% of GDP. Now it spends 2%. Therefore it is more ready.

Maybe.

The real test comes later.

• Can it produce?

• Can it repair?

• Can it move equipment?

• Can it finance sustained mobilisation?

• Can it work with allies?

• Can its institutions absorb the political and social pressure?

Budgets buy inputs. Readiness requires a system.

That is why this matters for governments, firms and investors.

The war economy shifts demand towards logistics, cybersecurity, energy systems, advanced manufacturing, materials and maintenance. But headline spending tells us surprisingly little about which countries can turn that demand into durable capability.

Most people have opinions about rearmament.

The useful question is whether countries can turn money into capability.

If this argument helped you see the war economy differently, share it with someone working on strategy, investment or policy.

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The final essay in this series turns to the transition that can quietly undermine all the others: the housing crisis.

One paper before the paywall

Lindvall, Johannes, Magnus Lundgren and Jan Teorell (2026), “Projecting power at home and abroad: domestic state capacity and military capability”, Journal of Peace Research, 63(4), 507-520. DOI: 10.1093/jopres/xjaf018. (OUP Academic)

The authors find that fiscal, administrative and informational state capacity helps countries convert economic resources into military power projection. The effect is stronger under geopolitical rivalry. (OUP Academic)

The limitation matters: this is largely a historical study of power projection, including naval tonnage. It is not a contemporary multidomain readiness index.

But its strategic lesson fits this essay perfectly:

Having resources and being able to mobilise resources are different things.

What paid readers get next

Free readers get the argument. Paid readers get the map.

Below the paywall: the Portugal diagnostic, the methodological warning hidden by the headline ranking and the Research Corner connecting mobilisation to economic growth, defence industry and democratic control.

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Imagine you are a procurement director.

Ten years ago, your job was largely about price, quality and delivery.

Today, you have another list.

Could a tariff hit this supplier? Could sanctions close that route? Should production move to a friendly country? Which technology might suddenly become strategic?

It feels like deglobalization.

But one number makes that story difficult to defend.

Between 1990 and 2025, 193 of 194 countries became more integrated on the AWTY New Globalization measure.

The global mean rose from 3.231 to 5.577 on a 0-10 scale. Even between 2020 and 2025, precisely when trade wars, sanctions, friend-shoring and industrial policy returned to the centre of economic strategy, 154 of 194 countries became more integrated.

• So globalization is not simply ending.

• Something more interesting is happening.

That distinction matters because the wrong diagnosis produces the wrong strategy.

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Fragmentation is real. Deglobalization is too simple.

The standard story has evidence behind it.

Governments are screening foreign investment. Firms are redesigning supply chains. Strategic technologies are being protected. Friend-shoring, reshoring and industrial policy are no longer fringe ideas.

Fragmentation is real.

But fragmentation can happen inside an integrated global economy.

That is what we miss when every tariff becomes evidence that globalization is dying.

The AWTY New Globalization Index does not simply measure how much a country trades.

It combines five dimensions: trade diversification, two-way foreign direct investment, digital trade, economic policy connectivity and cultural or information exchange.

That produces a different map.

• A country can reduce dependence on one supplier and become more connected to five others.

• A company can leave one geopolitical rival without bringing production home.

• A government can restrict one strategic technology while expanding investment and services trade elsewhere.

That is not autarky.

It is rerouting.

Globalization did not disappear. It changed its routing rules.

Integration is not dependence

This is the distinction that matters most.

Openness, integration, concentration and dependence are not the same thing.

• Integration gives an economy access to international flows of goods, capital, technology, information and knowledge.

• Concentration tells us how many alternatives it has.

• Dependence appears when too much rests on one supplier, product, market, technology or route.

A country can therefore become more globally integrated and less strategically dependent at the same time.

The alternative to dependence is not necessarily self-sufficiency.

It can be diversification.

That is why AWTY defines success in this transition as resilient integration, not maximum openness. Countries score better when they participate in international flows without relying excessively on one product, market or channel.

This changes the strategic question.

Instead of asking:

How do we bring everything home?

Ask:

Where are our single points of failure?

That is harder.

It is also much more useful.

What this changes for governments and firms

For governments, industrial policy should distinguish genuine strategic exposure from ordinary international exchange.

Trying to nationalize every supply chain can raise costs and reduce competition without removing the vulnerabilities that actually matter.

For firms, the choice is no longer simply offshore versus reshore.

The better question is where the organization is over-concentrated and which alternative suppliers, markets, technologies and routes would keep it operating after a shock.

For investors, a large number of international links is not itself evidence of fragility.

What matters is their composition.

The emerging competitive advantage is not isolation.

It is diversified access.

Small countries and large markets need different maps

Small economies have an obvious reason to integrate internationally.

Their domestic markets cannot provide every supplier, customer, technology or source of capital.

• In 2025, Ireland scores 8.470, Singapore 7.662 and Luxembourg 7.610.

But large domestic markets have not escaped the same transition.

• The United States scores 7.141, India 6.358 and China 5.963.

This is not a horse race between small and large countries.

Small economies often need dense external networks because they lack domestic scale.

Large economies have more internal options, but still benefit from foreign investment, diversified trade, technology links and digital exchange.

Different size. Different route. Same transition.

Europe tells the story differently again.

Its 2025 regional mean is 7.013, the highest of any region. Africa averages 4.506.

The United States alone scores 7.141, slightly above the European regional average.

Europe relies heavily on dense cross-border connections among relatively small and medium-sized economies.

The United States combines a vast domestic market with extensive external integration.

Neither model resembles self-sufficiency.

And neither proves that integration automatically creates prosperity.

AWTY measures movement toward diversified connectivity. It is not a prosperity, welfare or political approval ranking.

Portugal does not need “more globalization”

Portugal scores 6.997 and ranks 32nd in 2025.

But the headline score hides the useful part.

Trade diversification: 9.366
Foreign direct investment: 8.089
Policy connectivity: 9.110
Cultural globalization: 7.120
Digital trade: 1.301

Portugal already performs strongly across several traditional forms of international integration.

Its obvious bottleneck is digital trade.

That is why rankings should be diagnostics, not trophies.

I might be wrong

There is an important limitation.

AWTY may capture accumulated structural integration better than very recent geopolitical fragmentation.

The 2025 endpoint is an operational estimate. Some KOF components currently end in 2023 and are carried forward. The digital-trade component measures ICT service exports rather than every form of digitally ordered or digitally delivered trade.

New restrictions can therefore appear with a lag.

That matters.

But it does not rescue the simple deglobalization story.

If 193 of 194 countries are more integrated than in 1990, and 154 became more integrated even between 2020 and 2025, we need a better description of the transition.

Globalization is becoming more selective about who connects to whom, through which channels and under what conditions.

One paper worth reading

Thakur-Weigold and Miroudot challenge a central assumption in the resilience debate: that international sourcing is inherently less resilient.

Their analysis questions the automatic equation of foreign supply with fragility. The relevant policy problem is the actual source of vulnerability, including concentration, substitutability and network design.

The article is a Perspective, rather than a global causal test, so that limitation matters.

But its strategic message fits the AWTY evidence closely:

The problem is not connectivity itself. It is poorly designed connectivity.

Thakur-Weigold, B. & Miroudot, S. (2024). “Supply chain myths in the resilience and deglobalization narrative: consequences for policy.” Journal of International Business Policy, 7, 99-111. DOI: 10.1057/s42214-023-00170-3.

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The network survives. The routes change.

The common narrative says countries must now choose between globalization and self-sufficiency.

That is the wrong choice.

The more useful choice is between fragile dependence and diversified integration.

Trade wars do not make global networks irrelevant.

They make the design of those networks more important.

That is why this matters for executives, investors, consultants, professors and policymakers.

The winning question is no longer:

How global are we?

It is:

How easily can we reroute when one connection fails?

If this argument helped you see trade wars differently, subscribe to Polis Doxa or forward this essay to someone making supply-chain, investment or policy decisions.

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Next Tuesday, we move to the sixth transition essay:

The World Is Rearming. But Spending Is Not Readiness.

Because once international integration becomes more geopolitical, another question follows.

Which countries can actually mobilize when the system breaks?

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Every green transition has a photo opportunity.

A new solar park. A line of wind turbines. Another clean-energy project being announced.

These are real signs of progress.

But they are also an easy way to confuse what we can see with what we actually need to change.

Across 194 countries, the AWTY global Green score increased by just 0.376 points in 35 years, from 6.061 in 1990 to 6.437 in 2025.

And the long-run number hides something worse.

In 2025, 76 countries were below their own 1990 Green score. 107 were below where they stood in 2020.

We have become very good at counting visible green investment. We are less good at asking whether the economic system underneath it has changed.

The accepted story is simple: build more renewable capacity and you are winning the green transition.

That story is attractive because renewable capacity is visible, measurable and investable. It also matters enormously.

But it mistakes one input for the whole transformation.

A real green transition requires structural decoupling: economies must create more value with less carbon, less energy, less resource depletion and less destruction of natural capital.

A country can win the renewable-energy race and still lose the green transition.

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The green plateau

The AWTY Green Transition Index does not ask only how much renewable energy a country uses.

It combines five equally weighted dimensions:

Renewable energy. Carbon intensity. Natural-resource depletion. Energy intensity. Forest-cover change.

Together, they ask a harder question.

Is the economy actually becoming cleaner and more resource-efficient?

The global trajectory is surprisingly flat:

1990: 6.061
2020: 6.579
2025: 6.437

And progress has not been universal.

By 2025, 76 of 194 countries scored below their own 1990 Green level.

More strikingly, 107 of 194, or 55%, were below their 2020 level.

A megawatt is an input

This is where the distinction becomes important.

A megawatt of renewable capacity is an input. Structural decoupling is an outcome.

A country can add thousands of solar panels while its factories remain energy-intensive.

• It can replace coal electricity with wind while transport, buildings and industrial heat remain dependent on fossil fuels.

• It can improve energy efficiency while extracting natural resources faster.

• It can reduce domestic carbon intensity while losing forests.

That is why AWTY measures renewable energy in total final energy consumption, rather than simply asking what share of electricity comes from wind, solar or hydro.

The difference is not technical trivia.

Electricity is only part of the energy system. Heating, transport, industry and other uses matter too.

A renewable electricity headline can therefore look spectacular while the transformation of the wider economy remains much slower.

Visible progress versus structural progress

The 2025 ranking makes the point.

Europe has the highest regional Green score at 7.398.

But the Americas average 6.582, Oceania 6.372, Africa 6.124 and Asia 5.806.

And some of the strongest individual performers are not the countries we usually imagine when talking about rich-country green leadership.

At the other end, Oman scores 1.997, followed by Trinidad and Tobago at 2.375 and Turkmenistan at 2.994.

This is not evidence that income does not matter.

It is evidence that income alone does not define the transition.

Energy systems, resource extraction, land use, economic structure and efficiency matter too.

The transition is not simply about who can spend the most on clean technology.

It is about what the entire economy does with energy and natural resources.

Why efficiency is not enough either

There is another trap.

Suppose a new technology lets a firm produce the same output with 20% less energy.

That sounds like a 20% environmental gain.

But what if lower costs lead the firm to produce more?

Or consumers use the savings to consume more elsewhere?

Economists call this the rebound effect.

Efficiency is essential. But an efficiency improvement is not automatically an equivalent reduction in environmental pressure.

This is another reason to measure outcomes rather than announcements.

• Carbon intensity matters.

• Energy intensity matters.

• Resource depletion matters.

• Natural capital matters.

• The system matters.

What the evidence means

For governments, the implication is uncomfortable: stop rewarding the easiest number to announce.

Renewable deployment should remain a priority, but policy should identify the country’s binding green bottleneck.

• For one country, that may be renewable energy.

• For another, industrial energy productivity.

• For another, carbon intensity.

• For a resource exporter, the central problem may be depletion.

• For another economy, it may be forest loss.

A transition strategy built around the metric that generates the best headlines can easily neglect the constraint that actually stops transformation.

For firms and investors, the same logic applies.

A market full of clean-energy projects can still carry serious energy-cost, carbon, resource or natural-capital risks.

A high headline Green score can also hide an important weakness in one dimension.

The useful question is not simply: who is green?

It is: where is the bottleneck?

Limitations: what AWTY is actually measuring

The index also does not measure everything.

It does not directly measure climate vulnerability, adaptation finance or consumption-based emissions. Forest-cover change is based on changes in forest area and has methodological limits.

And AWTY measures transition readiness and management capacity, not prosperity, welfare or political approval.

Those distinctions matter.

I Might Be Wrong

There is a strong objection to this argument.

A multidimensional index can reward structural endowments.

A country blessed with hydropower or forests may have advantages another country does not have. A developing economy undergoing industrialisation may also look worse on some dimensions precisely because it is trying to raise living standards.

That criticism is legitimate.

Equal 20% weights are also a transparent modelling choice, not a claim that every green dimension should have exactly the same political importance everywhere.

But this strengthens the case for looking at the five dimensions rather than abandoning them.

The answer is not to return to one easy metric.

It is to distinguish endowments from policy, compare trajectories as well as levels, make trade-offs visible and recognise the role of climate finance and technology transfer.

The green transition starts where the headline ends

The common narrative says that the green transition is a race to install clean technology.

The evidence suggests something more difficult.

After 35 years, the global AWTY Green score is only 0.376 points higher.

More than half of the 194 countries are below their 2020 position.

Renewables matter enormously.

But the countries that truly transform will be those that use less carbon, less energy and fewer depleted natural resources to generate economic value, while preserving the natural capital on which that economy depends.

If this changed how you think about green leadership, subscribe to Polis Doxa - The Transitions Letter or share this essay with someone still judging the transition mainly by renewable capacity.

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The full AWTY 2026 research package comes together at the 24 September international launch, where we will look at how the five transitions interact.

Next in Are We There Yet?:

Globalization Is Not Ending. It Is Becoming Selective.

The green transition depends on technology, minerals, investment and supply chains.

That takes us directly into the next paradox: the world is talking about deglobalization while becoming integrated in a different way.

Research Corner: one paper before the paywall

Haberl, H. et al. (2020). “A systematic review of the evidence on decoupling of GDP, resource use and GHG emissions, part II: Synthesizing the insights.” Environmental Research Letters, 15(6), 065003. DOI: 10.1088/1748-9326/ab842a.

The distinction at the heart of the paper is crucial for this essay.

Relative decoupling means environmental pressure grows more slowly than GDP.

Absolute decoupling means environmental pressure actually falls while GDP continues to grow.

