A person holds a fake $1,000 bill signed by former Democratic presidential candidate Andrew Yang following a campaign event in Iowa City, Iowa, on 29 January 2020. A person holds a fake $1,000 bill signed by former Democratic presidential candidate Andrew Yang following a campaign event in Iowa City, Iowa, on 29 January 2020. Why universal basic income still can't meet the challenges of an AI economy Andrew Yang's revived pitch suits the automation debate, but UBI can't fix inequalities concentrated tech wealth drives Universal basic income (UBI) is back, like a space zombie in a sci-fi movie, resurrected from policy oblivion, hungry for policymakers' attention: brains! Andrew Yang, whose "Yang Gang" enthusiasm briefly shook up the Democratic presidential nomination in 2020 promoting a "Freedom Dividend" to save workers from automation - $1,000 a month for every American adult - is again the main carrier of the bug: offering UBI to save the nation when robots eat all our jobs. This time Chat GPT, Yang hopes, will help his argument land: if artificial intelligence truly makes human labor redundant, as so many citizens of the tech bubble in Silicon Valley expect, society will need something other than employment for all of us to make ends meet.

We derive the first closed-form condition under which artificial intelligence (AI) capital profits could sustainably finance a universal basic income (UBI) without relying on new taxation or the creation of new jobs. In a Solow-Zeira task-automation economy with a CES aggregator $σ< 1$, we introduce an AI capability parameter that scales the productivity of automatable tasks and obtain a tractable expression for the AI capability threshold -- the minimum productivity of AI relative to pre-AI automation required for a balanced transfer. Using current U.S. economic parameters, we find that even in the conservative scenario where no new tasks or jobs emerge, AI systems would only need to reach only 5-7 times today's automation productivity to fund an 11%-of-GDP UBI. Our analysis also reveals some specific policy levers: raising public revenue share (e.g. profit taxation) of AI capital from the current 15% to about 33% halves the required AI capability threshold to attain UBI to 3 times existing automation productivity, but gains diminish beyond 50% public revenue share, especially if regulatory costs increase. Market structure also strongly affects outcomes: monopolistic or concentrated oligopolistic markets reduce the threshold by increasing economic rents, whereas heightened competition significantly raises it. These results therefore offer a rigorous benchmark for assessing when advancing AI capabilities might sustainably finance social transfers in an increasingly automated economy.

Our socioeconomic system is facing an existential threat from AI. In our capitalist society, most people depend on jobs to sustain themselves. The U.S. government, in turn, relies heavily on taxing the income of individual workers for revenue. As artificial intelligence progressively eliminates job opportunities, a growing number of individuals will face severe job insecurity, leading to a corresponding decline in federal revenue. Radical action is needed now to steer away from a dystopian collapse toward better possibilities.

When Elinor O'Donovan found out she had been randomly selected to participate in a basic income pilot scheme, she couldn't believe her luck. In return for a guaranteed salary of just over 1,400 ( 1,200) a month from the Irish government, all the 27-year-old artist had to do was fill out a bi-annual questionnaire about her wellbeing and how she spends her time. "It was like winning the lottery. I was in such disbelief," she says. The income, which she will receive until September 2025, has enabled her to give up temping and focus instead on her art.

Driving Automation Systems (DAS) are subject to complex road environments and vehicle behaviors and increasingly rely on sophisticated sensors and Artificial Intelligence (AI). These properties give rise to unique safety faults stemming from specification insufficiencies and technological performance limitations, where sensors and AI introduce errors that vary in magnitude and temporal patterns, posing potential safety risks. The Safety of the Intended Functionality (SOTIF) standard emerges as a promising framework for addressing these concerns, focusing on scenario-based analysis to identify hazardous behaviors and their causes. Although the current standard provides a basic cause-and-effect model and high-level process guidance, it lacks concepts required to identify and evaluate hazardous errors, especially within the context of AI. This paper introduces two key contributions to bridge this gap. First, it defines the SOTIF Temporal Error and Failure Model (STEAM) as a refinement of the SOTIF cause-and-effect model, offering a comprehensive system-design perspective. STEAM refines error definitions, introduces error sequences, and classifies them as error sequence patterns, providing particular relevance to systems employing advanced sensors and AI. Second, this paper proposes the Model-based SOTIF Analysis of Failures and Errors (MoSAFE) method, which allows instantiating STEAM based on system-design models by deriving hazardous error sequence patterns at module level from hazardous behaviors at vehicle level via weakest precondition reasoning. Finally, the paper presents a case study centered on an automated speed-control feature, illustrating the practical applicability of the refined model and the MoSAFE method in addressing complex safety challenges in DAS.

The idea of a guaranteed income for all has been floating around for centuries, its popularity ebbing and flowing with the passing tide of current events. While it is still considered by many to be a radical concept, proponents of a universal basic income (UBI) no longer see it only as a solution to poverty but as the answer to some of the biggest threats faced by modern workers: wage inequality, job insecurity – and the looming possibility of AI-induced job losses. Elon Musk, at the recent Bletchley Park summit, said he believed "no job is needed" due to the development of AI, and that a job can be for "personal satisfaction". Economist and political theorist Karl Widerquist, professor of philosophy at Georgetown University-Qatar, sees it differently. "Even if AI takes your job away, you don't necessarily just become unemployed for the rest of your life," he says.

New AI initiatives are utilizing synthetic intelligence to streamline the event course of by automating repetitive duties. While the purpose is to optimize effectivity, issues have been raised concerning the potential affect on employment charges and the economic system. This article explores the price of AI effectivity and the potential want for Universal Basic Income (UBI) as an answer. UXOS AI and different comparable initiatives are working to streamline the event course of and scale back growth time and value. It operates on the Binance Smart Chain community and creates a custom-made set of instruments to automate the event course of. While this strategy can considerably improve effectivity and pace up mission completion, there are issues concerning the potential affect on employment charges and the economic system usually.

There's no denying that automation is a long-term goal for many industries. To automate a business is to transform it for the better, and there are so many different ways to achieve such a goal, particularly with AI (artificial intelligence). Even startup owners are looking toward automation sooner rather than later, as automation can help businesses scale without growing pains. That said, what about the world of insurance? While automation affects every aspect of the business sector, it's understandable to be confused about how it might impact the insurance space.

Insurance has traditionally operated on generalities and assumptions about people and behavior to determine coverage for customers. Young people are more likely to get in car accidents than more experienced drivers and pay higher premiums for their car insurance, even though that generalization fails to hold for every individual. The introduction of usage-based insurance, however, has created an effective means to provide the right insurance for the right price -- based on specifics, not assumptions. Now, UBI is about to gain a power-up through the robust computation capabilities of artificial intelligence. Here's a look at how AI-driven UBI might impact the industry. "UBI is like a traditional insurance product with one particular difference: It's tied to something that's measured on a recurring basis, which then informs how the premium is adjusted," says Doug McElhaney, a partner at McKinsey.

It was December 2020, and she was being invited into a pilot program providing guaranteed income--a direct cash transfer with no strings attached. For Softky, it was a lifeline. "For the first time in a long time, I felt like I could … take a deep breath, start saving, and see myself in the future," she says. The idea of "just giving people money" has been in and out of the news since becoming a favored cause for many high-profile Silicon Valley entrepreneurs, including Twitter's Jack Dorsey, Facebook cofounders Mark Zuckerberg and (separately) Chris Hughes, and Singularity University's Peter Diamandis. They proposed a universal basic income as a solution to the job losses and social conflict that would be wrought by automation and artificial intelligence--the very technologies their own companies create.