GoKawiil
Tech News
LinkedIn, owned by Microsoft, is rolling out a feature enabling verified companies to strip false employment claims from user profiles. The tool targets people who list jobs they never actually held at a given organization.
Reporting revisits comments made by leading AI company founders, including figures behind OpenAI and Anthropic, who warned of existential risks from artificial intelligence even as they built their firms. These statements, made years before recent viral discussions, framed advanced AI as a potential threat to humanity while the same executives pursued its development. The resurfacing of these quotes has fueled renewed scrutiny of the tension between AI leaders' public warnings and their commercial ambitions.
Food companies are facing growing consumer pressure to shorten ingredient lists and use recognizable, simpler ingredients, without sacrificing taste, texture, nutrition, consistency, or price. Meeting these demands requires manufacturers to rethink product development from the end result backward, applying more sophisticated formulation approaches even as labels appear plainer.
A commentary argues that tech companies are increasingly designing AI products to make it effortless for people to offload cognitive tasks, drawing a parallel to how fast food chains capitalized on consumers' desire for convenience. The author recounts a personal anecdote involving their accountant that revealed how deeply AI-driven automation has quietly crept into everyday professional decisions without public awareness.
Former FTC Chair Lina Khan argued on X that regulators don't need new legislation to rein in AI companies, pointing to decades-old consumer protection and antitrust statutes as tools to hold firms and executives accountable. She cited product liability and unfair competition laws as grounds to prosecute companies releasing unvetted or defective AI systems, or engaging in risky behavior that pressures rivals to match it.
Former FTC Chair Lina Khan argues that regulators do not need new legislation to hold AI company executives accountable, pointing to established legal precedent such as a 1934 Supreme Court ruling that barred a competitive 'race to the bottom' among companies. Her comments push back on the idea that AI's novelty exempts its leaders from existing corporate and antitrust law.
New federal rules, including a proposed $100,000 fee on H-1B visa applications introduced in September 2025, are adding to a string of immigration restrictions on skilled foreign workers. IEEE Spectrum found that U.S. tech companies are responding by expanding hiring and opening offices abroad rather than sponsoring visas domestically, though none of the 25 firms contacted would discuss the shift publicly.
A group of drug companies trained an open-source AlphaFold 3 replica called OpenFold3 on more than 20,000 proprietary protein structures pooled from their private vaults. The resulting model outperformed versions trained only on public data or on any single firm's siloed dataset, according to an unpublished, non-peer-reviewed blog post.
Employees are increasingly suspicious of coworkers who use AI tools, largely because organizations haven't established clear guidelines on when and how AI use is appropriate. This ambiguity is creating friction and eroding trust among team members who lack a shared understanding of acceptable practices.
A group of mathematicians argues that while large language models have rapidly gained the ability to solve major outstanding problems, AI companies' drive to treat these solutions as benchmarks conflicts with how mathematics actually operates as a discipline. They describe this as a broader misalignment between AI industry goals and the values of the mathematical community, which relies on slow, collective processes of verification, teaching, and simplification rather than one-off problem-solving feats.
A group of 25 Fields Medal-winning mathematicians has issued a joint declaration warning that AI companies' rush to use famous unsolved math problems as benchmarks for large language models is harming the discipline. They argue that while LLMs have rapidly gained the ability to crack major open problems, this benchmark-driven approach conflicts with how mathematical knowledge has traditionally been built and validated over generations.
Apple continues to frame its artificial intelligence efforts around everyday usefulness rather than grand claims of achieving superhuman or godlike machine intelligence. This contrasts with rivals who often emphasize dramatic warnings or promises about AI's transformative, even existential, power.