The authors’ synthesis questions whether observed decoupling has occurred at the scale and speed required to deliver large absolute reductions in resource use and greenhouse-gas emissions.

Why it matters: an economy becoming more efficient is not automatically an economy becoming environmentally sustainable.

Free readers get the argument. Paid readers get the map.

The aggregate Green score tells us whether a country is moving.

The dimensions tell us why.

Below the paywall: why Portugal is an unusually useful example of the difference between visible renewable leadership and structural green performance, the global ranking context, the methodology behind the five dimensions, and two additional research papers.

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Welcome back to Are We There Yet? Seven Essays on a World in Transition.

This edition is completely free to read, except for the Research Corner at the end.

And today I want to challenge one of the most persistent ideas about technology: that more internet, more smartphones and now more AI automatically mean more digital progress.

They do not.

Every one of the 194 countries measured by AWTY has a higher Digital Transition score in 2025 than it had in 1990.

So how can almost four decades of universal digital progress coexist with recent regression in 40% of the countries in the world?

Because we are still using an old definition of the digital divide.

The first divide was about getting connected.

The new divide is about what countries can do after they get connected.

My thesis is simple:

Access without infrastructure quality, skills, digital government and economic use creates a shallow digital transition.

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---

Internet access was the first digital divide

For years, measuring digital development was relatively straightforward.

• How many people use the internet?

• How many mobile subscriptions exist?

• How much broadband coverage does a country have?

Those were sensible questions because connectivity was scarce.

But success changed the problem.

Once large parts of the population are connected, another question becomes more important:

What are people, firms and governments actually able to do with that connection?

That is why the AWTY Digital Transition Index measures five dimensions rather than internet penetration alone:

• internet penetration

• digital infrastructure

• e-government

• digital economy integration

• ICT skills and education

Each represents 20% of the index.

The difference matters.

A country can install fibre without creating competitive digital firms.

It can distribute smartphones without developing digital skills.

It can put administrative forms online without building digital public services that radically reduce the cost of interacting with the state.

And it can become an intensive consumer of digital technology without becoming a significant producer or exporter of digital value.

That is the second digital divide.

Progress can hide a widening gap

There is another reason the 194-country result can mislead us.

Everyone improved, but they did not improve at anything close to the same speed.

• Liechtenstein gained 5.41 points between 1990 and 2025.

• Saudi Arabia gained 5.29.

• The United Arab Emirates gained 5.01.

Compare that with South Sudan, which gained just 1.09 points, or Papua New Guinea, which gained 1.17.

Nobody needs to be “left behind” in absolute terms for a divide to grow.

The frontier can simply move much faster.

The regional numbers make the same point.

• Europe averages 7.304 in 2025.

• Asia reaches 5.849.

• The Americas, 5.644.

• Oceania, 4.657.

• Africa averages 3.750.

That is a 3.55-point Europe-Africa gap on a 0-10 scale.

And this is not simply a story about access.

The African gap reflects cumulative weaknesses across connectivity, infrastructure, digital government, economic integration and human capital.

That combination is much harder to fix than installing another network.

Portugal shows exactly what the new divide looks like

Portugal is one of my favourite examples because the headline score looks good.

Portugal ranks 21st with a Digital Transition score of 7.524.

Look underneath it.

• Internet penetration: 8.849

• Digital infrastructure: 9.524

• E-government: 8.415

• ICT skills and education: 7.970

• Digital economy integration: 2.860

That last number changes the story.

Portugal has almost frontier-level digital infrastructure.

But there is a 6.66-point gap between digital infrastructure and digital economy integration.

This is what headline connectivity statistics cannot tell us.

Portugal’s problem is not primarily getting online.

It is conversion.

And many countries face a different version of the same problem.

But perhaps connectivity still explains most of the story

There is a strong counterargument.

Internet access and infrastructure remain enormous constraints in poorer countries.

They cannot simply be declared yesterday’s problem.

Correct.

The argument is not that connectivity has stopped mattering.

It is that connectivity has stopped being sufficient.

There is also a measurement problem.

The 2025 AWTY endpoint is an operational estimate. Only 10 internet observations and 93 ICT-service observations are directly reported for 2025. Other components rely partly on the latest available official observations.

The Digital Economy Integration measure also cannot capture every domestic platform, software business, informal digital worker or new AI application.

So the fact that 78 countries are below their 2020 score does not prove that technology itself is retreating in 78 countries.

It tells us something narrower and more useful:

Their measured ability to convert technology into broad digital capability has not advanced consistently.

Why this matters now

AI makes this distinction more important, not less.

The temptation will be to repeat the same mistake.

• Count AI users.

• Count corporate adoption.

• Count models.

• Count investment.

Then announce that the AI transition is advancing.

Governments should therefore stop asking only, “How connected are we?”

They should also ask:

• Can firms turn connectivity into scalable economic activity?

• Can citizens turn it into productive skills?

• Can governments turn it into better services?

• Can the economy turn it into internationally tradable value?

Companies should make the same distinction.

A highly connected country may offer plenty of consumers while still lacking the skills, institutions or business ecosystem required to generate deep digital productivity.

That difference matters for investment, recruitment, market selection and strategy.

The common narrative says that more internet, more smartphones and more AI equal a successful Digital Transition.

The evidence points somewhere else.

The first digital divide was about access. The new digital divide is about conversion.

Or, put differently:

Connectivity is infrastructure. Transformation is what societies learn to do with it.

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Next in Are We There Yet?:

The Green Transition Is Not a Renewable Energy Race.

The Digital Transition can move remarkably fast.

The Green Transition is about to show us what happens when visible technological progress meets deep structural inertia.

Research Corner preview

Gaspar, Fernando C. (2026), AWTY 2026 Digital Transition Index: Digital Capacity, Access and Integration Across 194 Countries, Are We There Yet? / Transition Science, 5 August 2026.

The report is the primary evidence source for this essay. It reconstructs the Digital Transition across 194 countries and 36 years using five equally weighted dimensions.

Its most useful contribution is conceptual: it separates connectivity from capability.

Its main limitation is equally important. The 2025 endpoint combines direct observations with carried and estimated values, so short-term movement should not be interpreted as a real-time measure of technological adoption.

Paid readers get the measurement layer behind the argument.

Below: methodology, source provenance, interpretation limits and the data architecture behind the Digital Transition results.

Research Corner

A note on access to the evidence: the underlying AWTY 2026 datasets will be published on Zenodo, with a permanent DOI for citation and reuse. The full AWTY 2026 analytical reports will also be made available to Polis Doxa subscribers as Substack perks. The Research Corner is where I connect those underlying data, methods and reports to the argument you have just read, so paid readers can inspect not only the conclusion, but the evidence behind it.

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Last week, I argued that AI, energy shocks, trade wars, war economy and housing crisis are not five separate crises.

They are five simultaneous transitions.

That creates a harder question.

How do we know which countries are actually managing them well?

GDP per capita tells one story. A digital ranking may crown one country. A green dashboard may crown another. Housing pressure can make both look fragile.

• The problem is not that these indicators are useless.

• The problem is what we ask one number to explain.

One AWTY 2026 Composite ranking sits on top of 174,600 normalized component-level results, covering 194 countries over 36 years.

That is the argument of this essay:

Transition management requires measurement that is multidimensional, historical, comparable and transparent about data quality.

Before we begin:

Your paper, proposal or landing page is excellent. Then the LLM evaluating it can’t extract the value you put into it... You’re gone.

The same can happen to your marketing copy and legal texts: the evidence is there, but the AI misses it or downgrades it.

How to Write for LLM Evaluation helps you learn how I fix this.

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The problem with headline indicators

The accepted narrative is attractive because it is simple.

Find the right indicator. Compare countries. Publish the ranking. Identify the winners.

That works when the question is narrow.

It works badly when the thing being measured is a structural transition.

A country can grow while housing becomes less affordable.

• It can build digital infrastructure without creating digital skills or digital exports.

• It can add renewable energy while remaining dependent on inefficient resource use.

• It can increase defence spending without developing the industrial and institutional capacity required for sustained mobilisation.

Every individual indicator can be correct.

The diagnosis can still be wrong.

The ranking behind the ranking

AWTY 2026 covers 194 countries from 1990 through 2025, a period of 36 years.

That gives us:

194 countries × 36 years = 6,984 country-year observations

For every country-year, AWTY measures five transition vectors:

Digital Transition
Green Transition
New Globalization
War Economy
Housing Crisis Management

That produces:

6,984 × 5 transitions = 34,920 vector scores

Each vector contains five dimensions.

So:

34,920 × 5 dimensions = 174,600 normalized component scores

There is an important caveat.

Those 174,600 scores are not 174,600 unique raw observations collected from data providers. They are normalized component-level results inside the AWTY measurement architecture.

The purpose of the number is not to impress you with dataset size.

It is to reveal what a ranking hides.

A ranking is useful only when you can see what it compresses.

Why put everything on a 0-10 scale?

Because internet access, carbon intensity, trade diversification, military mobilisation and housing pressure cannot be averaged in their original units.

AWTY converts the underlying measures into a common 0-10 scale.

That makes comparison and aggregation possible.

But it does not make the five transitions identical.

A 7 in Digital does not have the same social meaning as a 7 in War Economy.

In particular, a higher War Economy score means greater mobilisation capacity or intensity. It does not mean that a country is better, safer or more prosperous.

The final Composite gives each transition an equal 20% weight.

Why equal weights?

Not because 20% is some statistical law.

It is a transparent normative choice.

There is no assumption-free Composite Index. Dimensions have to be selected. Normalization rules have to be chosen. Weights have to be assigned.

The real choice is often between visible assumptions and hidden ones.

AWTY makes the weights explicit and keeps the five underlying transition scores available for inspection.

And what exactly is a 2025 “operational estimate”?

This matters because rankings often look more precise than the underlying data.

Official databases do not update simultaneously.

Some already contain observations for 2025. Others stop in 2024, 2023 or earlier.

So AWTY calls the 2025 endpoint an operational estimate.

It uses the latest available official evidence and can include direct observations, within-country interpolation, carried values and documented estimates.

It is not a forecast.

And it does not mean that every component was directly observed during 2025.

This is especially important when someone sees a small change between 2024 and 2025 and declares a global reversal.

The data may support movement.

They may not support that level of certainty about its timing.

AWTY therefore also reports data confidence.

For the 2025 Composite, 157 countries are Confidence A, 28 are B and 9 are C.

Confidence describes provenance and coverage.

• It does not describe success.

A country can have a high-confidence bad result or a lower-confidence good one.

• Completeness is not the same as certainty.

I might be wrong

There is a serious objection to composite indices.

They can create false precision.

Choose the dimensions. Normalize them. Assign weights. Calculate a score to three decimal places.

Suddenly, contestable choices look like objective truth.

That criticism is valid.

The answer is not to pretend measurement is neutral.

The answer is to expose the choices and test whether they matter.

Take Housing.

If Housing is removed entirely from the AWTY Composite, the alternative ranking still has a 0.975 correlation with the baseline.

So the broad pattern remains remarkably stable.

But the average country moves 9.3 ranking places.

Both facts matter.

The system is robust enough that removing one transition does not destroy the result.

But the choice of what we measure still changes what we see.

That is exactly why methodology should be visible.

Why this matters

Governments, firms, investors and researchers do not need another trophy table.

• They need to find the bottleneck.

The common narrative says that one or two headline indicators can tell us which countries are winning.

• I think that is the wrong question.

Structural transitions require multidimensional and historical measurement because countries can move forward in one area while becoming trapped in another.

AWTY is not a prosperity ranking.

• It is not a welfare index.

• It is not a political approval score.

• It measures transition position and management capacity across five simultaneous structural changes.

Most people have opinions about change. Polis Doxa measures it.

And this is the sentence I would keep:

A ranking is useful only when you can see what it compresses.

Share

If this argument was useful, share it with someone who still treats GDP growth or one annual ranking as a complete diagnosis.

And I would like to hear your answer to one question:

If you could change the five equal 20% weights, which transition would you give more importance to, and why?

Leave a comment

Next in the series:

The Digital Divide Is No Longer About Internet Access.

Refer a friend

Research Corner

The full essay above was free. Research Corner is for paid Polis Doxa subscribers.

1. Martin Ravallion: the danger of the “mashup index”

Ravallion, Martin (2012), “Mashup Indices of Development”, The World Bank Research Observer, 27(1), 1-32. DOI: 10.1093/wbro/lkr009

Finding: Composite indices can conceal assumptions about weighting, substitution between dimensions and the value assigned to different outcomes.

Why it matters here: AWTY should not ask readers to trust one final number. The underlying transition vectors, weights, provenance and limitations must remain visible.

Limitation: Ravallion’s critique warns against poorly founded aggregation. It does not establish that multidimensional measurement itself should be abandoned.

Read more

Not a map of borders or GDP.

A map of how 194 countries are navigating the five transitions already reshaping prosperity, power and risk: AI, energy shocks, trade wars, the war economy and the housing crisis.

Today, I am releasing the first results from Are We There Yet? 2026.

And one number immediately stood out.

In 2025, 132 of the 194 countries measured by AWTY scored below their own 2020 level.

That is more than two-thirds of the countries in the world.

This happened after three decades of remarkable progress. The global AWTY Composite rose from 3.555 in 1990 to 4.941 in 2020 on a scale from 0 to 10.

By 2025, it had slipped to 4.870.

Almost every country is still better positioned than it was in 1990.

But most are now behind where they stood only five years ago.

So what happened?

The five-crisis story is wrong

The accepted explanation is familiar.

AI is one problem.

Energy shocks are another.

Trade wars belong to trade policy.

The war economy belongs to defence.

Housing belongs to cities, planning and mortgages.

Each has different experts, ministries and political debates.

That division makes administrative sense.

Economically, it can be disastrous.

The world does not have five separate crises. It has five structural transitions moving at different speeds and increasingly blocking one another.

Between 1990 and 2025, 193 of the 194 countries in AWTY improved their Composite score.

The world clearly moved forward.

But progress peaked around 2020.

That is why the 132-country reversal matters more than the small decline in the global average.

It suggests that countries have not stopped changing.

They are finding it harder to make the changes work together.

You can separate ministries. You cannot separate the economy.

Consider AI.

A country can build excellent digital infrastructure and attract data centres.

But AI needs electricity.

Its firms may need semiconductors controlled by foreign suppliers.

Trade wars can restrict access to those technologies.

The war economy can compete for the same engineers, factories and public budgets.

