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Russia has attempted to fully block WhatsApp in the country, the company said, the latest move in an ongoing government effort to tighten control over the internet.A WhatsApp spokesperson said late Wednesday that the Russian authorities’ action was intended to “drive users to a state-owned surveillance app,” a reference to Russia’s own state-supported MAX messaging app that’s seen by critics as a surveillance tool.“Trying to isolate over 100 million people from private and secure communication is a backwards step and can only lead to less safety for people in Russia,” the WhatsApp spokesperson said. “We continue to do everything we can to keep people connected.”Russia’s government has already blocked major social media like Twitter, Facebook, and Instagram, and ramped up other online restrictions since Russia’s full-scale invasion of Ukraine in 2022.Kremlin spokesman Dmitry Peskov said WhatsApp owner Meta Platforms should comply with Russian law to see it unblocked, according to the state Tass news agency.Earlier this week, Russian communications watchdog Roskomnadzor said it will introduce new restrictions on the Telegram messaging app after accusing it of refusing to abide by the law. The move triggered widespread criticism from military bloggers, who warned that Telegram was widely used by Russian troops fighting in Ukraine and its throttling would derail military communications.Despite the announcement, Telegram has largely been working normally. Some experts say it’s a more difficult target, compared with WhatsApp. Some Russian experts said that blocking WhatsApp would free up technological resources and allow authorities to fully focus on Telegram, their priority target.Authorities had previously restricted access to WhatsApp before moving to finally ban it Wednesday.Under President Vladimir Putin, authorities have engaged in deliberate and multipronged efforts to rein in the internet. They have adopted restrictive laws and banned websites and platforms that don’t comply, and focused on improving technology to monitor and manipulate online traffic.Russian authorities have throttled YouTube and methodically ramped up restrictions against popular messaging platforms, blocking Signal and Viber and banning online calls on WhatsApp and Telegram. In December, they imposed restrictions on Apple’s video calling service FaceTime.While it’s still possible to circumvent some of the restrictions by using virtual private network services, many of them are routinely blocked, too.At the same time, authorities actively promoted the “national” messaging app called MAX, which critics say could be used for surveillance. The platform, touted by developers and officials as a one-stop shop for messaging, online government services, making payments and more, openly declares it will share user data with authorities upon request. Experts also say it doesn’t use end-to-end encryption. Associated Press
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Daniel Kokotajlo predicted the end of the world would happen in April 2027. In AI 2027 a document outlining the impending impacts of AI, published in April 2025 the former OpenAI employee and several peers announced that by April 2027, unchecked AI development would lead to superintelligence and consequently destroy humanity. The authors, however are going back on their predictions. Now, Kokotajlo forecasts superintelligence will land in 2034, but he doesnt know if and when AI will destroy humanity. In AI 2027, Kokotajlo argued that superintelligence will emerge through fully autonomous coding, enabling AI systems to drive their own development. The release of ChatGPT in 2022 accelerated predictions around artificial general intelligence, with some forecasting its arrival within years rather than decades. These predictions accrued widespread attention. Notably, JD Vance, U.S. vice president, reportedly read AI 2027 and later urged Pope Leo XIV who underscored AI as a main challenge facing humanity to provide international leadership to avoid outcomes listed in the document. On the other hand, people like Gary Marcus, emeritus professor of neuroscience at New York University, disregarded AI 2027 as a work of fiction, even calling various predictions pure science fiction mumbo jumbo. As researchers and the public alike begin to reckon with how jagged AI performance is, AGI timelines are starting to stretch again, according to Malcolm Murray, an AI risk management expert and one of the authors of the International AI Safety Report. For a scenario like AI 2027 to happen, [AI] would need a lot of more practical skills that are useful in real-world complexities, Murray said. Still, developing AI models that can train themselves remains a steady goal for leading AI companies. Sam Altman, OpenAI CEO, set internal goals for a true automated AI researcher by March of 2028. However, hes not entirely confident in the companys capabilities to develop superintelligence. We may totally fail at this goal, he admitted on X, but given the extraordinary potential impacts we think it is in the public interest to be transparent about this. And so, superintelligence may still be possible, but when it arrives and what it will be capable of remains far murkier than AI 2027 once suggested. Leila Sheridan This article originally appeared on Fast Company‘s sister publication, Inc. Inc. is the voice of the American entrepreneur. We inspire, inform, and document the most fascinating people in business: the risk-takers, the innovators, and the ultra-driven go-getters that represent the most dynamic force in the American economy.
