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The Rise of AI Financial MarketsThe most important market of the future could be in LLM tokens — and financial groups are rushing to build new infrastructure for them. China's Shanghai Futures Exchange is currently designing a derivatives market for AI tokens, while major derivatives exchanges CME Group and the Intercontinental Exchange (the owner of the NYSE) have separately announced they're working on launching futures contracts for renting GPUs.Building the AI Derivatives InfrastructureGPU markets are still maturing, but given the wide range of companies using, selling, and renting GPUs, there's already a robust market for spot prices on GPU rental, typically charged by the hour. This has prompted major financial players to develop futures contracts that would allow businesses to hedge against fluctuating compute costs.Enterprise plans for major AI companies are commonly denominated in tokens: OpenAI, for example, charges $5 per million input tokens, and $30 per million output tokens if you want to use the API for its latest GPT-5.5 model. Even cloud providers are increasingly offering the opportunity to charge per token, as in Amazon's Bedrock system.The Economics of GPU and Token PricingAccording to data from AI Mining Co., which tracks daily GPU rental pricing across 28 marketplaces and cloud providers, median prices for Nvidia H100 GPUs ranged from $1.40 to $4.27 per hour across 13 marketplaces, while the average price for H200 GPUs were between $2.34 and $5 per hour across 10 marketplaces.Just over the past seven days, average H100 prices ranged from $2.79 to $3.33, showing the volatility that makes futures contracts attractive for risk management.Transforming the AI Investment LandscapeThe effort comes amid an unprecedented buildout of AI infrastructure. Cloud service providers, private equity firms, and infrastructure players alike have poured hundreds of billions into building data centers, anticipating that demand for GPUs and compute will continue to rise.An emerging crop of global neocloud companies is also vying for a piece of this demand. Some of these new entrants are specializing, focusing on inference, while others are competing with cloud giants like Oracle, AWS, and Google Cloud to offer their services to AI companies.The Future of AI Financial InstrumentsBy targeting AI tokens, the Shanghai exchange's derivative product would be tied to how AI companies price their services, giving businesses, investors, and data center operators a way to hedge against the cost of compute. As AI becomes increasingly central to business operations, these financial instruments will likely become essential components of the technology investment ecosystem.

The Road to Wembley Wigan Warriors are gearing up for a big presence at Wembley as they compete in the Challenge Cup finals on Saturday. The club has selected two players, Liam Farrell and Jenna Foubister, to represent them at a Wembley photoshoot, showcasing the team's strong squad and dedication to the sport. Wigan's Successful History Wigan have a remarkable record in finals, rarely losing two on the bounce. With 21 Challenge Cups under their belt, the team is driven by a strong mentality and a rich history of success. According to Farrell, 'There's a bit of outside pressure, an expectation from fans in the town to win. There's also a strong mentality at the club of Wigan lads, who have grown up watching us win finals, wanting to reproduce that themselves.' The Next Generation The club's academy graduates account for 20% of all Super League players, demonstrating Wigan's commitment to developing young talent. Foubister, a 19-year-old player, has already made her England debut and won the quadruple, highlighting the club's ability to nurture and support its players. 'The club put a lot into the youth system,' she says. 'They are very supportive in the way they shape players for the future.' A New Era for Women's Rugby Wigan have taken a proactive approach to providing for their female players, with the women's team having sole use of the revamped Edge Hall Road site. This move has helped to increase the team's fanbase and provide a platform for players like Foubister to develop their skills. 'It is a privilege having our own ground,' she says. 'We can go up whenever we want and have the freedom to train, whereas at Robin Park you got slotted in at a certain time.' Building a Legacy As Farrell's career enters its final phase, Foubister aims to become a hero in her hometown and build a legacy for the women's team. 'The men have a lot of history behind them, but the women's team has to build a history of our own now,' she says. With the support of the town and the club, Foubister and her teammates are well on their way to achieving their goals.