And if workers cannot afford housing near technology clusters, the jobs and skills cannot move where they are needed.

The digital transition may be advancing.

The economy may still be stuck.

The same mechanism works in every direction.

Green industry depends on technology, trade and investment.

Defence production depends on energy, skills and industrial capacity.

New trade patterns reshape where firms invest.

Housing determines whether people can physically reach the opportunities created by the other transitions.

I call this a transition trap: progress in one transition cannot produce its full benefits because another has become the binding constraint.

The danger is not change itself. It is advancing in one transition while remaining trapped in another.

That is the central idea behind the AWTY Composite.

There are no complete winners

One result makes this especially clear.

No country reaches 6.5 out of 10 in the 2025 Composite.

• Ireland leads with 6.400.

• Finland follows at 6.398.

• Uruguay scores 6.374.

• Sweden reaches 6.364.

Only 0.036 points separate the top four.

And they do not succeed in the same way.

• Ireland leads the Digital Transition.

• Uruguay leads the Green Transition.

• Israel leads the Transition to a New Globalization.

• The United States leads the Transition to a War Economy.

• San Marino has the highest Housing Crisis Management score, although its housing result carries a lower confidence class.

Nobody leads everything.

That may be the most important finding in AWTY 2026.

Leading one transition is not the same as managing five.

Europe comes closest at regional level. Its 2025 Composite average is 5.929, compared with 5.019 in the Americas, 4.880 in Asia and 3.961 in Africa.

But even Europe remains far from a complete transition frontier.

I might be wrong

There is a serious objection to this argument.

Perhaps we are combining problems that should remain separate.

Digital performance has its institutions. Housing has others. Energy depends on geography. Defence choices reflect geopolitics. Trade has its own logic.

And the AWTY Composite gives each transition a 20 percent weight.

That is a transparent choice, not a law of nature.

There is another important warning.

AWTY does not measure prosperity, welfare or political approval.

A wealthy country can be badly unbalanced. A poorer country can be moving rapidly.

And a higher War Economy score means greater mobilisation capacity or intensity. It does not mean that militarisation is socially desirable.

The 2025 values are also operational estimates. Depending on the indicator, they may combine direct observations, interpolation, carried official data and documented estimates.

So the result should not be read as proof that coordination failure caused every national decline.

That is my interpretation of the pattern, not the measurement itself.

The map we need

The common narrative asks:

Which crisis should we solve first?

I think that is increasingly the wrong question.

The better question is:

Which transition is preventing the others from producing results?

• Governments should ask it before approving major investments.

• Companies should ask it before choosing technologies, suppliers or locations.

• Households already experience the answer.

They do not live five separate transitions.

They experience one reality made of rent, energy prices, employment, taxes, technology and the cost of everyday goods.

That is why the post-2020 slowdown matters.

The old economic map separates the problems.

The new map must show the connections.

Which transition is currently blocking progress in your country, company or career?

Tell me in the comments or reply directly.

Leave a comment

Get the weekly Post on AI/digital transition, energy shocks/green transition, trade wars/transition to a New globalization, Transition to a War Economy and housing crisis.

Subscribe now

Next in Are We There Yet?: Are We Measuring the Wrong Things?

I will open the Composite and show what sits behind one number: 194 countries, 36 years and 174,600 normalised component scores.

Research Corner

Research behind this essay
This essay draws on the forthcoming AWTY 2026 Global Transitions Composite Index, covering 194 countries from 1990 to 2025. The full research package will be released with the international AWTY 2026 launch on 24 September.

Gaspar, Fernando C. (2026), AWTY 2026 Global Transitions Composite Index: How Countries Are Navigating Five Simultaneous Economic Transitions, Are We There Yet? / Transition Science, version 5 August 2026. Forthcoming.

Paid readers get the map

Below the paywall: what the leading countries reveal, why Europe’s advantage should not produce complacency and the research behind the idea that transitions become dangerous when they interact.

Read more

There is a new hidden evaluator between your work and the person who decides its value.

You submit an academic paper with an original contribution.

• A grant proposal with strong evidence.

• A landing page with the right price, features and proof.

• A legal text containing the qualification that changes the conclusion.

Then someone asks an LLM to summarise it, compare it, screen it, extract the relevant facts or recommend what to do next.

The machine does not carry your complete work into that decision.

It constructs a compressed version.

• Some claims survive.

• Some evidence becomes separated from the conclusion it supports.

• Some numbers disappear.

• Some qualifications are treated as irrelevant.

• Some facts remain visible on the page but never enter the answer.

The human may still make the final decision.

But increasingly, that human is not starting with your work.

The human is starting with what the machine says your work contains.

This creates a new form of professional risk.

• It is not simply hallucination.

• It is not plagiarism.

• It is not even job replacement.

• It is value erasure.

Your strongest evidence can be present and still disappear during machine extraction.

Your work is no longer judged only by what it contains. It is judged by what the machine manages to extract from it.

That is the transition you must notice.

Your work can be excellent. AI can still downgrade it.

The common fear is that artificial intelligence will eventually replace human evaluators.

That already happens in some contexts.

But it is not the most immediate danger.

The immediate danger is that AI enters the process before the final decision.

• It retrieves your work.

• It segments it.

• It extracts claims.

• It connects, or fails to connect, evidence to conclusions.

• It creates a summary.

• It compares that summary with criteria, competing proposals, other papers or alternative products.

The human evaluator may still make the final decision.

But the human may be deciding on the basis of a machine-generated representation of your work.

And that representation may be weaker than the original.

AI is already inside academic evaluation

This is not a distant scenario.

A study of peer review at ICLR 2024 estimated that at least 15.8% of reviews had been written with AI assistance.

In comparable pairs of reviews of the same paper, the AI-assisted review gave the higher score in 53.4% of cases.

Papers close to the acceptance threshold that received an AI-assisted review were 4.9 percentage points more likely to be accepted.

We should treat these estimates carefully.

But the direction is clear.

Grant evaluation is the more dangerous grey zone

I cannot support the claim that every grant evaluator, or every EU evaluator, is using an LLM.

The evidence does not justify that statement.

But major funding institutions consider the risk sufficiently real to regulate it.

• The NIH prohibits reviewers from using generative AI to analyse applications or formulate critiques.

• UKRI states that assessors must not ask AI to understand, summarise or evaluate an application.

• The European Research Council now explicitly prohibits reviewers from delegating the summary or scientific assessment of proposals to AI systems.

These prohibitions are not proof of widespread use.

They are something else:

an institutional signal that informal or unauthorised AI assistance is operationally plausible, difficult to observe and important enough to govern.

That is why my earlier post, Marketing to Machines, argued that grant writing now has a second audience.

Websites now have two audiences

The same transition is taking place outside formal assessment.

Your website was designed to persuade a human.

Now it must also brief an agent.

Kevin Indig and David Kaufman gave AI agents three buying tasks involving 100 B2B products:

• find pricing and features;

• find integrations;

• find security and compliance information.

Each task was run five times, producing 1,500 agent runs.

Pricing and features were the breaking point.

Only 79% of the answers remained based on the vendor’s own website, compared with 93% for integrations and 92% for security.

Even when a numerical price was publicly displayed, the agent still cited a third-party source in 18% of the runs.

When access errors occurred, third-party fallback rose from 17% to 77%.

The price could be on the page.

The agent could still fail to extract it, trust it or cite it.

When that happens, the agent does not necessarily stop.

It goes somewhere else.

• A directory.

• A comparison site.

• An old blog post.

• A partner page.

• A third party now explains your offer for you.

Official guidance reflects the same architecture.

OpenAI says websites must allow OAI-SearchBot access for their content to be discovered, presented, summarised and cited in ChatGPT Search.

Google says that pages must be indexed and eligible for snippets before they can appear as supporting links in AI Overviews or AI Mode.

Your website is no longer only a showroom.

It is becoming a source file for machines.

The Three Extraction Gates

I ran workshop revealing that most failures can be organised around three gates.

1. Disclosure

Is the decisive fact stated explicitly?

• “Flexible pricing” is not a price.

• “Our project will generate significant impact” is not an indicator.

• “This paper contributes to the literature” is not a contribution.

The evaluator should not have to invent the factual statement you failed to make.

2. Extractability

Can the machine connect the fact to the claim it supports?

A proposal may explain the problem on page four, the solution on page twelve and the indicator in an annex.

A human expert will probably connect them.

The machine may return three disconnected fragments.

• The information is present.

• The evaluation logic is not.

3. Accessibility

Can the system retrieve the information at all?

The evidence may be trapped inside:

• a screenshot;

• an ambiguous table;

• a scanned PDF;

• a JavaScript calculator;

• a collapsed toggle;

• an inaccessible webpage.

A study of around 7,000 company webpages found that heterogeneous site structures and large numbers of irrelevant pages made direct product-information extraction by LLMs costly and inefficient.

This is the context required to understand the sentence:

Present is not the same as extractable.

But humans still make the decision

This is the strongest counterargument.

Most important papers, grants, legal texts and strategic documents are not yet being judged autonomously by an LLM.

Correct.

But the final decision is only one stage in the chain.

The system that controls retrieval, extraction, summarisation and comparison can shape what the decision-maker sees before the final judgment begins.

This is another form of the power that creates Algorithmic Rents.

The platform does not need to own your paper, proposal, website or product.

It only needs to control its visibility and representation.

That is also why Private Sovereignty matters.

Platforms are gaining the practical power to determine which claims become visible, credible and actionable.

Public institutions are then asked to regulate processes they cannot fully observe, reinforcing The Governability Crisis.

Why it matters

For professionals, the immediate risk is not simply job replacement.

It is value erasure.

• Your strongest evidence exists.

• Your original contribution exists.

• Your measurable result exists.

• Your decisive qualification exists.

But it disappears during machine extraction.

The danger is especially serious for work whose value depends on relationships between distant parts of a document:

• a criterion and the evidence answering it;

• a research gap and the contribution closing it;

• an obligation and the exception modifying it;

• a product claim and the factual specification supporting it.

If those relationships remain implicit, the machine may construct a weaker version of your work.

The common narrative

Most people believe the transition is:

Humans evaluate work today.
AI will evaluate work tomorrow.

My thesis

The real transition has already started:

AI is constructing the version of your work that humans (or machines) evaluate.

The power shift happens before the verdict.

It happens when the model decides what your work contains.

The shareable insight

Your work is not being judged only by what it contains. It is being judged by what the machine can extract from it.

Which type of work are you most concerned about: academic papers, grant proposals, websites, marketing copy, legal texts or strategic reports?

Reply or leave a comment. I am mapping where this transition is creating the greatest professional risk.

Leave a comment

Next week, I will show how to run a five-minute extraction test on one real section of your work.

Free readers get the argument. Paid readers get the evidence map.

Below, the Research Corner separates documented use, experimental evidence and institutional signals.

It also identifies what the research allows us to conclude, and what it does not.

The practical implication

Do not replace human writing with machine writing.

Use a two-pass process.

Pass 1: machine legibility

Make explicit:

• the criterion;

• the claim;

• the evidence;

• the measurable result;

• the indicator.

Pass 2: human persuasion

Restore:

• narrative;

• voice;

• tension;

• relevance;

• memorable language.

Apply the complete method

How to Write for LLM Evaluation is a seven-step digital experience for papers, grant proposals, websites, landing pages, marketing copy and professional texts.

You finish with one real section from your own work rewritten, tested and prepared for LLM-assisted evaluation.

The First Edition includes seven written lessons, four downloadable PDFs, six worksheets, ten copy-and-paste prompts, an anonymised before-and-after case and the final evaluation checklist.

€299. Enrollment closes on 31 August 2026. Access opens on 1 September 2026.

Join How to Write for LLM Evaluation

Research Corner

1. AI-assisted peer review can change outcomes

Russo Latona, G., Ribeiro, M. H., Davidson, T. R., Veselovsky, V., & West, R. (2024). “The AI Review Lottery: Widespread AI-Assisted Peer Reviews Boost Paper Scores and Acceptance Rates.”

Paper and full reference

Finding

The authors estimated that at least 15.8% of ICLR 2024 reviews involved AI assistance.

AI-assisted reviews were more likely to give higher scores, and receiving one was associated with a 4.9 percentage-point increase in acceptance among borderline papers.

Limitation

AI use was inferred using detection methods rather than observed directly.

The study establishes consequential association in one machine-learning conference, not universal behaviour across all scientific fields.

Strategic interpretation

The relevant unit of analysis is no longer only the paper.

It is the paper-reviewer-AI system.

A manuscript can receive a different outcome because of how an AI-assisted reviewer represents it.

Read more

It may not make the final decision.

But it can retrieve your work, divide it into fragments, extract selected claims, summarise the evidence and construct the version that reaches the human evaluator.

That version may be weaker than the original.

Your contribution may disappear.

Your evidence may become disconnected from the claim it supports.

Your measurable results may be reduced to vague promises.

A decisive fact may remain visible on your website while never entering the answer produced by the AI agent.

This episode of InnovEU, the EU Project Chronicles is built around selected excerpts from my recent live workshop on LLM-assisted evaluation.

Listen to the episode

In the episode, I examine how machine mediation is already affecting:

• academic peer review;

• grant proposals and funding applications;

• websites and landing pages;

• legal and professional documents;

• marketing and product information.

The evidence is not equally strong in every area.

Academic peer review already offers documented examples of AI-assisted reviews influencing the content of evaluations.

AI-assisted grant screening is emerging, contested and increasingly regulated.

Websites and landing pages face a different form of evaluation: AI agents decide whether facts are discoverable, extractable, trustworthy and strong enough to enter a comparison or recommendation.

Different settings.

The same underlying risk.

Your work is not being judged only by what it contains. It is being judged by what the machine manages to extract from it.

The machine version of your work

During the workshop, I demonstrated two versions of the same grant-proposal section.

The project was identical.

The problems were identical.

The activities, outputs and measurable results were identical.

But the first version required the evaluator to infer the relationship between them.

The second made the evaluation architecture explicit:

Criterion → gap → objective → activity → output → target → indicator

The project did not improve.

The evidence did not improve.

The machine representation improved.

That distinction matters because a weak extraction can turn excellent work into a weak verdict.

The Three Extraction Gates

The episode introduces three questions every important claim must survive.

Disclosure

Is the decisive fact explicitly stated?

“Flexible pricing” is not a price.

“This project will create significant impact” is not an indicator.

“This paper contributes to the literature” is not a contribution.

Extractability

Can the system connect the claim to the evidence supporting it?

