US to EU software: you can’t regulate people into better tech

The most obvious problem with a European social media platform and software migration, touted by the EU, is that a political stance is not enough to shift the dial. I’d also like to explore the other reasons for expressing doubt. Network effects don’t care about your values, which is why a ‘European alternative’ rarely become mainstream.

A political push will attract the Europhile fanboys and girls, so become a European BlueSky, a blue bubble of insufferable conformity to the view of almost everyone, part from those who are more libertarian, which is a vast number of even European citizens. Activism and a skewed political push are never wise when it comes to neutral platforms.

The EU’s tech problem isn’t politics,  it is product. We saw this with the European Mastodon and the Fediverse, both disastrous in terms of user experience and never mainstream, despite the efforts to push Mastadon as an alternative to X. BlueSky, is US owned, and has turned into an echo-chamber for those who don’t like X and Musk. These sprove how difficult it is to shift users from the incumbent platforms.

Quality matters

Quality has to be superior to effect a large migration, yet most of the options on the table are significantly inferior to their non-European comparators. There’s a good reason why many have a tiny fraction of users compared to US tech; they are more expensive, unreliable, have poor security and above all have much poorer functionality. 

Europe can’t migrate its way out of big tech

Consumer inertia also means that people stay with what they’ve got, as they are familiar with the platform. You can leave big tech, but your friends won’t. Then there’s the cost of change, losing your past data, friends and have to relearn a new platform and build again from scratch. It’s not often a monetary but time and social cost. But the biggest barrier is the network effect, where you are literally enmeshed in a vast network of friends , others and potential viewers. That’s hard to leave, like leaving home.

Facebook has faced endless claims of imminent death and decline. It has not happened. Network effects and the fact that we are creatures of habit, with a huge backlog of posts and memories on the platform, means we stay. We are not only creatures of habit, we are creatures of memory and identity. 

There’s a reason WhatsApp has not been replaces by Signal and Telegram. Millions said they’d leave but they didn’t. Platforms are sticky and when everyone else is on WhatsApp, you have to disentangle all of your existing social groups, and start again.

Organisational software

With organisational software, most European companies of any size use Teams. This will be fiendishly difficult to shift as they need to reduce risks on cybersecurity, outage and reliability. It is often a procurement as part of a wider Microsoft ‘house’ project. People complain about Outlook, Teams, Office, but the alternative, LibreOffice is worse. There’s also Google Workplace, which has oodles of great functionality. Google has a global revenue model, a massively integrated ecosystem, including its own operating system. It dominates search, video through YouTube and global maps with street view. There is no European entity that comes close to this. Both are solid as they have well-used file formats, are embedded into existing companies and their workflows, and people are familiar with their user interfaces and expected functionality. This inertia is hugely expensive to migrate to other platforms.

One way to push this European initiative is to mandate change in public institutions. But this may only result in even lower productivity in the public sector, compared to the private. That’s the last thing a stagnant economy needs.

Killer objection - AI

But the killer objection, for me, is the failure of Europe to embrace and utilise AI. This could have been a chance to break the US dominance but it is too late. Mistral is good but open source and now a bit player. The US and China are now unstoppable. More worrying is the failure of European companies to embrace AI within their own tech-stacks.

One exception

There is one exception to the rule, that’s Sweden’s Spotify. I’d have said SAP but their shareprice has dropped by a third in six months and they now look like a wounded, if not dying entity, under attack from those who can quickly code alternatives.

Good intentions, bad software

You can’t just legislate them out, there has to be choice, and as long as there is choice European tech companies are largely, but not entirely, screwed. Google, Meta, Microsoft, Amazon, Apple, OpenAI and Anthropic dominate in scale and global reach. Most of those major platform players are US firms but operate extensively in the EU and are regulated locally under laws like the Digital Markets Act, and include WhatsApp, Facebook, Instagram, LinkedIn, TikTok and YouTube.

Europe’s tech strategy is full of good intentions, but bad software. Most of the options are significantly inferior to what is available today. Using outdated technology will only set Europe even further back, through a clear drop in productivity, first in migration, then in poorer functionality and support.

AI Hierarchy of Expertise

There is a widening hierarchy of participants and drivers in AI. This is not unusual, as almost all technological revolutions show this type of fractional distillation.

