Artificial Intelligence causes jobs in offices, warehouses, call centers, clinics, classrooms and more. With the backlash between the displaced workers, the industry and its advocates will have to bend in cash – transfer plans – universal basic income and its kind – a release valve.
While surrounding the gatekeepers, this path will be paper on chaos. This will weaken the power of bargaining on work and create new and unexpected social and fiscal dependence.
We can do better than that. We have the opportunity to turn the trajectory, level the playground and ensure that the public paying for AI’s externality also benefits from its returns.,
What is this? Open and competitive market; Modern data rights; Liability that controls control; Guard-racing technical changes to serve shared good; And most importantly, public sharing in the reverse of AI.
The United States should set up a professional, installation of firewalls, untouched by public AI wealth funds, which have diverse bets in AI stack and distributes an annual dividend tied to AI Oplid (ie Real Return).
This will focus on public dividends, not on basic income; This will give the public a meaningful, permanent share in the returns of AI, while competition to keep the field open, uses data, labor and energy rules and loss in investigation.
The deal between Nvidia and AMD and the US government, which includes payment 15 percent of revenue from sales of some advanced chipsThis public AI provides an accurate platform to kick-start the construction of wealth funds.
Capital with equity warrants from large subsidy, tax credit or preferential contracts receive; A calculation above the rule was determined by excise rulmaking; A windfall surs benefit on the extraordinary AI margin; And location – based fees where data centers attract subsidized energy or water.
Adopt a simple fiscal rule: Protect the principal and pay a universal dividend from the returned returns. The dividend will start moderately and grow as a mature of funds, align public returns with performance instead of permanent deficit finances.
The reverse sharing must be complement – not replaced – open market and personal economic choice.
The Congress should behave as exclusive deals, as an antagomatative once after completing the market trigger; Bar self -promotioning in AI Marketplace; And filter the tie “model – plus’ platform” as potential vertical restrictions. The ability to move the interoperability and portability – API, export format, and fine – should be default, with clear timers and contractual exit rights. These rules ensure that the public is not forced to buy in the obstacles of entry.
Individuals require a clear opt for the use of personal data in training, the right to find out whether their data was used, and measures for unauthorized use. For high IS risk uses – hiring, housing, health care, finance and critical infrastructure – deployment should bear a duty of care: Documentary impact assessments, human ‘ – in human’ – loop schemes, events reporting and audit trails. The burden should rest on those who benefit from deployment to show that they fulfill these duties.
The Federal Trade Commission can investigate misleading AI claims, dark patterns and data misuse. Exclusion in the Department of Justice and FTC contracts can challenge bundles and uniqueness. Securities and exchange commissions may require disclosure of physical workforce effects from the deployment of AI, not only AI “strategies”. Civil of rights agencies can implement clarity, auditing and appeal rights in automated decisions. Energy and water regulators may require convenience from hypersscale data centers and interconnects on demand and response and conservation plans.
Executive branch and states should move forward in parallel. States should integrate their own AI wealth funds with data and percentage and resilient revenue and equity warrants with subsidized projects – but independent of national funds. A federated research cloud in universities, National Labs and State Innovation Hubs will provide students with access to shared AI infrastructure without being dependent on the seller credit.
Ask schools, employers and hospitals what systems they use, how they were evaluated, and how you can decide. Emphasized that public dollars tied to AI come with ownership, transparency and exit rights.
Talk to your elected officers and those who are running for the office; Tell them that this is time for our nation to create a public AI wealth fund.
John Dawadoss was a general manager in Microsoft for two decades. He did his Ph.D. Work in AI at the University of Massachusetts in Mahrst, specialized in machine learning.