A problem on page four, a solution on page twelve and an indicator in an annex may remain three unrelated fragments.

Accessibility

Can the system retrieve the information at all?

The evidence may be trapped in a screenshot, scanned PDF, inaccessible webpage, collapsed menu, calculator or ambiguous table.

The information can be present and still be functionally absent.

Present is not the same as extractable.

Writing for machines without sounding like one

The answer is not to remove your voice.

It is not to flatten every paragraph into repetitive machine prose.

It is not to manipulate an evaluator through hidden prompts.

The answer is a two-pass process.

Pass 1: machine legibility

Make the following elements explicit:

• criterion;

• claim;

• evidence;

• measurable result;

• indicator.

Pass 2: human persuasion

Restore:

• narrative;

• voice;

• relevance;

• tension;

• memorable language.

First make the value extractable. Then make it unforgettable.

Read the companion Decision File

The full evidence map, academic references, documented cases, limitations and strategic implications are available in the companion Polis Doxa Decision File:

Your Work Is Not Being Judged. Its Machine Version Is.

The article distinguishes between:

• documented use;

• experimental evidence;

• institutional signals;

• emerging practice;

• strategic inference.

That distinction matters.

Guru guessing is not strategy.

Related Polis Doxa reading

Marketing to Machines explains why professional writing now has a second audience.

Algorithmic Rents examines how platforms gain power by controlling visibility and allocation without owning the underlying assets.

Private Sovereignty explores how platforms acquire practical authority over decisions once reserved for public institutions.

The Governability Crisis explains why governments may be regulating visible AI systems while missing the economic structures beneath them.

Apply the method to your own work

How to Write for LLM Evaluation is a seven-step digital experience for papers, grant proposals, websites, landing pages, marketing copy and professional texts.

You finish with one real section from your own work rewritten, tested and prepared for both machine extraction and human judgment.

The First Edition includes:

• seven written lessons;

• four downloadable PDFs;

• six practical worksheets;

• ten copy-and-paste prompts;

• one anonymised before-and-after case;

• the final LLM evaluation checklist.

Enrollment closes on 31 August 2026.

Access opens on 1 September 2026.

Join How to Write for LLM Evaluation

Get the weekly Decision File on AI/digital transition, energy shocks/green transition, trade wars/transition to a New globalization, war economy/Transition to a War Economy and housing crisis.

For the past three weeks, I have followed a chain of economic power.

• First, markets became programmable.

• Then, controlling that programming created algorithmic rents.

• Next, platforms began to exercise something close to private sovereignty.

Now we reach the final consequence.

The institutions created to govern the economy are losing contact with the object they are supposed to govern.

Governing the wrong economy

Imagine trying to regulate a railway by inspecting the price of every ticket while a private company quietly redesigns the tracks.

That is increasingly what economic governance looks like.

Regulators inspect prices, contracts, market shares, employment relationships and data-processing practices.

Meanwhile, platforms redesign the architecture through which prices, contracts, work, information and economic opportunities are produced.

A market is not an architecture

A market connects buyers and sellers.

An architecture determines:

• who may enter;

• what they can see;

• how they are ranked;

• whom they can reach;

• which options are available;

• what information they receive;

• what happens when they break the rules.

A market coordinates mainly through prices.

An architecture coordinates through code, ranking, prediction, defaults, permissions and continuous feedback.

• It can change visibility without changing the price.

• It can reduce access without formally excluding anyone.

• It can redirect demand without issuing an order.

• It can sanction a worker, seller or creator without entering a courtroom.

• It can change the economic environment overnight.

Regulation is not standing still

It would be wrong to say governments are doing nothing.

The European Union has built a substantial digital regulatory system.

• The Digital Markets Act identifies large platforms as gatekeepers and imposes obligations before authorities have to prove a new competition violation in every individual case.
  As of July 2026, the Commission lists 23 designated core platform services.

• The AI Act entered into force in August 2024 and is moving through its implementation phases, with major obligations and enforcement powers taking effect over several years.

• The Platform Work Directive addresses employment status and algorithmic management.
  It gives workers greater transparency and the right to contest some automated decisions.
  The Council estimates that more than 28 million people work through digital labour platforms in the EU.

These measures matter.

• Antitrust is necessary.

• Privacy regulation is necessary.

• Labour law is necessary.

• AI regulation is necessary.

But necessary does not mean sufficient.

The system disappears between the regulators

Each branch of regulation sees a different object.

• Competition law sees a dominant firm.

• Privacy law sees a data controller.

• Labour law sees an employer, contractor or intermediary.

• Consumer law sees a transaction.

• AI regulation sees a model, application or risk category.

The person using the platform encounters all of them at once.

Consider a driver working through an app.

• Labour law can decide whether the driver is genuinely self-employed.

• Privacy law can limit how personal and location data are processed.

• AI rules can require transparency or human oversight in some automated decisions.

• Competition authorities can investigate commissions or exclusionary behaviour.

Yet the driver’s income may still be governed minute by minute by an integrated architecture that controls:

• job allocation;

• route visibility;

• performance scores;

• incentives;

• waiting times;

• geographical priorities;

• access to customers;

• suspension risk.

The economic outcome emerges from the architecture.

Owning the product is no longer enough

The same problem affects sellers.

A merchant may own the product, warehouse and brand.

But the platform can control:

• search ranking;

• customer recommendations;

• advertising access;

• review visibility;

• transaction data;

• fulfilment conditions;

• platform fees;

• eligibility for preferred placement.

The merchant owns the product.

The platform owns the path to the customer.

Central banks face their own governability problem

Central banks also govern through models.

They estimate inflation, employment, productivity, investment and the transmission of interest rates through the economy.

Those models are not merely academic.

They shape decisions affecting mortgages, business investment, asset values and government financing.

But AI and platform capitalism are changing the mechanisms that the models try to represent.

• Prices can change more frequently.

• Offers can be personalised.

• Demand can be redirected by recommendation systems.

• Expectations can be shaped by curated information.

• Credit decisions can be automated.

• Investment can move rapidly towards AI-related infrastructure and assets.

Central banks are responding by adopting machine learning and generative AI for economic analysis, nowcasting, statistical production, supervision and monetary-policy research.

BIS research published in 2026 describes a growing range of central-bank applications, including inflation analysis and real-time economic assessment.

This is progress.

It is also revealing.

The institutions that govern the economy through models increasingly need AI to improve their view of an economy being transformed by AI.

When behaviour is designed

Traditional models usually treat consumer preferences as something that exists before the market interaction.

• The consumer arrives with preferences.

• The market reveals them.

• The company responds.

Platform capitalism reverses part of this sequence.

The platform observes behaviour.

• It tests different messages, rankings and recommendations.

• It predicts reactions.

• It modifies the choice environment.

• It learns from the response.

• Then it repeats the process across millions of people.

Preferences are no longer merely revealed.

They are continuously influenced, reinforced and sometimes constructed.

This does not make traditional demand theory useless.

It makes it incomplete.

A model that sees the final purchase but ignores the architecture that produced the choice will misunderstand both demand and power.

Algorithmic pricing reveals the category error

Traditional competition analysis looks for firms setting prices, communicating with rivals or abusing a dominant position.

Algorithmic pricing complicates these categories.

A pricing system can monitor demand, inventory, consumer behaviour and competitor prices continuously.

Several businesses can rely on similar optimisation software.

Prices can converge without executives meeting in a room.

Different consumers may receive different offers for the same underlying product.

The economic question is no longer only whether firms colluded.

It is also whether a shared or interacting technological architecture produced an outcome that resembles coordination.

The obvious answer

The common response is that regulation needs to become faster.

Governments should:

• hire more technical experts;

• create regulatory sandboxes;

• use flexible frameworks;

• increase data access;

• coordinate agencies;

• update legislation more frequently;

• apply AI to regulatory work.

All of this would help.

The speed gap is real.

Many public institutions lack the staff, data, computing capacity and specialist knowledge available to the companies they regulate.

But speed cannot solve a category error.

• A regulator can move quickly and still regulate the wrong object.

• A law can be flexible and still depend on an obsolete model of how economic coordination works.

• An authority can understand an algorithm without understanding the architecture in which it operates.

Faster regulation is useful.

Faster misclassification is not.

The deeper lag

The usual diagnosis is regulatory lag.

• Technology changes.

• The law follows.

• By the time the law arrives, the technology has moved again.

That diagnosis is correct, but incomplete.

Beneath regulatory lag sits a more serious problem: model lag.

Governments cannot govern systems they cannot represent adequately.

And the discipline expected to provide those representations still relies heavily on assumptions inherited from a different economy:

• firms operate inside markets;

• prices are the principal coordination mechanism;

• preferences are formed outside the market;

• labour is exchanged through identifiable contracts;

• market power appears mainly through price, output or ownership;

• regulation is imposed externally through law.

Platform + AI capitalism changes every one of these assumptions.

• Firms can design the market.

• Ranking can coordinate more strongly than price.

• Preferences can be shaped within the system.

• Labour can be continuously allocated by algorithms.

• Power can come from access, visibility and infrastructure.

• Rules can be embedded directly into software.

• Sanctions can be automated and instantaneous.

Economics must be reprogrammed

This is not an argument for abandoning economics.

It is an argument for taking economics seriously enough to reconstruct it.

• Smith still matters.

• Keynes still matters.

• Hayek, Schumpeter, North, Stiglitz and the economics of information still matter.

• Prices, incentives, institutions and competition still matter.

But economics cannot treat platforms, data and algorithms as small additions to a fundamentally unchanged system.

They are changing the objects being studied.

• Platforms are becoming economic infrastructures.

• Algorithms are becoming allocation mechanisms.

• Data is becoming a source of persistent power.

• Design is becoming a form of governance.

The old economic map is not completely wrong.

It is increasingly incomplete.

The book I am trying to publish with my friend and co-author Vitor Lima (anyone knows an agent?) starts from that structural mismatch.

Economics was built largely to explain markets coordinated through prices and human decisions.

Platform + AI capitalism requires models that also explain architectural power, algorithmic agency, endogenous preferences and continuous control.

Why it matters

• Executives need to know where competition is actually being organised.

• Investors need to distinguish temporary profits from durable architectural rents.

• Workers and firms need to recognise when they are entering a market and when they are becoming dependent on an infrastructure.

• Governments need to understand why strong regulation can still produce weak control.

The same problem reaches beyond AI.

• Energy shocks increasingly travel through digitally managed systems.

• Trade wars now target semiconductors, cloud infrastructure, data and technical standards.

• The war economy depends on privately controlled dual-use technologies.

• The housing crisis is increasingly mediated by property platforms, automated valuations and data-driven allocation.

The five transitions expose the same vulnerability:

The common narrative

Technology is advancing too quickly.

The answer is faster regulation, better sandboxes, more flexible frameworks, stronger AI laws and greater technical capacity.

My view

Speed matters.

But the deeper problem is intelligibility.

Governments are trying to govern programmable architectures using theories built mainly for decentralised markets.

The governability crisis is therefore not only a regulatory crisis.

It is an epistemological crisis.

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You cannot regulate what you cannot model.

A practical consequence

The same intelligibility problem now affects your own work.

An EU proposal, academic paper, legal argument or marketing document may be read, summarised and compared by an LLM before a human reaches a decision.

The evidence may be present.

But that does not mean the system will extract it correctly.

How to Write for LLM Evaluation is a seven-step digital experience in which you rewrite and test one real section from your own work.

Prepare your work for LLM evaluation

The question for you

Where do you see the largest governability gap?

• In competition?

• Work?

• Finance?

• Education?

• Public administration?

• Or somewhere else?

Reply or leave a comment. I read every answer.

Leave a comment

What comes next

July began with programmable markets and ended with institutions struggling to govern them.

In August, I will move from the failure of the old economic map to the decisions people and institutions make when no stable map exists.

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Research Corner

Research Corner asks a narrower question:

What does peer-reviewed research actually establish about platform governance, algorithmic power and the limits of current regulatory approaches?

The five papers below do not prove every part of my thesis.

They do something more useful.

They show where the evidence is strong, where the conceptual gaps remain and why “faster regulation” cannot be the entire answer.

Research Corner: What the Literature Can See

1. Platforms do not simply participate in markets

Full reference

Teh, T.-H. (2022). “Platform Governance.” American Economic Journal: Microeconomics, 14(3), 213-254. DOI: 10.1257/mic.20190307.

Research question and method

Teh develops a formal microeconomic model of platform governance.

The paper moves beyond platform pricing and examines non-price decisions such as:

• seller entry;

• information provision;

• recommendations;

• quality standards;

• search design.

The central question is whether a platform has incentives to design its marketplace in ways that improve welfare.

Main finding

The platform’s governance choices can distort the degree of competition between sellers.

Depending on the fee structure, the platform may induce either too much or too little seller competition.

This is important because the distortion does not arise only from the price charged by the platform.

It comes from the way the platform designs participation, information and search.

The platform is therefore not simply another firm responding to market conditions.

It helps create those conditions.

Limitation

This is a theoretical model.

It clarifies mechanisms but does not estimate their empirical magnitude across real platforms.

It also focuses on marketplace governance rather than the wider political and institutional power exercised by large technology ecosystems.

Strategic interpretation

Competition policy traditionally studies firms operating inside markets.

Teh’s model gives economic support to a different view: the platform is partly a market governor.

The implication is direct.

Regulating fees without examining entry, search, recommendations and information flows may leave the central source of power untouched.

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Read more

For the past two weeks, I have been building a single argument.

• First, markets became programmable.

• Then, controlling the programme created algorithmic rents.

• Today, we reach the political consequence.

When a company controls the market, writes its rules and punishes those who break them, is it still only a company?

Paid readers will find the academic research behind this argument in the Research Corner below.

A private company now governs a population larger than any country

Meta estimates that 3.56 billion people used at least one of Facebook, Instagram, Messenger or WhatsApp every day in March 2026.

That daily population is more than twice the size of China or India.

Those people did not vote for Meta.

Yet Meta can decide which messages reach them, which disappear, which accounts are suspended and which businesses gain visibility inside its platforms.

This is not merely a social-media problem.

Platforms increasingly determine:

• who enters a market

• who reaches customers

• who appears in a search

• who receives a payment

• who can monetise an audience

• who is promoted

• who becomes invisible

We still describe this as market power.

That description is becoming too small.

From programmable markets to private sovereignty

On 10 July, I argued that the market is the algorithm.

Platforms no longer merely help buyers and sellers find each other. They design the market, organise its information and programme its outcomes.