The Shocking Exit at Roland GarrosJannik Sinner's bid for a maiden French Open title and career Grand Slam went up in smoke as he experienced physical issues in his second-round match against Juan Manuel Cerundolo and fell to a 3-6 2-6 7-5 6-1 6-1 defeat. The 24-year-old Italian arrived in Paris as the clear favorite for the title, having lifted claycourt titles in Monte Carlo, Madrid and Rome, with his main rival and defending champion Carlos Alcaraz ruled out with injury and Novak Djokovic searching for his best form.The Dramatic Turn of EventsBut Cerundolo tore up the script in a dramatic clash on a scorching Thursday where he held his nerve even as last year's runner-up Sinner crumbled while on the verge of a big win, sending shockwaves through Roland Garros. As the temperature climbed over the 30 degrees Celsius (86F) mark for the first time in the afternoon, Sinner had already breezed through the first set on the back of a solitary break, and looked to be in cruise mode.The Physical Toll of the MatchCerundolo offered resistance towards the end of the second set, but the 56th-ranked Argentinian was left with a mountain to climb after Sinner unleashed a huge forehand winner to double his lead in the match for the loss of only five games. The four-time Grand Slam champion cooled off with an ice towel in the break and turned up the intensity on his unseeded opponent in the third set to go 5-1 ahead, before he began to struggle and halted play when serving at 5-4.The Comeback VictorySinner returned from an off-court medical timeout five minutes later and was immediately broken for 5-5, and dropped the next two games to hand the set to his opponent, who sensed the chance to pull off a major upset. Still not at his best, Sinner surrendered the fourth set tamely and was broken early in the decider, as Cerundolo took full advantage to leave the Grand Slam without its title favorite.The Tournament AftermathSinner's unexpected exit creates a wide-open draw at Roland Garros, with the top half of the men's bracket now lacking a clear favorite. The Italian's physical concerns may also raise questions about his preparation for the upcoming grass court season, including Wimbledon. For Cerundolo, the victory represents the biggest win of his career and establishes him as a dangerous floater in the tournament, with the confidence to challenge remaining players.