1) AI creators (working on models & other source techniques)

2) AI experts (implementing structured projects and products)

3) Experts-in-the -loop (using tools like Claude code etc)

4) Humans-in-the loop (using chatbots)

5) Humans who don’t use AI

It is particularly acute in AI, as there are a huge conceptual and competence gaps between these different types of participants.

AI creators

Those who work in the large foundational model companies, such as OpenAI, Google, Anthropic, Meta and others are experts both in the fundamental mathematics and techniques. It is a mistake to focus entirely in the training of models here as the field has widened out into advanced techniques before and after training that are proving effective.

This is the most opaque group, as the surrounding system that makes those models useful, reliable and economical  viable in the real world are highly technical. Companies now rely on techniques to ground responses in external knowledge, tool use and function calling to let models act rather than just speak. They use agentic architectures to plan and execute multi-step tasks and long-term memory to maintain context and personalisation across sessions. There’s also serious evaluation and guardrails, synthetic and curated data pipelines, inference optimisation to cut costs and latency, even human-in-the-loop training. The centre of gravity has shifted to these more mature enhancing techniques because real value now depends on accuracy, trust, controllability, cost and integration into workflows. These problems that are solved at the system and product level rather than in the training run.

AI experts 

These are the companies and researchers who are building the next layer, on the basis of the fundamental technology. They may enable model orchestration, routing tasks across multiple specialised models as a service to customers. They are also creating vertical products in law, healthcare, training, education etc.

Many are involved in RAG and similar techniques to create useful projects and products across and within organisations. This is similar to the growth of internet companies in the early 2000s on top of the internet, now a massive portion of the world economy. I’ve been involved in this layer for over a decade. It’s pioneering, exciting and difficult but without this layer of experimentation the core technology would remain aloof and unusable. It takes commitment by CEOs and a real push to get through prototypes to product.

Experts-in-the-loop

As the technology matures a new group emerged which used more advanced techniques and tools, often the ‘pro’ models. This can be the sophisticated use of AI to handle and use data. But the dominant use has turned out to be coding and on a wider scale media production (text, graphics, images and video). My book on AI and Productivity’ explains why this emergent group is the key to solving the productivity problem and overcoming productivity paradoxes using AI.

Experts bring domain knowledge, this is usaally a mix of tacit and explicit knwoe3ldge and skiils that allow them to get the best out of the available tools. Coders make goof users fo Clade Code, graphic artists of image creation tools and will video creators in creating video. Experienced researchers will get the best out of the technology for research and so on.

Humans-in-the loop

By far the largest group are the ordinary users. One could split them into the two lower fremium groups of paid and free users but both sit on a rising tide of quality and functionality. They tend to single promtt and use the technology as a form of performance support, namely solving an immediate problem; finding something they don't knopw, communications issue, summarising, translating, rewriting and so on. They tend to prompt simply and as the technology improves get more and more bang for their buck.

Humans who don’t use AI

This last group are the disinterested or those who have no real need, which is fine. It also includes those who actively dislike the technology. They have all sorts of objections, some rational, others irrational and ill-informed. This is where the biggest, some say yawning, gap exists.

Least informed but loudest are this last group, at least on social media. They often invoke an eye-roll from the rest, not because they don’t have a point but because the claims are often unsubstantiated or just plain wrong. Typically they will refer to content from old models, rely on anecdote and make claims about the technology hitting brick walls.

Integration

There are people who find ways to integrate AI into organisational culture. They follow the hierarchy, as it depends on the degree of integration. Most organisational institutions focus on encouraging 'humans-in-the-loop' activity, with but this caps your productivity gains at mere text search. Capping effort at 'share prompts' and AI literacy courses, is to neither understand the capabilities of current AI, nor make serious organisational gains in productivity.

Conclusion

We should not be surprised by any of this. The fractional distillation into these sedimental layers of expertise is both a normal and necessary part of the evolution of a technology. In truth, as the technology matures and improves, this hierarchy tarts to massively expand in the middle layers, while the bottom layer shrinks. Think printing, cars, television internet. In this sense resistance is futile.

Ultimately the hierarchy turns in to fully realised rout and it becomes a normalised consumer product woven into the fabric of our lives. That will happen faster tyan most people think, within a few years. It is a hierarchy that automates, thereby blurring the lines, flattening it out and dropping the doubters.

At this stage of AIs development, only 3 years in, the last group is still the largest and therefore the loudest, that does not mean it is the most significant, merely the least informed and practiced.