On 17 July, I argued that this architecture creates algorithmic rents.

The new rent does not require ownership of every asset inside the market. It comes from controlling access, visibility, ranking, data, matching and participation.

Private sovereignty is the next step.

Once a platform controls the rules, the rents and the sanctions, it begins to perform functions traditionally associated with government.

• It does not need territory.

• It has an ecosystem.

• It does not need borders.

• It has interfaces.

• It does not need legislation.

• It has terms of service.

• It does not need police.

• It has automated enforcement.

The platform writes the law

A state publishes laws.

A platform publishes terms of service.

These documents are legally contracts, not constitutions.

But inside the ecosystem, they perform something close to a constitutional role.

They establish:

• who can participate

• what behaviour is permitted

• what content can circulate

• what commercial practices are allowed

• when the platform may change the rules

• how an account can be suspended

The difference is not only democratic legitimacy.

It is also speed.

Public law normally requires procedures, institutions and opportunities for challenge.

Platform rules can be executed immediately.

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The platform does not need to ban you

We tend to imagine platform power through dramatic acts of deplatforming.

• A politician is banned.

• A creator loses an account.

• A seller is removed from a marketplace.

But exclusion is only the most visible sanction.

A platform can allow you to remain while making you economically irrelevant.

Meta explicitly states that it may reduce the distribution of certain content even when that content is not removed from the platform.
Its ranking systems can make content harder to discover without deleting it.

• A seller can stay in the marketplace and disappear from recommendations.

• A creator can keep publishing and stop reaching her audience.

• A business can remain listed and lose search traffic.

• A video can stay online and lose monetisation.

The platform does not need to silence you.

It can make you invisible.

Visibility has become an economic right without legal protection

In conventional markets, firms compete through prices, products and productivity.

In programmable markets, they also compete for algorithmic visibility.

That visibility is not distributed neutrally.

It is allocated by systems designed around the platform’s objectives.

• A change in ranking can redirect millions of customers.

• A change in recommendation can create a new market leader.

• A change in monetisation rules can destroy a creator’s income.

• A change in an app store can alter an entire business model.

The platform is not simply observing the market.

It is continuously reallocating economic opportunity.

Commissions as private taxation

Consider the App Store.

Apple’s standard commission on many digital goods and services is 30%.
Qualifying developers in its Small Business Program pay 15%.

A commission is not legally a tax.

But the economic resemblance grows when one private gatekeeper controls access to an essential commercial ecosystem and determines the percentage required to reach it.

The state taxes income because it claims public authority.

The platform takes a percentage because it owns the gate.

The important question is not simply whether 15% or 30% is fair.

Moderation as private justice

Platforms must enforce rules.

Fraud, abuse, manipulation, illegal content and coordinated attacks cannot simply be ignored.

Some form of moderation is necessary.

But look at the institutional structure.

• The platform writes the rule.

• The platform collects the evidence.

• The platform interprets the violation.

• The platform imposes the penalty.

• The platform controls the appeal.

The legislature, administration, police, prosecutor and judge can all sit inside the same corporate architecture.

This does not mean every moderation decision is wrong.

Surveillance as administrative capacity

States build governing capacity by collecting information about citizens, income, property and economic activity.

Platforms have built another form of administrative capacity.

They observe:

• searches

• clicks

• purchases

• locations

• relationships

• attention

• behaviour

This information allows them to predict who will buy, who will leave, who will engage and who can be influenced.

AI increases the scale of this power.

It automates classification, prediction, ranking, pricing, moderation and exclusion across billions of daily interactions.

The East India Company warning

The East India Company analogy should not be exaggerated.

Today’s platforms are not colonial administrations.
They do not govern physical territories or command conventional armies.

But the institutional comparison matters.

The East India Company began as a commercial organisation and gradually acquired political, administrative and military authority.
By the late eighteenth century, it combined the functions of trader and territorial governor.
Its governing powers were eventually transferred to the British Crown in 1858.

The analogy is not moral equivalence.

It is a warning about institutional evolution.

Commercial organisations can accumulate governing functions before political theory and public institutions fully recognise what has happened.

The East India Company controlled territory.

Platforms control ecosystems.

The territory is digital.

The dependency is economic.

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But states can still regulate platforms

There is a strong counterargument.

Platforms remain companies incorporated under national law.

Governments can investigate them, tax them, fine them and restrict their conduct.

Europe has already moved beyond conventional antitrust.

The Digital Markets Act explicitly calls the largest platforms gatekeepers.
In April 2025, the European Commission found Apple and Meta in breach of specific DMA obligations.

Regulation is not powerless.

The GDPR, Digital Services Act, Digital Markets Act and AI Act can impose real costs and change behaviour.

Europe has recognised something important: dominant platforms do not merely compete inside markets.

They control entrances to markets.

But the word gatekeeper is also an admission.

Public regulation is trying to govern private institutions that already govern other economic actors.

• The regulator intervenes periodically.

• The platform governs continuously.

• The investigation may take months or years.

• The ranking can change this afternoon.

Why the traditional regulatory map remains incomplete

Antitrust examines competition and concentration.

Data protection examines personal information.

The AI Act examines particular systems and risks.

The Digital Services Act examines platform responsibilities and harmful content.

Each instrument addresses a real problem.

But private sovereignty emerges from the combination of several powers:

• rule-setting

• surveillance

• revenue extraction

• allocation

• enforcement

• exclusion

Regulation separates these powers into different legal boxes.

The platform reunites them inside one architecture.

Power arrived disguised as convenience

We imagine transfers of political power through elections, constitutions, wars and revolutions.

Platform power arrived differently.

It arrived through convenience.

• A better search engine.

• A larger marketplace.

• A safer app store.

• A more engaging social network.

• A faster payment system.

Each solved a genuine problem.

Each attracted more users.

Each accumulated more data.

Each became harder to leave.

Then the service became infrastructure.

And the infrastructure began to govern.

There was no constitutional moment.

Nobody announced that authority had moved.

We clicked Accept.

Closing punch

We may be looking for government in the wrong place.

We watch presidents, parliaments and regulators.

But some of the most consequential rules in economic life are now written in product meetings, embedded in code and executed by algorithms.

The central question of platform capitalism is no longer only:

How should governments regulate large companies?

It is:

Who governs the private governors of the economy?

Why it matters

For firms, platform dependency is now a strategic risk.

For investors, a rule change can destroy value faster than ordinary competition.

For policymakers, economic governance increasingly occurs outside democratic institutions.

For professionals and creators, access and visibility have become revocable economic assets.

Common narrative

Big Tech is powerful, but it remains inside the sovereign order.

Antitrust, the GDPR, the AI Act and digital regulation can keep it under public control.

My view

Platforms no longer operate only inside the sovereign order.

They have acquired quasi-sovereign powers.

They write rules, extract revenues, monitor behaviour, allocate opportunity and impose sanctions.

Regulation can constrain private sovereignty.

It cannot govern it properly until it recognises what it has become.

The new sovereign is not the one who owns the territory. It is the one who controls access.

Share this issue with someone whose business, income or public visibility depends on rules written by a platform.

Refer a friend

Good writing can make AI underrate your work

• Storytelling.

• Analogies.

• Varied vocabulary.

• Avoiding repetition.

• Delayed conclusions.

These techniques can work beautifully for human readers and hide the claims, evidence and scoring signals an LLM needs to extract.

The solution is not to write like a machine.

It is to make your strongest evidence impossible for the machine to miss.

See How to Write for LLM Evaluation

Your turn

Which platform has the greatest power over your work, business or public visibility?

What could it change tomorrow without asking your permission?

Reply to this email or leave your answer in the comments.

Leave a comment

Next week

If platforms govern markets, the problem goes beyond platform regulation.

Our institutions are trying to govern an economy that their theories can no longer explain.

Next week:

The Governability Crisis

Paywall bridge

Free readers get the argument.

Paid readers get the map.

This week’s Research Corner examines six academic papers behind the concept of private sovereignty.

They explain how platforms became systems of governance, why terms of service fail basic rule-of-law tests, how algorithms regulate behaviour, how demonetisation functions as an economic sanction and why traditional antitrust sees only part of the problem.

Research Corner

What the research says about private sovereignty

Private sovereignty is not yet a single, settled theory in platform research.

The authors below use different concepts:

• platform governance

• digital constitutionalism

• private ordering

• hypernudging

• tiered governance

• platform infrastructure

But they converge on one conclusion.

Platforms are not neutral intermediaries.

They organise participation, write rules, influence behaviour, distribute visibility and enforce sanctions.

The debate is no longer about whether platforms govern.

It is about whether that governance is legitimate, accountable and politically controllable.

Paper 1: Platforms are governance systems

Gorwa, R. (2019). “What Is Platform Governance?” Information, Communication & Society, 22(6), 854-871.

DOI: 10.1080/1369118X.2019.1573914

What the paper does

Gorwa develops the concept of platform governance to connect research that had previously been fragmented across political science, communication, law and technology studies.

Governance is not limited to what the platform does to users.

It includes relationships among:

• platforms

• users

• advertisers

• governments

• civil-society organisations

• commercial partners

Main contribution

The paper shows why platform power cannot be understood through a simple firm-consumer relationship.

Platforms sit inside a governance system.

They are regulated by governments, pressured by advertisers and civil society, and simultaneously regulate users and commercial participants.

Platform governance therefore operates in several directions at once.

Limitation

This is primarily a conceptual mapping exercise.

It establishes the field and its vocabulary but does not quantify the economic consequences of platform decisions.

It also concentrates heavily on social-media governance and content moderation.

Strategic interpretation

The unit of analysis should not be the individual platform decision.

It should be the wider governance relationship.

A business dependent on Amazon, Apple, Google or Meta is not simply a customer.

It is a governed participant inside a private ecosystem.

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Read more

This week, I want to follow the money.

Because once markets become programmable, someone acquires the power to decide who enters, who appears and who gets paid.

Paid readers will find the research map after the paywall.

You can own everything and still control nothing

• You can own the product.

• You can own the car.

• You can write the paper.

• You can produce the video.

• You can build the company.

Someone else can still decide whether you are seen.

That is the economic shift hiding behind platform capitalism.

The rent has moved

Traditional economic theory taught us to look for rent in familiar places.

• Landowners received Ricardian rent because land was scarce.

• Capital owners received returns because they owned productive assets.

• Monopolists extracted profit by restricting competition and raising prices.

• Employers captured value through their bargaining power over labour.

All of these mechanisms still exist.

But platforms and AI have introduced another one.

• A platform can extract value without owning the products being sold, the cars providing the service, the houses being rented, the articles being written or the audiences being reached.

It only needs to control the architecture through which economic activity must pass.

I call the resulting return algorithmic rent.

Algorithmic rent is the return generated by controlling access, ranking, visibility, matching, data, interfaces and participation rules.

It is the rent collected from owning the road rather than the vehicle.

Four people enter the same market

Consider four familiar situations.

The seller

A small company owns its products, inventory and brand.

But the marketplace controls where those products appear.

The platform can determine search ranking, recommendations, comparisons, advertising placement and account access.

The seller owns the product.

The platform controls discovery.

The driver

A driver may own the car and pay for fuel, insurance and maintenance.

But the platform allocates demand, chooses the match, monitors performance, manages ratings and can restrict access to future work.

The International Labour Organization defines algorithmic management as the use of tracked data to organize, assign, monitor, supervise and evaluate work.

It notes that this has become a defining feature of digital labour platforms.

The driver owns the productive asset.

The platform controls the opportunity to use it.

The creator

A writer, researcher, musician or video producer owns the work.

But search engines, recommendation systems and social feeds determine whether anyone encounters it.

A creator can improve the product and still lose reach.

Visibility is not distributed only by quality.

It is allocated by architecture.

The professional

A consultant writes an EU proposal.

A researcher submits a paper.

A marketer prepares a campaign.

The work may increasingly pass through an AI system that summarises, categorises, compares or extracts its central claims before a human evaluates it.

The professional owns the argument.

The AI-mediated evaluation layer influences which part of that argument becomes visible.

Europe has already named the problem

The European Union does not describe the largest platforms simply as successful companies.

That word matters.

A gatekeeper controls passage.

The DMA was designed to make digital markets more contestable and imposes specific obligations and prohibitions on companies controlling core platform services such as search engines, app stores and messaging services.
It complements traditional competition law rather than replacing it.

The OECD has also had to expand its language.

Its analysis of digital market power discusses platform power, intermediation power, bottleneck power, gatekeepers, unavoidable trading partners and economic dependence.
These concepts exist because conventional measures such as price, market share and profit do not always reveal how control operates in multi-sided digital markets.

This is not a minor vocabulary change.

Economic institutions are struggling to name a form of power that old models do not fully capture.

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The middle class pays before it earns

The middle class used to build security through a combination of skills, employment, professional reputation and small-scale ownership.

• A car could produce income.

• A shop could reach customers.

• Expertise could build a career.

• A publication could reach an audience.

Today, ownership and competence are no longer enough.

• The professional must be ranked.

• The company must be discovered.

• The worker must be matched.

• The creator must be recommended.

• The proposal must be correctly extracted.

The middle class is therefore not only taxed after earning.

It is charged for the possibility of earning.

This compounds an older problem.
Research on labour-market polarisation has documented how computerisation weakened many middle-wage routine occupations while employment expanded at the top and bottom of the labour market.

Platform capitalism adds another layer.

Even people who retain valuable skills must now negotiate access to the systems that allocate attention, work and opportunity.

But platforms create real value

There is an obvious counterargument.

Platforms make markets work.

• They reduce search costs.

• They connect buyers and sellers.

• They verify identities.

• They process payments.

• They improve matching.

• They create transactions that might otherwise never happen.

All that is true.

The mistake is to assume that efficiency and rent extraction cannot coexist.

Refer a friend

The question is not whether the platform creates value.

The question is whether controlling the architecture allows it to capture value that cannot be explained by innovation, risk or marginal productivity alone.

Old remedies arrive too late

The conventional response is familiar:

• Tax the profits.

• Protect the workers.

• Block the merger.

• Break up the monopoly.

These policies still matter.

But they often intervene after the architecture has already allocated the gains.

• A progressive tax acts after income has been created.

• Labour protection acts after work has been classified and assigned.

• Traditional antitrust often looks for prices, market shares and consumer harm after a dominant position has formed.

Algorithmic rent begins earlier.

It begins when someone designs the ranking.

• When someone decides the default.

• When someone controls the interface.

• When someone determines what can be found.

