Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now OpenAI adds to an increasingly competitive AI voice market for enterprises with its new model, gpt-realtime, that follows complex instructions and with voices “that sound more natural and expressive.” As voice AI continues to grow, and customers find use cases such as customer service calls or real-time translation, the market for realistic-sounding AI voices that also offer enterprise-grade security is heating up. OpenAI claims its new model provides a more human-like voice, but it still needs to compete against companies like ElevenLabs. The model will be available on the Realtime API, which the company also made generally available. Along with the gpt-realtime model, OpenAI also released new voices on the API, which it calls Cedar and Marin, and updated its other voices to work with the latest model. OpenAI said in a livestream that it worked with its customers who are building voice applications to train gpt-realtime and “carefully aligned the model to evals that are built on real-world scenarios like customer support and academic tutoring.” AI Scaling Hits Its Limits Power caps, rising token costs, and inference delays are reshaping enterprise AI. Join our exclusive salon to discover how top teams are: Secure your spot to stay ahead: https://bit.ly/4mwGngO The company touted the model’s ability to create emotive, natural-sounding voices that also align with how developers build with the technology.  Speech-to-speech models The model operates within a speech-to-speech framework, enabling it to understand spoken prompts and respond vocally. Speech-to-speech models are ideally suited for real-time responses, where a person, typically a customer, interacts with an application.  For example, a customer wants to return some products and calls a customer service platform. They could

Nous Research, a secretive artificial intelligence startup that has emerged as a leading voice in the open-source AI movement, quietly released Hermes 4 on Monday, a family of large language models that the company claims can match the performance of leading proprietary systems while offering unprecedented user control and minimal content restrictions.The release represents a significant escalation in the battle between open-source AI advocates and major technology companies over who should control access to advanced artificial intelligence capabilities. Unlike models from OpenAI, Google, or Anthropic, Hermes 4 is designed to respond to nearly any request without the safety guardrails that have become standard in commercial AI systems.“Hermes 4 builds on our legacy of user-aligned models with expanded test-time compute capabilities,” Nous Research announced on X (formerly Twitter). “Special attention was given to making the models creative and interesting to interact with, unencumbered by censorship, and neutrally aligned while maintaining state of the art level math, coding, and reasoning performance for open weight models.”Hermes 4 introduces what Nous Research calls “hybrid reasoning,” allowing users to toggle between fast responses and deeper, step-by-step thinking processes. When activated, the models generate their internal reasoning within special tags before providing a final answer — similar to OpenAI’s o1 reasoning models but with full transparency into the AI’s thought process.

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Nvidia reported $46.7 billion in revenue for fiscal Q2 2026 in their earnings announcement and call yesterday, with data center revenue hitting $41.1 billion, up 56% year over year. The company also released guidance for Q3, predicting a $54 billion quarter. Behind these confirmed earnings call numbers lies a more complex story of how custom application-specific integrated circuits (ASICs) are gaining ground in key Nvidia segments and will challenge their growth in the quarters to come. Bank of America’s Vivek Arya asked Nvidia’s president and CEO, Jensen Huang, if he saw any scenario where ASICs could take market share from Nvidia GPUs. ASICs continue to gain ground on performance and cost advantages over Nvidia, Broadcom projects 55% to 60% AI revenue growth next year. Huang pushed back hard on the earnings call. He emphasized that building AI infrastructure is “really hard” and most ASIC projects fail to reach production. That’s a fair point, but they have a competitor in Broadcom, which is seeing its AI revenue steadily ramp up, approaching a $20 billion annual run rate. Further underscoring the growing competitive fragmentation of the market is how Google, Meta and Microsoft all deploy custom silicon at scale. The market has spoken. AI Scaling Hits Its Limits Power caps, rising token costs, and inference delays are reshaping enterprise AI. Join our exclusive salon to discover how top teams are: Secure your spot to stay ahead: https://bit.ly/4mwGngO ASICs are redefining the competitive landscape in real-time Nvidia is more than capable of competing with new ASIC providers. Where they’re running into headwinds is how effectively ASIC competitors are positioning the combination of their use cases, performance claims

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now A new training framework developed by researchers at Tencent AI Lab and Washington University in St. Louis enables large language models (LLMs) to improve themselves without requiring any human-labeled data. The technique, called R-Zero, uses reinforcement learning to generate its own training data from scratch, addressing one of the main bottlenecks in creating self-evolving AI systems. R-Zero works by having two independent models co-evolve by interacting with and challenging each other. Experiments show that R-Zero substantially improves reasoning capabilities across different LLMs, which could lower the complexity and costs of training advanced AI. For enterprises, this approach could accelerate the development of specialized models for complex reasoning tasks without the massive expense of curating labeled datasets. The idea behind self-evolving LLMs is to create AI systems that can autonomously generate, refine, and learn from their own experiences. This offers a scalable path toward more intelligent and capable AI. However, a major challenge is that training these models requires large volumes of high-quality tasks and labels, which act as supervision signals for the AI to learn from. Relying on human annotators to create this data is not only costly and slow but also creates a fundamental bottleneck. It effectively limits an AI’s potential capabilities to what humans can teach it. To address this, researchers have developed label-free methods that derive reward signals directly from a model’s own outputs, for example, by measuring its confidence in an answer. While these methods eliminate the need for explicit labels, they still rely on a pre-existing set of tasks, thereby limiting their applicability in truly self-evolving scenarios. AI Scaling Hits Its Limits Power caps, rising token costs, and inference delays are reshaping enterprise AI.

Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now OpenAI and Anthropic may often pit their foundation models against each other, but the two companies came together to evaluate each other’s public models to test alignment.  The companies said they believed that cross-evaluating accountability and safety would provide more transparency into what these powerful models could do, enabling enterprises to choose models that work best for them. “We believe this approach supports accountable and transparent evaluation, helping to ensure that each lab’s models continue to be tested against new and challenging scenarios,” OpenAI said in its findings.  Both companies found that reasoning models, such as OpenAI’s 03 and o4-mini and Claude 4 from Anthropic, resist jailbreaks, while general chat models like GPT-4.1 were susceptible to misuse. Evaluations like this can help enterprises identify the potential risks associated with these models, although it should be noted that GPT-5 is not part of the test.  AI Scaling Hits Its Limits Power caps, rising token costs, and inference delays are reshaping enterprise AI. Join our exclusive salon to discover how top teams are: Secure your spot to stay ahead: https://bit.ly/4mwGngO These safety and transparency alignment evaluations follow claims by users, primarily of ChatGPT, that OpenAI’s models have fallen prey to sycophancy and become overly deferential. OpenAI has since rolled back updates that caused sycophancy.  “We are primarily interested in understanding model propensities for harmful action,” Anthropic said in its report. “We aim to understand the most concerning actions that these models might try to take when given the opportunity, rather than focusing on the real-world likelihood of such opportunities arising or the probability that these actions would be successfully completed.” OpenAI noted the tests were designed

This is today’s edition of The Download, our weekday newsletter that provides a daily dose of what’s going on in the world of technology. Google’s still not giving us the full picture on AI energy use  —Casey Crownhart Google just announced that a typical query to its Gemini app uses about 0.24 watt-hours of electricity. That’s about the same as running a microwave for one second—something that feels insignificant. I run the microwave for many more seconds than that most days.I welcome more openness from major AI players about their estimated energy use per query. But I’ve noticed that some folks are taking this number and using it to conclude that we don’t need to worry about AI’s energy demand. That’s not the right takeaway here. Let’s dig into why.
This article is from The Spark, MIT Technology Review’s weekly climate newsletter. To receive it in your inbox every Wednesday, sign up here. + If you’re interested in AI’s energy footprint, earlier this year, MIT Technology Review published Power Hungry: a comprehensive series on AI and energy.
The AI Hype Index: AI-designed antibiotics show promise Separating AI reality from hyped-up fiction isn’t always easy. That’s why we’ve created the AI Hype Index—a simple, at-a-glance summary of everything you need to know about the state of the industry. Take a look at this month’s edition here. The must-reads I’ve combed the internet to find you today’s most fun/important/scary/fascinating stories about technology. 1 The White House has fired the director of the CDCBut Susan Monarez is refusing to go quietly. (WP $)+ Monarez is said to have clashed with RFK Jr over vaccine policy. (NYT $)+ She was confirmed by the Senate to the position just last month. (The Guardian)+ Vaccine consensus is splintering across the US. (Vox) 2 A Chinese hacking campaign hit at least 200 US organizationsIntelligence agencies say the breaches are among the most significant ever. (WP $)+ AI-generated ransomware is on the rise. (Wired $)

3 Ukraine’s new Flamingo cruise missile took just months to buildRussia’s air defenses are weakening. Can this missile exploit the gaps? (Economist $)+ 14 people were killed in an overnight bombardment of Kyiv. (BBC)+ On the ground in Ukraine’s largest Starlink repair shop. (MIT Technology Review) 4 AI infrastructure spending is boosting the US economyCompanies are throwing so much money at AI hardware it’s lifting the real economy, not just the stock market. (NYT $)+ How to fine-tune AI for prosperity. (MIT Technology Review)5 OpenAI and Anthropic safety-tested each other’s AIThey found Claude is a lot more cautious than OpenAI’s mini models. (Engadget)+ Sycophancy was a repeated issue among OpenAI’s models. (TechCrunch)+ This benchmark used Reddit’s AITA to test how much AI models suck up to us. (MIT Technology Review) 6 Climate change exacerbated Europe’s deadly wildfiresAnd fires across the Mediterranean are likely to become more frequent and severe. (BBC)+ What the collapse of a glacier can teach us. (New Yorker $)+ How AI can help spot wildfires. (MIT Technology Review) 7 911 centers are using AI to answer callsIt’s helping to triage anything that isn’t urgent. (TechCrunch) 8 Wikipedia has compiled a list of AI writing tropesBut their presence still isn’t a dead giveaway a text has been written by AI. (Fast Company $)+ AI-text detection tools are really easy to fool. (MIT Technology Review) 9 Melania Trump has launched the Presidential AI Challenge But it’s not all that clear what the competition actually is. (NY Mag $) 10 Netflix’s algorithm-appeasing movies are bland and boringBut millions of people will watch them anyway. (The Guardian)
Quote of the day “The more you buy, the more you grow.”
—Nvidia CEO Jensen Huang conveniently sees no end to the AI chip spending boom, Reuters reports.