GoKawiil
Tech News
Researchers demonstrated that large language models can analyze scattered public information—writing style, posting patterns, and incidental details—to identify the real people behind pseudonymous social media accounts with notable accuracy. The technique relies on piecing together fragments of publicly available data rather than any single identifying clue.
An Entrepreneur contributor argues that while AI coding agents have been rapidly embraced by software teams, autonomous agents for go-to-market functions like sales and marketing remain underused. The piece attributes this gap not to AI capability limits but to fragmented, duplicated sales data that lacks connection to reliable external signals needed for commercial decisions.
An engineering firm's COO ran AI agents and large language models across the company's full software development lifecycle to test their real impact on production projects rather than demos. The experiment found AI most useful for automating repetitive boilerplate code, catching edge-case bugs during review, and speeding up documentation searches, while architectural decisions still required human judgment.
A paper published in JAMA this month, authored by bioethicist Ezekiel Emanuel and venture capitalist Vinod Khosla, argues that AI is already surpassing physicians at core clinical tasks like gathering patient information, diagnosing conditions, and managing chronic disease, and predicts fully AI-run care will soon beat human-only or hybrid care. American Medical Association CEO John Whyte pushed back, telling Wired that many cited studies relied on simulations rather than blinded trials and that contradictory evidence, including a Nature study on patients struggling to use chatbots, undermines the paper's conclusions.
AI companies have started purchasing used physical books in bulk to feed data into their large language models. This practice follows a copyright settlement earlier this year in which courts determined that buying books and destroying them during the digitization process for AI training qualifies as fair use.
An Entrepreneur opinion piece argues that generative AI tools have become the primary way customers, investors, employees and partners form first impressions of companies, synthesizing news, reviews, social media and other online signals into a single confident narrative. The author warns that old controversies can linger in AI-generated summaries long after public attention fades, quietly shaping perception without a company's input.
The Trump administration filed a 20-page brief supporting OpenAI in its copyright dispute with The New York Times, arguing that training large language models on copyrighted material qualifies as fair use. The filing cites an executive order calling for the US to maintain global leadership in artificial intelligence, and warns that restricting AI training practices could harm American innovation and economic competitiveness.
A professional has published a personal AI usage policy stating they do not currently use generative AI or large language models in their code, design, or correspondence. The policy, first drafted in mid-2025 and updated in May 2026, says they will avoid AI wherever practical but will still collaborate with clients and colleagues who use it, provided they are not held accountable for AI-generated portions of joint work.
The Trump administration filed a statement of interest in The New York Times' copyright lawsuit against OpenAI and Microsoft, siding with OpenAI's claim that training AI models on copyrighted text qualifies as fair use. Government attorneys warned that narrowing fair-use doctrine could hinder scientific and economic progress tied to large language models.
An entrepreneur who returned as CEO of his company in early 2024 describes reorganizing its technology strategy around profitable growth just as large language models surged in prominence. Rather than chasing AI investment without restraint, as he observed many companies doing, he prioritized cost discipline, setting a goal of $100 million in free cash flow within three years while using AI to address internal data problems that mirror those of the company's merchant customers.
A Glassdoor analysis of employee reviews found mentions of AI and LLMs jumped 240 percent between May 2025 and May 2026, while positive sentiment about the technology fell from 81 percent in 2019 to just 43 percent this year. Insurance claims adjustors were the most critical group, with 98 percent of their AI-related comments negative, followed by writers, editors, journalists and accountants.
USC PhD student Zhivar Sourati and colleagues published a March paper arguing that widespread reliance on large language models is homogenizing writing, creativity, and even cultural values, drawing a parallel to 'McDonaldization' of society. Unlike past information technologies, they argue, generative AI actively reshapes content rather than simply distributing it, flattening stylistic diversity across users. Duke computer scientist Emily Wenger adds that this convergence could pose an existential risk by encouraging groupthink and eroding independent judgment.