• When someone sets the conditions for participation.

By the time the transaction happens, much of the distribution has already been programmed.

The market is not only where value is exchanged

• It is where visibility is allocated.

• It is where opportunities are ranked.

• It is where participation is permitted or denied.

• It is where dependence is converted into revenue.

It must examine who controls the architecture through which everyone else must move.

Why it matters

Executives, professionals, investors and policymakers must identify the controlled passage points inside every market.

Do not ask only who owns the asset.

Ask who controls access to the opportunity.

Common narrative

Platforms are digital monopolies.

Antitrust, progressive taxation and labour regulation will contain their power.

My view

Monopoly is the visible outcome.

Architectural control is the deeper mechanism.

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The new rent is not charged on what you own. It is charged on your need to be seen.

AI will evaluate your work

Your next EU proposal, academic paper, website, landing page or marketing campaign will not be evaluated by a human alone.

An LLM will summarise it, compare it, extract its claims and shape what the evaluator sees.

A strong text can receive a weak evaluation because the LLM failed to extract what made it strong.

How to Write for LLM Evaluation is a seven-step digital experience built around one real section from your own work.

First Edition opens 1 September.

Prepare your work for LLM evaluation

Where do you depend on an algorithm?

Is it for customers, employment, professional visibility, funding, publication or access to an audience?

Reply to this email or leave a comment. I would like to know where algorithmic dependence is most visible in your work.

Leave a comment

Next week

If a platform controls access, sets rules, ranks participants and enforces compliance, it is no longer behaving only like a company.

It is beginning to behave like a government.

Next week: Private Sovereignty.

Paid readers get the map

Free readers get the argument.

Paid readers get the economic mechanism behind it.

In the Research Corner, I will distinguish algorithmic rent from Ricardian rent, capital income and conventional monopoly profit.

I will also map the six mechanisms through which platforms turn architectural control into income, and the strategic questions firms, professionals and policymakers should ask before becoming dependent on a platform.

Research Corner outline

1. Working definition

Algorithmic rent: income captured through control over an algorithmically managed coordination layer, beyond the return attributable to productive contribution, innovation or conventional ownership.

The definition will separate:

2. The six mechanisms

1. Access rent: payment or compliance required to enter the market.

2. Ranking rent: value created by controlling relative position.

3. Visibility rent: monetisation of discoverability.

4. Matching rent: control over who transacts with whom.

5. Data rent: cumulative advantage generated by behavioural and transaction data.

6. Dependency rent: extraction made possible by switching costs and ecosystem lock-in.

3. Strategic implications

The final Research Corner will translate the mechanism for:

• professionals dependent on search, recommendations or LLM evaluation;

• firms selling through marketplaces;

• investors assessing platform defensibility;

• policymakers designing competition and predistribution rules;

• EU project teams using AI-supported evaluation and selection systems.

4. Bridge to Private Sovereignty

The concluding question:

When a private platform controls access, writes the rules, monitors compliance and imposes sanctions, where does market coordination end and government begin?

Core papers

Rochet and Tirole, 2003

Platform Competition in Two-Sided Markets

Foundation for understanding how platforms coordinate different groups whose participation is mutually dependent.

DOI: 10.1162/154247603322493212

Read more

This month, Polis Doxa opens a new arc: the new economic theory after platform + AI capitalism.

The question is simple: what happens when markets change faster than the models used to explain them?

Thank you for reading Polis Doxa. Paid subscribers get the Research Corner, monthly Terrain Layer, strategic tables and the measurement layer behind each essay.

2. The market is not disappearing. It is being rewritten.

We still talk about markets as if buyers meet sellers, prices adjust and competition disciplines power.

But open Amazon, Google, Uber, Booking, Airbnb, YouTube, LinkedIn or an app store.

You are not entering a neutral market.

You are entering an architecture.

3. Why the old economic map is failing

Classical economics was built for markets coordinated mainly by prices.

Platform + AI capitalism is creating markets coordinated by software/algorithms.

A growing part of the economy is now organized through:

• visibility

• ranking

• access

• matching

• search

• data

• prediction

• reputation

• recommendations

• default options

• attention allocation

• algorithmic governance

That is the shift.

In a traditional market, many decentralized choices produce an outcome.

In a programmable market, the platform designs part of the outcome before choice happens.

• It sets the menu.

• It ranks the options.

• It filters access.

• It prices dynamically.

• It nudges behavior.

• It collects data from today’s transactions to reshape tomorrow’s market.

4. Where the algorithm already acts like the market

Look at online retail.

A seller on Amazon is not only competing with other sellers.

She is competing for ranking.

A better product does not automatically win.

A better position inside the architecture may win.

Look at search.

A restaurant may be excellent.

A consultant may be brilliant.

A small business may offer better value.

But if Google, AI search or recommendation systems do not surface it, the market may never see it.

In the old economy, the question was:

Can customers find you?

Look at app stores.

A developer may build the best app in a category.

But distribution depends on platform rules, commissions, rankings, reviews, search visibility and approval processes.

The market is not only demand.

It is access.

Look at ride-hailing.

Drivers do not simply sell labor at a wage.

They are matched, monitored, rated, priced and nudged by algorithmic systems.

The platform does not look like a traditional employer.

But it still organizes work.

Look at media.

Writers, podcasters, researchers, consultants and creators are not only competing for attention.

They are competing inside ranking systems that decide who becomes visible.

The scarce resource is no longer information.

Information is abundant.

The scarce resource is attention.

And attention is allocated by algorithms.

5. The obvious objection

The objection is fair: algorithms can make markets better.

They reduce search costs, improve matching, compare options and coordinate millions of interactions that old market systems could not handle.

But when the same system controls ranking, access, pricing, information, reputation and attention, it becomes market infrastructure.

That changes the question.

The issue is no longer only whether platforms are efficient or monopolistic.

6. This is not digitization. This is programmability.

In the old economic map, price was the central coordinating signal.

• Price compressed information.

• Price told producers what to produce.

• Price told consumers what was scarce.

• Price helped markets adjust without a central planner.

That logic still matters.

But in programmable markets, price is no longer alone.

• Ranking can coordinate.

• Visibility can coordinate.

• Recommendation can coordinate.

• Access can coordinate.

• Reputation scores can coordinate.

• Default options can coordinate.

• Dynamic pricing can coordinate.

• AI agents may soon coordinate on behalf of buyers, sellers, workers and firms.

So price does not disappear.

It loses its monopoly as the main market signal.

This is the real fracture.

This is not digitization.

This is programmability.

7. Why it matters

Regulators still look at prices, market share and consumer choice, while platforms increasingly control visibility, access and architecture.

Firms and professionals are no longer competing only for customers. They are competing for algorithmic visibility.

8. The common narrative

The common narrative says: the economy is the same, only faster and more digital.

9. My view

No. Markets have stopped being purely emergent. They are becoming programmed.

10. The line to remember

The market is the algorithm.

And increasingly, the algorithm is the market.

11. Become a paid subscriber

If this argument helps you see the economy differently, become a paid subscriber.

Paid readers get the Research Corner behind each essay, the monthly Terrain Layer, strategic tables, rankings, diagnostics and the measurement layer behind Polis Doxa.

12. Go deeper

Support independent research:

• Listen to InnovEU - The EU Project Chronicles: Substack, YouTube, Spotify, Apple Podcasts

• Order Transition Science in the Economy on Amazon: Amazon

• Explore the First-Mover Opportunity Radar: Payhip

These are for readers who do not just want to understand the five transitions, but want to move before the crowd.

13. Next week

Next week, I move from programmable markets to Algorithmic Rents.

14. For paid readers

Today’s free essay gives you the argument.

The Research Corner gives you the map.

Below the paywall, I collect the key papers behind this week’s argument: algorithmic pricing, rankings, data markets, platform antitrust, algorithmic management and attention rents.

Research Corner - Paid Subscriber Section

1. Artificial Intelligence, Algorithmic Pricing, and Collusion

Paper: Calvano, E., Calzolari, G., Denicolò, V. and Pastorello, S. (2020). Artificial Intelligence, Algorithmic Pricing, and Collusion. American Economic Review, 110(10), 3267-3297.

Source: American Economic Review

DOI/link: https://doi.org/10.1257/aer.20190623

Why it matters: This paper shows that Q-learning pricing algorithms can learn to charge supracompetitive prices without communicating with one another. That matters because traditional antitrust often looks for agreement, intent or communication.

Strategic takeaway: Regulators cannot only ask whether humans colluded. They must ask whether algorithmic systems create pricing environments where competition weakens by design.

Read more

Today, I want to do something simple.

• Not predict the Green Transition.

• Not praise it.

• Not attack it.

Measure it.

Everyone talks about Europe becoming fossil-free.

But almost nobody asks the brutal question:

How many days are still left?

• Not politically.

• Not emotionally.

Energetically.

Here is the uncomfortable answer.

If we extend the official EU final-energy renewable trend in a conservative linear way, Europe reaches full renewable replacement around 2121.

That is roughly 34,800 days from 3 July 2026.

Yes.

Not 2030.

Not 2050.

Around 2121.

But here’s what everyone misses...

Europe is winning the electricity transition.

It is not yet winning the total energy transition.

And confusing the two is one of the most dangerous mistakes in the climate debate.

1. The Green Transition Is Not a Solar Panel Race

The accepted story is easy to sell.

• Build wind.

• Build solar.

• Close coal.

• Electrify everything.

• Wait for the miracle.

That story is not false.
It is incomplete.

That is why I frame it through Transition Science.

In the AWTY Green Transition Index, the Green Transition is not measured by one number alone.

It includes renewable energy consumption, carbon intensity, resource depletion, energy intensity and forest cover change.

Because a real transition is not a fuel swap.

It is a system redesign.

2. The Big Number - 25.2% Renewable, 74.8% Not Yet

Let’s start with the cleanest official figure.

In 2024, renewable energy represented 25.2% of energy consumed in the EU, up from 24.6% in 2023. Eurostat also reports that the EU was at 9.6% in 2004.

So, if the EU consumed 100 units of final energy in 2024:

That is the whole post in one table.

• Europe has cleaned one quarter of final energy use.

• Three quarters remain.

This matters for your life because final energy is not an abstract statistic.

It is:

• Your heating bill.

• Your grocery logistics.

• Your company’s electricity contract.

• Your commute.

• Your flight.

• Your industrial base.

• Your tax money.

• Your inflation risk.

Energy is not a sector.

Energy is the bloodstream of the economy.

3. The Gap Europe Still Has to Close

Use this as a simple visual in the post:

EU gross final energy consumption, 2024

Renewable energy        █████████████████████          25.2%

Still non-renewable     ████████████████████████████████████████████████████████████████  74.8%

That chart is why this post matters.

Europe’s Green Transition is real.

But the remaining gap is still three times larger than the renewable base.

• This is why “we are almost there” is wrong.

• And why “nothing is happening” is also wrong.

The truth is more useful.

Europe is moving.

Just not fast enough across the whole system.

4. The Electricity Illusion ⚡

Now comes the seductive part.

Electricity looks much better.

In 2024, renewable energy sources made up 47.5% of gross electricity consumption in the EU.

Preliminary Eurostat data also show EU renewable electricity production at a record 1,313 TWh in 2024, while fossil-fuel electricity fell to a record low 810 TWh.

Even better, wind generated 487.6 TWh, more than natural gas at 443.4 TWh.

That is a real milestone.

You can almost hear the old system cracking.

But electricity is only one part of the energy system.

Look at the sector split.

Heating and cooling reached 26.7% renewable energy in 2024. Transport reached only 11.2%.

So here is the key distinction:
Europe is much closer to cleaning electricity than to cleaning energy.

For a 9th grade reader:

• Cleaning electricity is like fixing the lights in the house.

• Cleaning total energy is like rebuilding the heating, kitchen, plumbing, garage, road access and food supply at the same time.

That is much harder.

5. How Many Days Are Left? ⏳

Now let’s do the rough calculation.

In 2004, renewables were 9.6% of EU gross final energy consumption.

In 2024, they were 25.2%.

That is a gain of 15.6 percentage points in 20 years.

So the linear pace is:

15.6 percentage points / 20 years = 0.78 percentage points per year

The remaining gap is:

100% - 25.2% = 74.8 percentage points

At the same linear pace:

74.8 / 0.78 = 95.9 years

Starting from 2024:

2024 + 95.9 = around 2120

From 3 July 2026, that means roughly:

34,800 days

That is the conservative countdown.

Not because Europe is doing nothing.

Because the total-energy denominator is huge.

And because transport, heat and industry do not move like solar panels.

6. Three Possible Finish Lines

When could EU renewables replace all final energy?
Optimistic acceleration path       2051-2064
Conservative linear path           Around 2121
Stress case with rising demand     No full crossover

1. The optimistic case assumes renewable growth compounds fast.

2. The conservative case assumes the historical trend continues linearly.

3. The stress case assumes demand keeps rising enough to eat the gains.

That last case matters.

Because the Green Transition is not only about producing more clean energy.

• It is also about controlling total energy demand.

• If demand grows faster than renewable replacement, the finish line moves away.

This is the smell of the trap.

• Fresh solar panels.

• Old fossil dependence.

• And a grid that still has to feed transport, heat, industry and AI.

7. The Transport Problem 🚗✈️🚚

Transport is the most embarrassing number in the table.

Only 11.2% renewable in 2024 (Eurostat).

That means the sector most people touch every day remains heavily fossil-based.

• Cars.

• Trucks.

• Ships.

• Planes.

• Delivery vans.

• Tourism.

• Imports.

• Food logistics.

This matters because transport is not just about emissions.

It is about vulnerability.

When oil prices rise, the pain moves through the economy like heat through metal.

• Food gets more expensive.

• Flights get more expensive.

• Construction gets more expensive.

• SMEs lose margins.

• Families feel poorer.

• And politicians pretend it is a temporary shock.

It is not.

It is a structural dependency.

8. The Gas Problem 🔥

Gas is the other weak point.

Eurostat reports that in 2024, 88% of gas supply in the EU grid came from imports.

That is not just an energy statistic.

It is a geopolitical statistic.

It means Europe can build more renewables and still remain strategically exposed.

Especially when gas is used to stabilize electricity, heat buildings and support industry.

This is where policy speeches often become too smooth.

• They show the rising green line.

• They hide the sticky fossil base.

9. The AI Twist - The Grid Becomes an Algorithm 🤖⚡

Here is where this post connects with the deeper Transition Science argument.

A renewable system is harder to coordinate than a fossil system.

