Should AI Be Communist?

When I reflect on how AI should be governed, I often draw parallels to my upbringing in Eastern Europe. Growing up in communist Czechoslovakia, children like me were funneled into rigorous training programs from an early age of 6 some even earlier. Whether it was football, ice hockey, or swimming, we were placed in specialized sports schools, and from age six, the routine became everything.

The idea was simple: out of a mass of children who had trained intensely for years, a few would rise to the top and become stars—not necessarily because they were born with natural talent, but because they had been trained, guided, and conditioned to succeed. I was obviously not one of these sport geniuses ;).

However, this structured approach to nurturing talent is something I think about often when it comes to the future of work, particularly in the context of AI. If we’re not upskilling and reskilling people to meet the demands of AI, we’re setting ourselves up for failure. And it’s not just about teaching technical skills—it’s about preparing workers, teams, and organizations for a future dominated by AI, ensuring they can thrive in this new landscape. According to McKinsey's research automation and AI could potentially displace a significant number of workers by 2030. In their midpoint scenario, around 15% of the global workforce (about 400 million workers) could be displaced and in their fastest adoption scenario, up to 30% of the global workforce (about 800 million workers) could be displaced.

The Urgent Need for AI Upskilling

In a world increasingly shaped by AI, upskilling the workforce is not just a "nice to have"—it’s critical for companies that want to remain competitive. A recent BCG report which was published by Vinciane Beauchene outlines five key actions that organizations must take to ensure successful AI upskilling, and the research makes it clear: companies that invest in AI now gain a significant competitive edge. These companies benefit from superior innovation, improved productivity, and higher revenues. For example, early adopters of Generative AI (GenAI) have seen up to $1 billion in additional profits, with a significant portion of that gain realized within the first 18 months.

But here’s the problem: many companies are lagging behind. In BCG’s survey of 1,400 C-suite executives, only 6% said they had begun meaningful upskilling efforts related to AI. The same survey revealed that 62% of leaders cited a shortage of talent and skills as their biggest challenge, with many expressing ambivalence or dissatisfaction about their progress in adopting AI. Clearly, upskilling is a major bottleneck.

BCG’s research outlines five success factors that can help overcome this bottleneck:

1. Assess Needs and Measure Outcomes: Organizations must thoroughly assess what skills are required and tailor learning programs to meet those needs. The process must be strategic, with systems in place to measure the success of upskilling efforts.
2. Prepare People for Change: People need to understand not just how to use AI tools, but how their roles will evolve. In my last Human Stories, I talked about "Humans and API end Points" Companies should focus on preparing individuals, teams, and entire organizations for the changes AI will bring.
3. Incentivize Learning: People aren’t always keen to learn AI—many see it as a threat. To overcome this reluctance, organizations need to tap into both intrinsic and extrinsic motivators. Gamification, digital nudges, and peer groups can all help unlock people’s willingness to learn.
4. C-Suite Commitment: AI upskilling cannot be successful without full buy-in from the C-suite. Senior leaders need to advocate for AI adoption and set the tone for the rest of the organization. This means embracing AI in their own work and modeling the desired behaviors for the rest of the company.
5. Use AI for AI Upskilling: To make learning impactful, scalable, and fast, companies should use AI tools to help upskill their people. The more AI is embedded into daily work, the more a network effect occurs, amplifying learning and innovation across the organization.

The evidence is clear: companies that invest in AI and upskilling now are positioning themselves for long-term success. But for those that drag their feet, the risk of falling behind grows every day.

A Lesson from Mustafa Suleyman’s "The Coming Wave"

While companies race to adopt AI, we also need to think critically about how to contain its risks. Mustafa Suleyman , co-founder of Google DeepMind and CEO of 谷歌 AI, addresses this in his book The Coming Wave (another of my favorite reads) . He outlines 10 principles for managing AI safely, and they are not just recommendations—they’re urgent actions we must take if we are to keep AI under control:

1. Safety: Prioritize technical safety research and development, akin to an Apollo program, to ensure AI systems are reliable and secure.
2. Audits: Conduct regular audits of AI systems to promote transparency and accountability. Knowledge about these systems is crucial for effective control.
3. Choke Points: Establish strategic "choke points" by limiting the sales of critical components like chips and controlling the export and import of sensitive technologies.
4. Makers: Encourage critics of AI to actively participate in building and improving the technology to foster a more balanced perspective and responsible development.
5. Businesses: Promote a sense of purpose beyond profit within businesses developing AI, ensuring ethical considerations are integrated into their practices.
6. Governments: Urge governments to adapt, reform, and effectively regulate AI technologies to ensure their responsible use and maintain control.
7. Alliance: Foster global treaties and alliances to establish international standards and regulations for AI, promoting cooperation and preventing a race to the bottom.
8. Containment: Recognize that perfect containment of AI may not be possible and focus on managing the risks and consequences of its spread.
9. Citizens: Empower citizens with the knowledge and tools to navigate the changing technological landscape and participate in shaping the future of AI.
10. Meaning: Engage in a deeper societal discussion about the purpose and meaning of life in an age of increasingly powerful technology.

Suleyman’s vision is clear: AI isn’t something we can leave to chance. It requires oversight, regulation, and a global effort to ensure it serves humanity, rather than controlling us. The speed at which AI is developing means we don’t have the luxury of waiting—we must act now.

As AI reshapes the future of work, it’s clear that upskilling is no longer optional. Companies and individuals alike must commit to learning the skills needed to harness AI’s potential while mitigating its risks. By investing in structured, purposeful upskilling initiatives, we can not only survive in an AI-driven world but thrive in it.

But success won’t come from blind enthusiasm or resistance. It will come from taking a structured, strategic approach—much like those rigorous training programs I grew up with in Eastern Europe. Just as we trained young athletes from a young age, we now need to train workers, leaders, and citizens for the AI era. Because if we don’t build the right skills now, we’ll find ourselves at the mercy of technologies we can’t control.