The Rise of Recursive Self-Improvement in AIThe word "recursion" is the latest buzzword in AI circles. Two separate startups have taken on the name, and many more have started referencing recursive self-improvement (RSI) in their roadmaps. Like AGI before it, RSI has become a three-letter byword for a cataclysmic AI takeoff – even if there's still a little disagreement about what it exactly means.In basic terms, RSI refers to an AI system that can continuously upgrade itself. Once AI systems can manage the upgrade cycle better than humans, the process can become a closed loop, limited only by the compute power they can access, and humans are no longer necessary or even helpful.Scary or not, that's a vision that a lot of AI labs are eager to chase.Key Players Pursuing Recursive SystemsEarlier this month, well-known AI researcher Richard Socher launched the aptly named Recursive Superintelligence with RSI as an explicit goal. "Our main focus is to build truly recursive, self-improving superintelligence at scale," Socher told TechCrunch at launch, "which means that the entire process of ideation, implementation, and validation of research ideas would be automatic."A number of other prominent researchers are already chasing that same goal, hoping for a breakthrough that will make recursive self-improvement possible.One of the most prominent is Andrej Karpathy, a legendary figure from Tesla and OpenAI, who is using agent swarms to train LLMs on simple tasks for a project he calls Auto-Research. Karpathy has been unusually open about the project, tweeting about milestones regularly and making the building blocks available through a public GitHub repo. So far, the work has mostly been confined to making minor improvements on a GPT-2 scale model — as Karpathy noted in March, "It's not novel, ground-breaking 'research' (yet)" — but it's been enough to convince lots of other researchers to follow the RSI dream. And with Karpathy now working on pre-training at Anthropic, he will have plenty of opportunity to apply the idea at a larger scale.Adaption — founded by Cohere and Google alum Sara Hooker — recently launched a similar tool called AutoScientist in an effort to automate frontier training. Like Karpathy's auto-researchers, the system trains agents to make incremental improvements — but for Adaption, the goal is to make it easier to train a full-scale frontier model. If those same researchers start to push the frontier forward, the system could quickly spiral into something very much like RSI.Disarray founder Doris Xin drew more specific RSI interest when her self-trained machine learning agent took home 28 medals in a recent Kaggle competition, beating out many human-trained agents. As she sees it, the major challenge is reliability."I would argue, given infinite compute and infinite time horizon, we are already there," Xin told me. "I want to make an argument that this is not a creative endeavor, really. It's just a lot of meat-and-potatoes engineering."The Current State of Self-Improving AIThere's also plenty of evidence that the AI industry isn't very close to recursive systems in any meaningful way — and is still grappling with talking to a wary public about its progress. So Google CEO Sundar Pichai basically admitted in a recent podcast interview."It's a continuum, and we are all definitely making progress," Pichai said. "But in the way people describe RSI, that would represent a next level of acceleration and would have a lot of implications, but we aren't quite there yet."But the continuum includes an awful lot of self-improving AI systems.In January, one of Anthropic's lead programmers for Claude Code estimated that "close to 100%" of his team's code was written by the tool — a frank admission that Claude Code was literally writing itself.Just because engineers are using an AI tool doesn't mean the tool can replace them — but Anthropic seems to be getting close to replacing engineers too. In a recent survey tied to the Mythos preview, five out of 18 Anthropic engineers believed that, with harness improvements, this version of Mythos could soon substitute for an L4 engineer — a midlevel programmer who can take on involved projects without supervision.Still, there were some of the same weaknesses you might expect."Some of Claude's major reported weaknesses compared to an L4 include: self-managing week-long ambiguous tasks, understanding org priorities, taste, verification, instruction-following, and epistemics," the report reads.In other words, its weaknesses are everything involved with self-direction, which is the cornerstone for RSI. But sure, for everything else, Claude is ready to step right in.Expert Perspectives on RSI TimelinesJust like the AGI term before it, the AI industry also can't tell us how far away it is from showcasing a meaningful recursive system. When Georgetown's Center for Security and Emerging Technology assembled a group of experts to study RSI last year, the group found a major split in assessments — some expecting an imminent "superintelligence" style explosion while others expected slower progress and an eventual plateau. But all agreed that recursion made the future especially difficult to predict.Helen Toner, director of CSET and a former board member at OpenAI, told TechCrunch that simply using AI tools to do AI research isn't enough to qualify as RSI. "They're just using AI for as much as they can," Toner told TechCrunch. "And I think that is different from the classic definition of RSI, which is really that there are no humans needed."Toner pointed to a recent post by METR's Ajeya Cotra, which distinguishes different milestones on the path to the AI research takeover. One step, which Cotra calls "adequacy," would come when the system can still perform research after all humans are removed — even if the resulting research isn't as valuable or efficient. "Parity" comes when an AI-only system is as good at research as a human-only system. "Supremacy," the final stage, comes when an AI-only system outperforms a collaborative system between humans and AI.Ultimately, Cotra concludes that AI is very close to the adequacy threshold of being able to produce some work on its own — similar to the incremental changes made by Karpathy's Auto-Research system. "I wouldn't be totally shocked if you told me this milestone had already passed, and I expect it to happen in the next couple years," Cotra wrote.She was less clear on when parity will come, but once it does, she thinks it would "massively accelerate the pace of AI progress, leading to AI research supremacy within another year."The Challenges Ahead for Recursive AIWith so much of AI built on scaling laws, there's a strong tendency to think RSI will follow the same curve. Toner thinks that many of those pursuing AI research and development via RSI "think of it as a pretty smooth ladder, where you can just keep scaling up."But even if AI researchers are able to make incremental improvements like Karpathy's auto-researchers, there will be larger challenges in handing off the whole process of research. Toner put it in terms of the history of computing, which has seen human beings handing off more and more of the process while still directing things from the top."We went from machine languages to assembly language and compiled languages; you're getting further and further from the guts of the computer," Toner said. "But the human is still, in some intuitive sense, running the show."Moving beyond that paradigm will take significant challenges, both in engineering and alignment. But even with the massive investments happening, there's no infinite compute available — and the basic trade-off between human labor and machine intelligence will be hard to overcome.The Future of Recursive Self-ImprovementAs for a total recursive AI system of apocalyptic visions? The only thing researchers essentially agree on is that, like AGI, it's not here yet.

The Lead Enterprise organizations are not rejecting AI. They are rejecting operational instability. This is the shift many founders still misunderstand — and it is becoming one of the defining realities separating enterprise AI companies that scale from the ones that stall after early momentum. The Event Details At TechCrunch Disrupt 2026, taking place October 13–15 at Moscone West in San Francisco, Arsalan Tavakoli-Shiraji, co-founder and SVP of field engineering at Databricks, will unpack that shift during his AI Stage session, “The Enterprise Isn’t Broken. Your Assumptions About It Are.” The Data Analysis The enterprise AI market is full of successful pilots that never became real deployments. Not because the technology failed. But because the organization could not absorb the operational consequences of adopting it. The Impact Analysis Now the reality founders need to face is that startup AI deals rarely die because the model underperformed. They die because the enterprise lost confidence in what the deployment would require. The AI startups gaining traction inside large organizations increasingly share one thing in common: They reduce uncertainty. The Prediction The startups that succeed in enterprise AI over the next several years may not necessarily be the ones with the most advanced models. They may be the ones that best understand how enterprises actually absorb change. That is the kind of operational pressure that Tavakoli-Shiraji and other speakers on the AI Stage at Disrupt will explore.