• Coal and gas can be dispatched.

• Wind and solar fluctuate.

• The sun sets.

• The wind slows.

• Demand spikes.

• Batteries fill and empty.

• Millions of devices connect to the grid.

So the future energy system needs software.
A lot of software.

And that changes who has power.

• Data.

• Algorithms.

• Grid access.

• Dynamic pricing.

• Load balancing.

• Storage optimization.

• Demand prediction.

This is the hidden institutional battle.

That is why “more renewables” is not enough.

The political question is:

• Who controls the clean system?

• Citizens?

• States?

• Utilities?

• Industrial coalitions?

• Or platforms?

10. The Shareable Line 🟨

A clean grid controlled by private algorithms is not energy democracy. It is fossil power with better branding.

Share

11. Why This Countdown Is Not a Forecast

Critics will say this calculation is too simple.
They are right.

Europe does not need 100% renewable final energy to reach climate neutrality.

• Nuclear matters.

• Energy efficiency matters.

• Hydrogen may matter.

• Synthetic fuels may matter.

• Carbon capture may matter in hard-to-abate sectors.

• Imports of clean energy may matter.

• Demand reduction matters.

Also, renewable growth may accelerate.

The linear countdown may be too pessimistic.

But the optimistic compound scenario may be too generous.
Because growth rates become harder to sustain as systems approach saturation.

• The first solar panels are easier than the last fossil trucks.

• The first wind farms are easier than seasonal storage.

• The first heat pumps are easier than retrofitting millions of old buildings.

So the countdown is not a prophecy.
It is a stress test.

It forces one question:

Are we measuring the transition by political ambition, electricity headlines, or total system replacement?

Only the third one tells the truth.

12. What This Means for Decision-Makers

For policymakers, the message is clear.
Stop treating electricity as the whole transition.

The bottlenecks are transport, heating, industry, grids, storage and demand management.

For investors, the message is different.
The most valuable opportunities may sit in the boring layers.

• Grid reinforcement.

• Thermal renovation.

• Industrial heat.

• Battery storage.

• Demand response.

• Cybersecurity.

• Smart metering.

• Permitting.

For companies, the implication is practical.
Your energy risk is not just today’s bill.

It is exposure to fossil volatility, grid congestion, carbon regulation and transition timing uncertainty.

For households, it is even simpler.
The transition will decide your bills, your mobility costs, your housing comfort and your tax burden.

This is not climate theatre.

This is economic structure.

13. The AWTY Lens - Are We There Yet?

The AWTY framework exists because transitions are usually discussed as slogans.

1. Green.

2. Digital.

3. New Globalization.

4. War Economy.

5. Housing crisis.

But slogans do not help decision-makers.
Measurement does.

The Green Transition Index treats success as multi-dimensional: renewables, emissions, resource use, energy efficiency and natural capital.

That matters because a country can look green in one dimension and fragile in another.

• A country may have clean electricity but fossil transport.

• Efficient industry but imported gas dependence.

• High renewables but weak grids.

• Low emissions but weak resilience.

That is why the question is not only:
“Are renewables growing?”

The better question is:
“Is the whole system becoming cleaner, cheaper, more resilient and more democratic?”

That is the Transition Science question.

14. The Transition Ends When the Fossil Base Breaks

The Green Transition does not end when politicians announce targets.

• It does not end when solar breaks a record.

• It does not end when wind beats gas for one year.

It ends when fossil fuels stop setting the price of economic life.

• When oil shocks no longer eat household income.

• When gas imports no longer shape foreign policy.

• When transport no longer depends on diesel.

• When factories can compete without fossil volatility.

• When heat is clean, affordable and secure.

• When the grid is not only smart, but governable.

That is the real finish line.

And Europe is not there yet.

• The electricity story is encouraging.

• The total energy story is sobering.

• The governance story is just beginning.

I Might Be Wrong 💬🔥

I just explained my opinion on this.

But I might be wrong.

What’s yours?

Is Europe genuinely on a fast transition path?

Or are we fooling ourselves by confusing cleaner electricity with a fossil-free economy? ⚡

And here is the sharper question:
If transport, heat and industry stay this slow, is “2050 fossil phase-out” still analysis - or just branding? 👀

Comment with your pushback.

Leave a comment

Restack this if you think the electricity-vs-total-energy distinction deserves more attention. 🔁

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Share it on Notes or social media if you know someone who still thinks the Green Transition is only about solar panels. 🌍

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Listen to the Podcast InnovEU - The EU Project Chronicles.

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Feeling transition pressure but not sure where to move next?

AI, energy shocks, tariffs, supply-chain disruption, war economy dynamics and infrastructure pressure are already changing the strategic map.

The problem is not uncertainty.

The problem is not knowing which move deserves attention now.

I created the First-Mover Opportunity Radar™ as a 60-minute self-guided diagnostic to help organizations map transition pressure, spot risk blind spots and identify 3 possible strategic moves for the next 90 days.

Research Corner 🔒

1 Eurostat - Renewable Energy Statistics

Why it matters:

This is the official EU source for renewable shares in gross final energy consumption, electricity, heating and cooling, and transport.

Key number:

Renewables reached 25.2% of EU gross final energy consumption in 2024.

Strategic takeaway:

Do not confuse electricity progress with total energy replacement.

Url/DOI: https://ec.europa.eu/eurostat/statistics-explained/index.php?title=Renewable_energy_statistics

Read more

What if it is weak because the scoring logic is hidden?

In this episode of InnovEU - The EU Project Chronicles, I share excerpts from my webinar:

How to Pre-Mortem Your EU Proposal Before the Evaluator Does

And the lesson is uncomfortable:

• A proposal can be technically strong.

• It can have a good consortium.

• It can match the call.

• It can use all the right words: innovation, impact, excellence, dissemination, scalability.

But if the evaluator cannot extract the logic quickly, the proposal becomes fragile.

Because EU proposals are not just documents.

They are scoring interfaces.

In this episode, you’ll discover:

• Why a proposal pre-mortem should happen before submission

• How evaluator-style stress testing exposes downgrade risks

• Why strong proposals still lose points when impact logic is vague

• How LLM-assisted review changes proposal readability

• Why headings, terminology and evidence placement matter

• How to make your proposal easier to extract, verify and score

This is not a webinar about using AI to replace proposal writers.

It is about using evaluator logic before the evaluator does.

Because the real risk is not only writing a bad proposal.

The real risk is submitting a good proposal that is hard to score.

▶️ Watch or listen to the full episode

Watch the full episode:

Substack: https://fernandocgaspar.substack.com/podcast

YouTube: https://youtube.com/playlist?list=PLhPsYe80-Wq1bOuVZmZGso_Kr3W8biIpx

Listen only:

Spotify:

Apple Podcasts:

Stress-test your proposal before evaluators do

Quick note before we continue.

Most proposals are not rejected because they are useless.

They are rejected because they trigger downgrade logic that the team did not see.

The Grantshield Pre-Mortem™ helps teams stress-test the structure of a proposal before submission, using evaluator logic, transition alignment, impact plausibility and fast readability.

Use it before the evaluator does.

Grantshield Pre-Mortem™: https://payhip.com/b/yUeiV

Go deeper

My book Transition Science In The Economy develops the full framework behind the green transition, digital transition, new globalization, war economy and housing crisis.

Book: https://a.co/d/c7RoOA4

Let’s grow this community together

If you enjoyed the episode:

Like the podcast on your favourite platform.

Subscribe on Substack or any podcast app.

Subscribe now

Restack this post.

Share

Leave a comment.

Leave a comment

Share it with colleagues working on EU-funded projects, Horizon Europe proposals, Erasmus+ applications, innovation funding or research strategy.

Refer a friend

Follow me on LinkedIn: https://www.linkedin.com/in/fernandoacgaspar/

And if you would like to be featured on InnovEU - The EU Project Chronicles, send me a private message and let’s schedule it.

• You can feel it in boardrooms.

• You can feel it in energy bills.

• You can feel it in housing markets, supply chains, universities, elections, investment decisions and grant proposals.

Something is moving under the floor.

Most people call it uncertainty.

I do not.

I call it a measurement problem.

The world is not changing in one direction

The common narrative is simple.

AI is changing everything.

• Or climate is changing everything.

• Or geopolitics is changing everything.

• Or housing is breaking the middle class.

Each story is partly true.

That is exactly why it misleads.

• Digital transition, driven by AI.

• Green transition, exposed through energy shocks.

• New globalization, now visible through trade wars.

• War economy, where security and industrial capacity move back to the centre.

• Housing crisis, where the middle class feels the squeeze in rent, mortgages, mobility and family life.

These are not five separate stories.

They are one operating system.

And most decision-makers are still reading it with old tools.

What Polis Doxa does

Polis Doxa is not another newsletter about “what happened this week.”

It is a weekly attempt to measure the forces reshaping prosperity, power and risk.

The purpose is simple: to help decision-makers move before the crowd.

Most people have opinions about change.

Polis Doxa measures it.

That is the work.

Who this is for

Polis Doxa is for people who can feel the old economic map failing, but do not yet have a better one.

It is for executives, investors, consultants, policy actors, professors, researchers, innovation managers and senior professionals who need to understand what is changing before it becomes consensus.

Not because they enjoy complexity.

Because they make decisions inside it.

If you are trying to understand how AI, energy shocks, trade wars, war economy and housing crisis are changing work, business, investment, funding, policy and the middle class, you are in the right place.

The danger is not so many transitions at once

That is the visible problem.

The deeper danger is different.

The danger is losing the opportunities they open.

Every transition creates fear.

But it also creates new markets, new needs, new risks, new funding priorities, new forms of power and new strategic openings.

That is what Polis Doxa is building.

What free readers get

Free readers get the weekly argument.

Every Friday, Polis Doxa publishes a structured op-ed on the forces reshaping prosperity, power and risk.

• You get the thesis.

• The evidence.

• The counterargument.

• The common narrative being challenged.

• The strategic question that follows.

What paid readers get

Paid readers get the map behind the argument.

The paid layer is for readers who want the measurement, not only the narrative.

Paid subscribers receive:

• Research Corner
  The research section behind the weekly essay: sources, studies, data, indicators and strategic interpretation.

• Monthly Terrain Layer
  A monthly synthesis of the last four free essays: what changed, the strategic takeaway and what to watch next.

• Rankings, diagnostics and strategic tables
  Country, sector and transition comparisons based on Transition Science and AWTY.

• Strategic Tools Library
  Frameworks, checklists and decision templates to apply the analysis to careers, organisations, investments, projects and strategy.

What Founding Members get

Founding Members go one step closer.

They help build the Polis Doxa research circle.

This tier is for readers who want early access, proximity and influence over the questions I investigate next.

Founding Members receive everything in Paid, plus Founding Member status, monthly office hours, one priority question per month, early access to tools and reports, and a 40% discount on selected applied products such as FMA, Pre-Mortem and cohorts.

Where to start

Start with the question that matters most to you.

• If you are worried about AI, read how digital transition is changing work, productivity and bargaining power.

• If you are worried about energy, read how green transition is becoming a cost, competitiveness and resilience issue.

• If you are worried about globalization, read how trade wars are changing the rules of openness.

• If you are worried about geopolitics, read how war economy is bringing industrial capacity back into strategy.

• If you are worried about living standards, read how housing crisis is becoming the hidden constraint on the middle class.

Join Polis Doxa

Subscribe if you want to read the weekly argument.

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Upgrade if you want the research layer behind it.

Become a Founding Member if you want to help shape the research circle.

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What if AI disruption, energy volatility, trade wars, security pressure and housing shortages are not only threats?

In this episode of InnovEU - The EU Project Chronicles, I share selected excerpts from my Profit Signals webinar, the first step of the First-Mover Advantage approach.

And the lesson is uncomfortable:

• Waiting feels safe.

• Freezing feels rational.

• Delaying action feels responsible.

But in a polycrisis, hesitation has a cost.

Because the same pressures that create risk also reveal new demand, new inefficiencies, new partnerships and new market positions.

The question is not whether your firm can predict the future.

The better question is:

What can you test before everyone else is forced to react?

In this episode, you’ll discover:

• Why firms should stop treating uncertainty only as a threat

• How AI, energy, trade, security and housing pressures reveal profit signals

• Why the five transitions are reshaping business opportunities now

• How the AWTY Index helps separate noise from structural change

• Why 90-day tests are more useful than waiting for certainty

• How firms can move first, learn first and capture opportunities first

This is not just a webinar about strategy.

It is about how firms, founders, SMEs, institutions and EU project teams can turn transition pressure into practical business action.

Watch or listen to the full episode

Watch the full episode:

Substack: https://fernandocgaspar.substack.com/podcast

YouTube: https://youtube.com/playlist?list=PLhPsYe80-Wq1bOuVZmZGso_Kr3W8biIpx

Listen only:

Spotify:

Apple Podcasts:

Escape the “chaos of transitions” trap

Quick note before we continue.

When AI, energy, trade, security and housing pressures all move at once, many organizations freeze.

Others move first.

That is where the opportunity opens.

The First-Mover Opportunity Radar™ helps you map those signals and identify 3 possible strategic moves for the next 90 days.

It is self-guided, practical and designed to be completed in 60 minutes.

Get the RADAR tool here:

https://payhip.com/b/MJ6AY

Go deeper

My book Transition Science In The Economy develops the full framework behind the green transition, digital transition, new globalization, war economy and housing crisis.

It explains why these transitions are not abstract trends.

They are already deciding prosperity, risk, resilience and competitive advantage.

Book:

https://a.co/d/c7RoOA4

Let’s grow this community together

If you enjoyed the episode:

Like the podcast on your favourite platform.

Subscribe on Substack or any podcast app.

Subscribe now

Restack this post.

Share

Leave a comment.

Leave a comment

Share it with colleagues working on EU-funded projects, innovation strategy, SMEs, regional development or Horizon Europe proposals.

Refer a friend

Follow me on LinkedIn:

https://www.linkedin.com/in/fernandoacgaspar/

And if you would like to be featured on InnovEU - The EU Project Chronicles, send me a private message and let’s schedule it.

Today, I want to challenge one of the most dangerous ideas in economics:

The idea that richer countries automatically create freer, happier people.

• It sounds reasonable.

• It is also incomplete.

Because:

• GDP can rise while trust collapses.

• GDP can rise while housing becomes impossible.

• GDP can rise while democracy becomes theatre.

• GDP can rise while algorithms quietly decide who gets visibility, work, credit, attention, and influence.