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For most of modern finance, one number has quietly dictated who gets ahead and who gets left out: the credit score. It was a breakthrough when it arrived in the 1950s, becoming an elegant shortcut for a complex decision. But shortcuts age. And in a world driven by data, digital behavior, and real-time signals, the score is increasingly misaligned with how people actually live and manage money. Were now at a turning point. A foundational system, long considered untouchable, is finally being reconstructed by using AIspecifically, advanced machine learning models built for risk predictionto extract more intelligence from existing data. These are rigorously tested, well-governed systems that help lenders see risk with greater nuance and clarity. And the results are reshaping core economics for lenders. THE CREDIT SCORE WASNT BUILT FOR MODERN CONSUMERS Legacy credit scores rely on a narrow slice of information updated at a pace that reflects the black-and-white television era. A single late payment can overshadow years of financial discipline. Data updates lag behind real behavior. And lenders are forced to make million-dollar decisions using a tool that cant see volatility, nuance, or context. A single, generic credit score is a compromise by design. National credit scores are designed to work reasonably well across thousands of institutions, but not optimally for any specific one. That becomes clear when you compare regional differences. A lender in an agricultural region may see very different income seasonality and cash-flow patterns than a lender in a major metro areadifferences that a universal score was never designed to capture. Financial institutions need models built around their actual membership that can adjust to different financial histories and behaviors. That rigidity has created the gap were now seeing across the economy. Consumers feel squeezed, lenders feel exposed, and businesses struggle to grow in a risk environment that looks nothing like the one their scoring tools were built for. Modern machine-learning models give lenders something the score never coulda panoramic view instead of a narrow window. HOW AI CHANGES THE GAME The data in credit files has long been there. Whats changed is the modelingmodern machine learning systems that can finally make full use of those signals. These models can evaluate thousands of factors inside bureau files, not just the static inputs, but the patterns behind them: How payment behavior changes over time Which fluctuations are warning signs versus temporary noise How multiple variables interact in ways a traditional score cant measure This lets lenders differentiate between someone who is truly risky and someone who is momentarily out of rhythm. The impact is profound: more approvals without more losses, stronger compliance without more overhead, and decisions that align with how people actually manage their finances today. For leadership teams, this also means making intentional choices about who to serve and how to allocate capital. Tailored models let institutions focus their resources on the customers they actually want to reach, rather than relying on a one-size-fits-all score. AI FIXES SOMETHING WE DONT TALK ABOUT ENOUGH There’s widespread concern about AI bias, and rightly so. When algorithms aren’t trained on a representative set of data or arent monitored after deployment, this can create biased results. In lending, these models arent deployed on faith; theyre validated, back-tested, and monitored over time, with clear documentation of the factors driving each decision. Modern explainability techniques, now well-established in credit risk, can give regulators and consumers a clearer view into how and why decisions are made. Business leaders should also consider that there is bias embedded in manual underwriting. Human decisionsespecially in high-volume, time-pressured environmentsvary from reviewer to reviewer, case to case, hour to hour. Machine learning models that use representative data, are regularly monitored, and make explainable, transparent decisions, giving humans a dependable baseline. This allows them to focus on exceptions, tough cases, and strategy. THE NEW ADVANTAGE FOR BUSINESS LEADERS The next era of lending will be defined by companies that operationalize AI with discipline, building in strong governance, clear guardrails, and transparency. Those who do will see higher approval rates, lower losses, faster decisions with fewer manual bottlenecks, and fairer outcomes that reflect real behavior, not outdated shortcuts. For the first time in 70 years, were able to bring real, impactful change to one of the most influential drivers in the economy. THE FUTURE ISNT A SCORE, ITS UNDERSTANDING If the last century of lending was defined by a single, blunt number, the next century will be defined by intelligence. By the ability to interpret risk with nuance, adapt to fast-moving economic signals, and extend opportunity to people who have long been underestimated by the system. AI wont make lending flawless. But it gives us the clearest path weve ever had toward a credit ecosystem that is more accurate, more resilient, and far fairer than the one we inherited. And for leaders focused on growth, innovation, and long-term competitiveness, that shift is transformational. Sean Kamkar is CTO of Zest AI.
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E-Commerce
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