how will companies navigate the promise and threat of dataism?

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When a small Chinese artificial intelligence lab showed in January how to build a large language model that outperformed OpenAI’s ChatGPT at a fraction of the cost, the tech world went into a tailspin and $1tn was wiped off the stock market in a day.

DeepSeek, founded by hedge fund manager Liang Wenfeng, released its R1 model and detailed how to build on a budget a model that can automatically learn and improve itself without human supervision.

The revelation captured the zeitgeist of China and the US jockeying for global supremacy in technology. Far less attention, however, has been paid to the creep underpinning this modern-day struggle: the rise of “dataism” and its implications for the future of human capital.

Dataism is the belief that by gathering ever more data and feeding it to ever more powerful algorithms alone, businesses can uncover the truth, make the right decisions and create value.

This view challenges many of the foundations of management theory — and the economic interplay between labour and capital — while raising loftier expectations for generative AI.

Businesses are grappling with the future of “knowledge work”, a looming demographic cliff and return to work challenges. Some have postulated other consequences, such as a descent into a post-literate society and a faltering of business as a driver of human development.

Human capital management faces disruption at the level of the individual, company or society on a scale not seen since the industrial revolution. How it navigates both the promise and threat of dataism is its most pressing issue.

Dataism need not be at odds with human capital in an imagined robots versus people future. Rather, human capital management is faced with ciphering through the complex calculus of automation and augmentation simultaneously.

“You can think of automation as a machine that takes a job’s inputs and does it for the worker,” says David Autor, Massachusetts Institute of Technology economist, and “augmentation as a technology that increases the variety of things that people can do, the quality of things people can do, or their productivity”.

Autor’s research shows that one outcome of the interplay between automation and augmentation in the US since 1940 has been the creation of a significant percentage of jobs that represent new types of work — from industrial engineers to nuclear reactor operators and mobile app developers.

However, “the new work is bifurcated [between high-paying and lower-income jobs]”, Autor says. “As old work has been erased in the middle, new work has grown on either side.”

In turn, that bifurcation represents another significant contributor to the increasing urgency faced by human capital management functions.

Success in managing this delicate balancing act will depend largely on human capital management’s efficacy in achieving innovative work design and the sociotechnical systems principle of “joint optimisation” — ensuring that organisational systems are intentionally optimised for value creation (the promise of dataism) as well as quality in humans’ work experience.

Capitalising on the potential of dataism will, of course, require humans, but how those two protagonists interact in generating business value is still playing out — and with significant dependencies on the field of human capital management.

Some aspects of that relationship will be driven intrinsically as knowledge work evolves and détente is reached regarding remote work. Others will be driven by forces such as the global AI race and demographic shifts across the developed world.

This will take place against the shifting forces of automation and augmentation.

As that evolution unfolds, what is frequently framed as a zero-sum competition for sovereign hegemony can be seen in parallel as a step change in reframing the role of human capital in business.

Tom Davis is clinical assistant professor of business administration at the University of Pittsburgh, Joseph M Katz Graduate School of Business