But there’s more:

That is why we need a new dashboard.

Not to replace GDP.

To stop worshipping it.

1. The Real Argument

For decades, the accepted story was simple.

1. Growth creates wealth.

2. Wealth creates freedom.

3. Freedom creates happiness.

That story still contains truth.

But it no longer explains the world we live in.

The real relationship is more fragile:

And GDP can grow while all of this weakens.

That is the core argument of Transition Science in the Economy.

GDP is incomplete because prosperity is no longer a single economic outcome.

It is a transition condition.

It depends on whether countries can protect freedom, trust, purchasing power, institutional quality, housing access, digital autonomy, and resilience at the same time.

That is why the AWTY Index matters.

AWTY does not ask: “How rich is this country?”

It asks:

Are we ready for the pressures already reshaping the economy?

1. Green transition.

2. Digital transition.

3. New globalization.

4. War economy.

5. Housing crisis.

That is the real prosperity test now.

2. The Old Dashboard Is Failing 📊

2.1 GDP is still useful, but dangerously incomplete

GDP measures output.

That matters.

A country needs production, income, tax capacity, investment, and employment.

But

• GDP does not measure whether people feel secure.

• It does not measure whether institutions are fair.

• It does not measure whether young people believe they can buy a home.

• It does not measure whether democracy is becoming hollow.

• It does not measure whether digital platforms control the architecture of economic life.

So GDP is like checking the speedometer while ignoring the fuel, brakes, engine temperature, road conditions, and the storm ahead.

That is not analysis.

That is dashboard blindness.

2.2 Democracy is weakening while the economy still looks “fine”

Freedom House reports that global freedom declined for the 20th consecutive year in 2025.

In that year, 54 countries deteriorated, while only 35 improved (Freedom House, 2026).

This matters because freedom does not usually disappear with dramatic music.

It fades quietly.

• A court becomes less independent.

• A regulator becomes more political.

• A journalist becomes more afraid.

• A citizen becomes more careful.

• A company learns not to challenge insiders.

And GDP may still rise.

That is the trap.

A country can look economically alive while becoming institutionally weaker.

2.3 Elections are not enough

The Economist Intelligence Unit’s Democracy Index 2024 fell to 5.17 out of 10, the lowest score since the index began in 2006.

Only 45% of the world’s population lived in a democracy, while 39% lived under authoritarian rule (Economist Intelligence Unit, 2025).

Why does this matter for an ordinary reader?

Because democracy is not only about voting every few years.

It is about whether power can be corrected.

• Can bad leaders be removed?

• Can corruption be exposed?

• Can courts say no?

• Can citizens speak without fear?

• Can the rules apply to everyone?

If the answer is no, wealth becomes fragile.

The lights may still be on.

But the building is cracking.

2.4 Freedom explains happiness better than GDP alone

The World Happiness Report 2025 uses six major factors to explain life evaluations:
GDP per capita, social support, healthy life expectancy, freedom, generosity, and corruption (World Happiness Report, 2025).

Look carefully.

• GDP is there.
  But it is not alone.

• Freedom is there.

• Social support is there.

• Corruption is there.

• Health is there.

• Generosity is there.

This is the key.

A bigger economy helps.

But only if people can breathe inside it.

2.5 Human freedom is falling in rich and poor countries

The Human Freedom Index 2025 reports that 89.6% of the world’s population lived in jurisdictions where human freedom declined between 2019 and 2023 (Cato Institute and Fraser Institute, 2025).

That sentence should stop us.

This is not only a poor-country problem.

• It affects rich and poor countries.

• Democracies and non-democracies.

• Advanced economies and fragile states.

Which means the issue is deeper than income.

It is about control.

• Who controls speech?

• Who controls markets?

• Who controls data?

• Who controls movement?

• Who controls access?

• Who controls the future?

A rich society with shrinking freedom is not becoming more developed.

It is becoming more comfortable to obey.

3. Why Wealth Stops Producing Happiness

Here is the mechanism.

GDP can increase happiness when it creates:

• security.

• Better jobs.

• Better housing.

• Better health.

• Better schools.

• Better infrastructure.

• Better life chances.

But GDP stops working when growth becomes detached from lived experience.

That happens when:

• Wages lag behind productivity.

• Housing rises faster than income.

• Platforms extract rents.

• Institutions lose legitimacy.

• Corruption rewards insiders.

• AI increases productivity but weakens bargaining power.

• Globalization lowers prices but destroys local resilience.

• War economy pressure diverts budgets.

• Green transition costs arrive before green benefits.

• Digital transition creates convenience but also surveillance.

This is why people can live in rich countries and still feel poorer.

Not because they are irrational.

Because their personal transition pressure is rising.

• They feel it in rent.

• They feel it in work.

• They feel it in attention.

• They feel it in debt.

• They feel it when politics becomes noise and institutions stop feeling fair.

4. Where AWTY Changes the Conversation 🧭

This is where AWTY matters.

The AWTY 2024 Global Transitions Composite Index measures how countries navigate five simultaneous transitions from 1990 to 2024, using a 0-10 composite score across Green, Digital, New Globalization, War Economy, and Housing crisis dimensions .

That changes the question.

Instead of asking only:
“How rich is this country?”

We ask:
“How ready is this country?”

• Ready for climate pressure.

• Ready for AI pressure.

• Ready for supply-chain rewiring.

• Ready for defense and geopolitical pressure.

• Ready for housing affordability pressure.

• Ready for institutional stress.

• Ready for middle-class anxiety.

The AWTY logic is simple:

That is why some countries with high GDP still feel unstable.

And why some smaller countries may perform better than expected.

The AWTY report shows that top performers combine several strengths at once: digital infrastructure, renewable energy, global trade integration, stable security alliances, and effective housing policies .

That is exactly the point.

No single variable explains the future.

5. Democracy, Freedom, Happiness and Wealth - The Real Model

Here is the model I would use.

Wealth gives capacity

GDP creates resources.

Without economic capacity, states struggle to fund schools, hospitals, infrastructure, defense, courts, housing, and research.

So yes, wealth matters.
But capacity is not destiny.

Money can build public goods.
Or it can protect insiders.

Money can expand freedom.
Or it can buy surveillance.

Money can reduce fear.
Or it can finance control.

Democracy gives correction

Democracy matters because transitions create mistakes.

Every country will make bad decisions.

The question is whether the system can correct them.

• Autocracies can move fast.

• But democracies can correct course.

In transitions, correction may matter more than speed.

Freedom gives agency

Freedom matters because prosperity is not only comfort.

It is the ability…

• To act.

• To speak.

• To move.

• To start.

• To disagree.

• To learn.

• To leave.

• To build.

• To refuse.

Formal freedom asks: “What are you allowed to do?”

Real freedom asks: “What are you actually able to do?”

That is why freedom must include personal freedom, economic freedom, and capability freedom.

• A person who can vote but cannot afford rent is not fully free.

• A person who can start a business but cannot access platforms, capital, or visibility is not fully free.

• A person who can speak but is buried by algorithmic noise is not fully free.

Happiness gives the lived signal

Happiness is not soft.
It is a signal.
It tells us whether the system is working in human terms.

When happiness falls in rich societies, we should not dismiss people as spoiled.

We should ask what GDP is missing.

• Maybe it is loneliness.

• Maybe it is housing insecurity.

• Maybe it is loss of trust.

• Maybe it is digital exhaustion.

• Maybe it is fear of downward mobility.

• Maybe it is the feeling that the future has been stolen.

People do not become unhappy only because they are poor.

6. Other Indices Worth Adding to the Dashboard

GDP remains one metric.

But a serious prosperity dashboard needs more.

Democracy Index

Shows whether political institutions are functioning, not just whether elections exist.

Freedom in the World

Captures political rights and civil liberties.

Human Freedom Index

Combines personal freedom and economic freedom.

Economic Freedom of the World

Measures legal systems, property rights, sound money, trade freedom, and regulation.

Human Development Index

Brings health, education, and income into one development measure.

OECD Better Life Index

The OECD updated the Better Life Index in 2025 to align with its well-being framework and track living conditions, inequalities, and resources shaping future well-being (OECD, 2025).

Rule of Law Index

The World Justice Project’s 2025 index covers 143 countries and jurisdictions and measures rule of law across areas such as rights, justice, corruption, and constraints on power (World Justice Project, 2025).

Corruption Perceptions Index

Transparency International’s 2025 CPI ranks 182 countries and territories from 0 to 100 based on perceived public-sector corruption (Transparency International, 2026).

Global Innovation Index

The 2025 Global Innovation Index ranks Switzerland, Sweden, the United States, South Korea, and Singapore as the top five innovation economies, with China entering the top 10 for the first time (WIPO, 2025).

AWTY Index

AWTY adds the missing layer:
transition readiness.

That is the bridge between macroeconomic indicators and lived pressure.

7. What Critics Will Say

Critics will say this is too complex.

• They will say GDP is objective, clean, and comparable.

• They will say happiness is subjective.

• They will say freedom indices are ideological.

• They will say democracy metrics are Western-biased.

• They will say AWTY is too ambitious because it combines different transitions.

Some of that criticism is fair.

• Every index has bias.

• Every metric simplifies reality.

• Every dashboard can mislead.

But the alternative is worse.

Because GDP also simplifies reality.
GDP counts activity, not quality.

• It counts rebuilding after disasters.

• It counts rent extraction.

• It counts military spending.

• It counts pollution cleanup.

• It counts economic output even when people feel trapped inside the system producing it.

So the real choice is not between perfect and imperfect measurement.

The real choice is between narrow measurement and better measurement.

And in a transition economy, narrow measurement becomes dangerous.

8. The New Prosperity Test

The old prosperity test was simple:

Is the economy growing?

The new prosperity test is harder:

• Can people live freely inside that growth?

• Can institutions correct mistakes?

• Can families afford homes?

• Can workers adapt to AI?

• Can firms survive energy shocks?

• Can societies absorb geopolitical pressure?

• Can democracies remain legitimate when platforms shape attention?

• Can countries move through the five transitions without destroying the middle class?

That is the question.

• GDP cannot answer it alone.

• Democracy cannot answer it alone.

• Freedom cannot answer it alone.

• Happiness cannot answer it alone.

But together, with AWTY, they begin to show the real picture.

That is prosperity now.

I Might Be Wrong 💬🔥

I just explained my opinion on this.

But I might be wrong.

What’s yours?

Is GDP still the best shortcut for prosperity?

Or has it become a dangerous comfort metric?

And here is the more provocative question:

Can a country still be called successful if it gets richer while its citizens become less free, less happy, more anxious, and less able to shape their future?

Drop your view in the comments 👇

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Feeling transition pressure but not sure where to move next?

AI, energy shocks, tariffs, supply-chain disruption, war economy dynamics and infrastructure pressure are already changing the strategic map.

The problem is not uncertainty.

The problem is not knowing which move deserves attention now.

I created the First-Mover Opportunity Radar™ as a 60-minute self-guided diagnostic to help organizations map transition pressure, spot risk blind spots and identify 3 possible strategic moves for the next 90 days. https://payhip.com/b/MJ6AY

Next week, I will go into energy and the green transition.

Are We There Yet?

🔒 Research Corner - Paid Subscriber Section

Research Corner: The Prosperity Dashboard GDP Cannot Build

Paid subscribers get the deeper research map behind this post.

This week’s Research Corner includes:

1. Freedom in the World 2026

Title: Freedom in the World 2025: The Uphill Battle to Safeguard Rights

Source: Freedom House (2025)

Why it matters: This comprehensive report highlights that global freedom has declined for the 19th consecutive year. It reveals a dangerous trend where elected leaders in democracies are actively undermining fundamental institutions like the judiciary and the media, paving the way for digital and physical autocratization.

Strategic Takeaway: Prepare your organization to navigate environments where institutional impartiality is collapsing; as democratic checks and balances erode, your business risks exposure to unpredictable, politically motivated regulatory capture.

Link/DOI: www.freedomhouse.org

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Read more

What if it is weak because nobody will remember it after the funding ends?

In this episode of InnovEU - The EU Project Chronicles, I speak with Christine Lester, founder and director of Minster Development Centre Ltd, in the UK.

Christine has worked for decades across learning, leadership, organisational development, funding, programme delivery, inclusion and European cooperation.

And the lesson is uncomfortable:

• A project can be approved.

• It can deliver the outputs.

• It can produce the reports.

• It can receive a good evaluation.

But if beneficiaries do not use the results, institutions do not absorb the learning, and decision-makers never see the evidence, the project may still leave almost no trace.

In this episode, you’ll discover:

• Why good projects can disappear after funding ends

• Why evaluation should start on day one, not at the final report

• Why deliverables are not the same as legacy

• Why beneficiaries must be reached in the real world

• Why inclusion cannot be treated as an add-on

• How to build consortia around real capability, not formal eligibility

This is not just a conversation about project evaluation.

It is about how EU funded projects turn funding into learning, partnerships into capability, and temporary activities into lasting change.

Because the real question is not only:

Did the project deliver?

The deeper question is:

Will anyone still use what the project created after the funding ends?

▶️ Watch or listen to the full episode

Watch the full episode:

Substack: https://fernandocgaspar.substack.com/podcast

YouTube: https://youtube.com/playlist?list=PLhPsYe80-Wq1bOuVZmZGso_Kr3W8biIpx

Spotify:

Apple Podcasts:

Stress-test your proposal before evaluators do

Quick note before we continue.

Most proposals are not rejected because they are bad.

They are rejected because they trigger silent downgrade logic.

Weak impact logic.

Vague dissemination.

Unclear legacy.

Poor evaluator readability.

A consortium that looks eligible, but not strategically convincing.

The Grantshield Pre-Mortem™ helps teams stress-test the structure of a proposal before submission, using evaluator logic, transition alignment and impact plausibility.

Use it before the evaluator does.

Grantshield Pre-Mortem™: https://payhip.com/b/yUeiV

Go deeper

My book Transition Science In The Economy develops the full framework behind the green transition, digital transition, new globalization, war economy and housing crisis.

It shows why EU funded projects, innovation strategies and investment decisions now need to be designed around structural transitions, not just isolated calls.

Book: https://a.co/d/c7RoOA4

Let’s grow this community together

If you enjoyed the episode:

Like the podcast on your favourite platform.

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And if you would like to be featured on InnovEU - The EU Project Chronicles, send me a private message and let’s schedule it.

If you want to reach Christine Lester:

christine@minsterdevelopmentcentre.co.uk

https://www.linkedin.com/in/christinelester1/

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