Choose your AI future before it chooses you

Enterprise leaders like you have happily and cozily operated within a relatively stable social contract since the end of World War II: Governments set the rules, capital provided the fuel, and labor turned the growth engine. Firms were welcome to operate, attract consumers, and reap dividends in return for jobs and tax revenue. That bargain is breaking down.

AI is restructuring the foundations of value creation, workforce design, and economic legitimacy. This could lead in one of three directions, each outlined in the scenarios we explore in this paper: Techno-feudalism, democratic AI commons, or algorithmic corporatism. What you decide today will shape what is to come— and the role your organization will play in it.

AI has broken the link between labor and value creation

Enterprises once needed large teams of people to do, well, pretty much anything—from innovate to produce to sell to deliver. Now, a few hundred engineers hooked up with LLMs and agents can unlock billions in market cap. The world’s most valuable firms today often have the fewest employees per dollar of revenue—such as OpenAI, with a valuation of $300bn and a headcount of 4500. In comparison, Accenture has a market cap of sub $200bn with a headcount of around 775,000. Scaling revenue without huge headcounts is becoming commonplace (see Exhibit 1).

Exhibit 1: AI is breaking the traditional link between labor and value creation

Source: HFS Research, 2025. Examples are illustrative of a broader trend

Services become software, and labor’s role as producer shrinks

As AI decouples economic growth from labor (as captured in the HFS 2028 Tech-Services Vision in which labor arbitrage is replaced by tech arbitrage—see Exhibit 2), the political and social systems that held post-war capitalism together start to wobble. Consequently, labor’s role as producer shrinks.

Exhibit 2: Services-as-Software looms—HFS now predicts its arrival by 2028

Source: HFS Research, 2025

Some governments, recognizing this disruption, are beginning to realign—shifting from a post-industrial pro-market stance to a more interventionist posture in defense of labor, sovereignty, and trust (enshrined in the EU’s AI Act, for example). Others are aligning themselves to capital, offering greater freedom to tech leaders such as the US support for the $500bn Stargate initiative.

This moment demands more than tactical AI adoption. It runs deeper. It can either happen to you, or you can help shape it. You are no longer just choosing between cloud vendors or model architectures. You are navigating three possible futures—each with radically different implications for how firms create value, compete, and justify their existence to society (see Exhibit 3).

By exploring these three futures, you can prepare for each.

1. Techno-feudalism: AI becomes the lord of the digital manor

The rapid advance of AI has created a new kind of value chain—one that replaces labor with technology arbitrage. Sitting at the apex, we find the digital lords: hyperscalers, frontier model owners, and platform monopolies. These folk don’t need large workforces or even massive consumer markets to thrive. Their power lies in owning the pipelines—data, infrastructure, and models.

This represents a new kind of feudalism. AI enables revenue without large headcounts; platforms generate rents from digital tenants (users, developers, enterprises) who pay to access compute, intelligence or reach. With a handful of players concentrating control, enterprise innovation risks being stifled unless leaders break free of dependency.

AI creates the possibility of an economy where production and consumption are orchestrated with minimal human involvement. For many firms, this may feel like liberation from ever-spiraling headcount, but for the societies in which you operate, it’s likely to be destabilizing.

How to prepare:

• Conduct dependency audits on your AI value chain: Are you overly reliant on one or two AI platforms?
• Invest in AI sovereignty: Explore open-source models and fine-tuning in secure private environments.
• Collaborate with regulators to support frameworks that preserve competitive pluralism.
• Upskill internally so you’re not just renting intelligence—you’re building it.

2. Democratic AI commons: The cooperative infrastructure of the post-capitalist firm

In contrast to feudal control, an alternative is emerging—one rooted in transparency, participation, and collective ownership of AI infrastructure—the democratic AI commons. This scenario imagines AI not as a proprietary rent-extracting tool but a public good developed through partnerships between governments, citizens, open-source communities, and enterprises.

This future gains credibility as countries such as France, India, and Japan invest in sovereign AI programs and public digital infrastructure. Organizations such as Hugging Face and Stability AI push for transparency, auditability, and accessibility. The promise is that AI becomes a multiplier of social inclusion rather than inequality.

If you want to make this happen, it will take more than ethical posturing. Get busy co-creating with communities, aligning with open standards, and contributing to public AI stacks. In this scenario, firms are not only profit engines but good digital citizens accountable to broader societal interests.

How to prepare:

• Engage in open model ecosystems and standards-setting bodies such as the Open AI Alliance, Model Context Protocols (MCPs), and MIT Media Labs’ NANDA platform for an internet of agents.
• Contribute code, datasets, or governance mechanisms to public commons initiatives.
• Develop ethical AI leadership as a core C-suite competency, not a side function.
• Build trust architectures into product design, especially where AI mediates high-stakes decisions.

3. Algorithmic corporatism: A new social contract for AI

Between unregulated techno-feudalism and AI democracy lies a more nuanced and necessarily negotiated path: algorithmic corporatism. In this model, states and corporations jointly manage AI development through formalized collaboration, infrastructure partnerships, and regulatory frameworks.

Signs of this future are already here:

• The US Executive Order on AI
• The EU AI Act for high-risk AI applications
• India’s Digital Public Infrastructure (DPI), a reference point for digital public-private cooperation

In this world, AI development is not left to either the market or the state—it is co-governed. Enterprises participate in setting norms and earning licenses to operate. The upside is stability, inclusion, and legitimacy. The cost? Expect to have to be more accountable with limits on pure profit maximization.

How to prepare:

• Establish internal AI governance boards and participate in external consortia.
• Design compliance agility into your AI operations and supply chains.
• Appoint a Chief AI Governance Officer to sit at the intersection of legal, technical, and strategy.
• Monitor AI regulation globally and shape the agenda rather than merely react to it.

Exhibit 3: Each future scenario impacts enterprise power and comes with risks and opportunities you must plan for

Source: HFS Research, 2025

The Bottom Line: AI is the engine of growth, but leadership is still the steering wheel. Choose your road before it chooses you.

This is not a time to get caught in the headlights – you need to be at the steering wheel. Now is the moment you must:

• Choose the ecosystem you want to build in
• Contribute to governance structures proactively
• Design for agility, legitimacy, and long-term participation

AI is the engine of growth, but leadership is the steering wheel. Choose your road before it chooses you.

Learn more from these references:

1. Yanis Varoufakis – Techno-Feudalism: What Killed Capitalism → Link
2. Shoshana Zuboff – The Age of Surveillance Capitalism → Link
3. Elinor Ostrom – Governing the Commons → Link
4. Hugging Face – Open-source model governance → Link
5. Digital Public Goods Alliance → Link
6. European Union AI Act→ Link
7. India’s Digital Public Infrastructure (DPI) → Link