When you purchase through links in our articles, we may earn a small commission. AI is cheaper than workers. Could an AI token tax keep businesses from replacing human jobs? The idea is gaining traction, and the Microsoft co-founder says he's on board. AI is about to change everything, and we're not ready.

Creator Playbook Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Mashable Voices Look Up Trending Now Say More Mashable Selects Safety Net Versus Gift Ideas For Everyone On Your List Switch Off In My Bag All Series Bill Gates proposes a'token tax' on AI to protect human workers In a wide-ranging interview with the New York Times, Gates said that some jobs should be Human Reserved. Former Microsoft head Bill Gates has a few ideas on how to deal with AI replacing human workers. As anti-AI sentiment continues to spread, one tech industry leader is now floating an idea for how to deal with it. In a 6,000-word essay posted on his Gates Notes website, billionaire and former Microsoft head Bill Gates explains his fears about artificial intelligence and offers a few proposals for addressing them. Gates also discussed his concerns in a wide-ranging interview with the .

Look Up Say More Versus Creator Hub Switch Off Mashable's Best: E-readers, robovacs, laptops, earbuds, smart home and more Trending Now Safety Net In My Bag VidCon with Mashable Back to School Furtastic All Series Save on laptops, generators, and more. Tabitha Britt is an award-winning freelance journalist, editor, and SEO/AEO strategist. Aside from reviewing dating apps and sex toys for Mashable, Tabitha is also the founding editor-in-chief of DO YOU ENDO -- a digital magazine by individuals with endometriosis, for individuals with endometriosis. She has a Master's degree in Creative Publishing and Critical Journalism from The New School for Social Research and is a grad of Sextech School. You can find more of her work in various online publications, including,, and .

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Why $370bn tech group Palantir pays 1.4 percent tax rate: Report Palantir Technologies, the United States data analytics and artificial intelligence company which has contracts with the country's military and intelligence apparatus, has "engineered its corporate structure" to pay no US federal corporate income tax, according to a new report. The study by the Centre for International Corporate Tax Accountability and Research (CICTAR) comes as Palantir reports soaring revenues, driven partly by government contracts, while it faces continued criticism for providing technology to the Israeli military amid the genocide in Gaza. What does the report show? Earlier this week, Palantir reported second-quarter revenue of $1.94bn, up 93 percent from a year earlier. But despite its rapid growth, CICTAR said its global effective tax rate was just 1.4 percent in 2025.

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Things to Do in L.A. Tap to enable a layout that focuses on the article. This is read by an automated voice. Please report any issues or inconsistencies here . See more from the L.A. Times in Google Search. More than three dozen California legislators are calling for Gov. Gavin Newsom to exempt the state's film and TV production incentive program from a recently approved cap on corporate tax credits, warning that without action it will be "significantly kneecapped."

Direct preference optimization (DPO) is widely used as a simple and stable method for aligning large language models (LLMs) with human preferences. This paper investigates a generalized DPO loss that enables a policy model to match the target policy from a likelihood ratio estimation perspective. The ratio of the target policy provides a unique identification of the policy distribution without relying on reward models or partition functions. This allows the generalized loss to retain both simplicity and theoretical guarantees, which prior work such as f-PO fails to achieve simultaneously. We propose Bregman preference optimization (BPO), a generalized framework for ratio matching that provides a family of objective functions achieving target policy optimality.

The DeceptionBench is designed as a research benchmark to systematically study deception behaviors in LLMs, fostering a deeper understanding of their decision-making processes in real-world scenarios. Our primary intent is to provide a standardized, transparent tool for the research community to evaluate and improve LLMs' ethical alignment, not to enable or encourage deceptive practices. To prevent potential misuse by malicious actors, we commit to publicly releasing all evaluation data under an open license. This transparency ensures that DeceptionBench's methodology and outcomes are subject to scrutiny, replication, and improvement by the research community, reducing the risk of hidden exploitation. By prioritizing openness, we aim to advance responsible AI development while safeguarding against misuse in harmful contexts. The field of Large Language Models (LLMs) has undergone remarkable evolution in recent years, reshaping the landscape of natural language processing.

Despite the remarkable advances of Large Language Models (LLMs) across diverse cognitive tasks, the rapid enhancement of these capabilities also introduces emergent deception behaviors that may induce severe risks in high-stakes deployments. More critically, the characterization of deception across realistic real-world scenarios remains underexplored. To bridge this gap, we establish DeceptionBench, the first benchmark that systematically evaluates how deceptive tendencies manifest across different societal domains, what their intrinsic behavioral patterns are, and how extrinsic factors affect them. Specifically, on the static count, the benchmark encompasses 150 meticulously designed scenarios in five domains, i.e., Economy, Healthcare, Education, Social Interaction, and Entertainment, with over 1,000 samples, providing sufficient empirical foundations for deception analysis. On the intrinsic dimension, we explore whether models exhibit self-interested egoistic tendencies or sycophantic behaviors that prioritize user appeasement. On the extrinsic dimension, we investigate how contextual factors modulate deceptive outputs under neutral conditions, reward-based incentivization, and coercive pressures. Moreover, we incorporate sustained multi-turn interaction loops to construct a more realistic simulation of real-world feedback dynamics. Extensive experiments across LLMs and Large Reasoning Models (LRMs) reveal critical vulnerabilities, particularly amplified deception under reinforcement dynamics, demonstrating that current models lack robust resistance to manipulative contextual cues and the urgent need for advanced safeguards against various deception behaviors.