## Why Data Should Be Shared

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### Feedback effect and market concentration
- Google trained its spellchecker "some 20 years ago" with typos "billions" of users made when searching; this created a durable competitive advantage and "no competitor could come close."
- "9 in every 10 internet searches" are accounted for by Google; a "recent antitrust ruling" imposed new restraints.
- AI magnifies the "feedback effect": massive information and processing power are required to train and tune AI models, resources that "large internet platforms have in spades."
- Six Big Tech companies—Alphabet (Google), Netflix, Meta, Apple, Amazon, and Microsoft—account for "almost half the world’s internet traffic."
- "Four of these—Alphabet, Microsoft, Meta, and Amazon—dominate AI computing capacity."
- The feedback effect is self-reinforcing: "As more data lead to better products and services, the largest players attract more customers, generating even more data," creating market concentration dynamics that less data-rich competitors cannot join.

### Effects on conventional firms and innovation
- Conventional companies often lack processing power, technical skills, and crucially a "data mindset"—the realization that using data creates value.
- Surveys show that "at least 80 percent of what’s collected worldwide is never used," weakening incumbent firms' capacity to innovate.
- Dominant platforms may also "lose interest in innovation once they become dominant" and prioritize protecting market share over improving offerings, allowing them to "weaken their offerings and still keep their substantial market share."

### Limitations of existing policy approaches
- "Using antitrust and competition regulation to break up large data platforms tackles the symptoms but not the cause of data concentration." Breaking up a platform may simply allow another to rise, without changing the underlying data-access dynamic.
- Policies granting individuals more control over personal data (for example, the EU’s General Data Protection Regulation) routinely fail to counter data concentration because of collective-action problems: "many people care about their personal data, but few exercise their right to control it."
- Legal ownership or exclusion rights over data face practical hurdles; complex licensing and transaction costs "disproportionally burden individuals and small start-ups," potentially reducing overall data accessibility and tilting the field toward large platforms.

### Case for data access mandates (policy recommendation)
- Regulations that "mandate access to data, especially nonpersonal data," offer greater promise by reducing transaction costs—"no licenses need be negotiated"—and enabling smaller firms to access data to become data savvy.
- Data access mandates shift incentives toward using data rather than hoarding it: "If data holders can extract value only by using the data themselves, it motivates those who have it to use it," encouraging broader data use and ideation.
- Multiple players applying ideas to shared data rewards ideation over hoarding; "the secret is often in data’s clever application, or use, rather than its collection."

### Precedents and historical examples supporting access mandates
- Governments already require access to valuable data in multiple domains: GPS (US military) and the EU’s Galileo system provide costless positional data that "begot a multibillion-dollar industry" and improved safety and logistics.
- Laws often require companies to make certain data public, "from financial results to emissions data."
- The EU has begun mandating that "large digital platforms must now share some data with smaller competitors."
- In the US, "antitrust settlements have repeatedly required companies to let competitors access their data," including a recent requirement on Google.
- A landmark antitrust settlement in the 1950s required AT&T to let US firms use its transistor patents for free; startups used this to design integrated circuits, "essentially bootstrapping Silicon Valley and the digital age."
- The patent system itself balances limited exclusive use with the requirement to "share the details of their invention so that others can learn from it," illustrating a precedent for regulated knowledge sharing.

### Conclusion and policy implication
- As the world transitions toward a comprehensive data economy, the value data can generate for innovation will increase but so will concentration dynamics and associated spillover costs.
- Among suggested interventions, "data access mandates hold the most promise" because they lower transaction costs, broaden who can innovate with data, and reward ideation and use rather than mere collection or possession.

*Viktor Mayer‑Schönberger — DECEMBER 2025, F&D Point of View*

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_Source: https://www.imf.org/-/media/files/publications/fandd/article/2025/12/mayer-schnberger.pdf_