Anti‑immigrant anger is reaching a new peak in South Africa as municipal officials ordered the removal of makeshift camps that housed thousands of migrants, leaving them exposed on public streets. The move has ignited protests, a surge in xenophobic incidents, and a heated debate over the nation’s immigration policy. Escalating Xenophobic Tensions After Forced Evictions City councils in Johannesburg and surrounding townships issued eviction notices this week, citing health and safety concerns. Residents of the cleared camps report being given less than 24 hours to vacate, with many forced to sleep on sidewalks or in overcrowded shelters. Evictions began on 2026-05-25 across three major informal settlements. Local NGOs estimate that over 5,000 migrants were displaced. Community leaders claim the actions were taken without adequate consultation. Limited Data Highlights a Growing Crisis Official statistics on the displacement are scarce, but available reports point to a sharp rise in xenophobic activity: The South African Police Service logged a noticeable uptick in hate‑crime complaints in the past month. Human‑rights groups note an increase in verbal and physical attacks targeting foreign nationals. Economic analysts warn that prolonged unrest could deter foreign investment. Political Fallout and Social Cohesion at Risk The government’s response has split opinion. While some politicians defend the evictions as necessary for public order, opposition parties and civil‑society groups accuse the administration of stoking xenophobia. President Cyril Ramaphosa called for “orderly migration management” but avoided direct criticism of local authorities. Opposition leader John Steenhuisen demanded an immediate halt to evictions and a review of immigration policy. International bodies, including the UN, have urged South Africa to uphold the rights of migrants. Potential Policy Shifts and International Scrutiny Analysts predict that sustained pressure could force the government to adopt a more coordinated approach: Implementation of a national framework for temporary housing of displaced migrants. Increased funding for community‑integration programs to mitigate xenophobic sentiment. Possible sanctions or aid reductions from foreign partners if human‑rights violations continue. Until concrete measures are taken, the risk of further unrest remains high, and South Africa’s reputation as a regional hub for trade and tourism could suffer.

The Lead Crystal Palace's turbulent season has ended in triumph as they won the Conference League, securing a spot in the Europa League. The team's journey was marked by challenges, including tensions between manager Oliver Glasner and owner Steve Parish. The Event Details Crystal Palace's victory in the Conference League final against Rayo Vallecano was sealed with a goal from Jean-Philippe Mateta. The win marked the team's first major trophy under Glasner's management. Parish reflected on the journey, saying, 'It's incredible... an amazing achievement. All the ups and downs … To get to the Europa League, where we deserve to be.' The team's captain, Dean Henderson, was handed the trophy by UEFA President Aleksander Ceferin. The Data Analysis The team's success was fueled by key players, including Maxence Lacroix, Daniel Muñoz, Adam Wharton, and Ismaïla Sarr, who was the Conference League's top scorer. The team's journey to the Europa League was a significant achievement, especially considering their tumultuous season. The Impact Analysis The victory marked a new era for Crystal Palace, as Glasner departed and a new manager, Andoni Iraola, is expected to be appointed. Parish acknowledged the challenge of keeping the team together, as several players are expected to be coveted by bigger clubs. The team's success has raised expectations, and Parish emphasized the need to 'stay there' and continue to compete at a high level. The Prediction As Crystal Palace looks to the future, they will face a new set of challenges under their new manager. With a talented squad and a taste of European competition, the team is poised to make a strong impact in the upcoming season. The appointment of Iraola or another manager will be crucial in determining the team's trajectory and ability to sustain their success.

The Final Days of Early Bird Pricing Time is running out to secure discounted tickets to TechCrunch Disrupt 2026. Early Bird pricing ends tomorrow, May 29, at 11:59 p.m. PT. After that, prices for the highly anticipated tech conference will increase. Unlock Savings of Up to $410 By registering now, you can lock in savings of up to $410 on your pass or up to 30% on group passes of 4+. Why Attend TechCrunch Disrupt 2026? TechCrunch Disrupt 2026, taking place from October 13–15 at San Francisco’s Moscone West, is a premier event for startups, investors, and tech enthusiasts. Here’s what you’ll gain by attending: Founder Pass: Accelerate growth with the right insights, tools, and connections. Meet investors aligned with your startup. Investor Pass: Discover standout startups and expand your portfolio with curated access. Use matchmaking tools to make every conversation count. Don’t Miss Out The window to the lowest ticket rates of the year is closing at 11:59 p.m. PT tomorrow, May 29. Register now to secure your ticket with up to a $410 discount.

General Compute, a new inference neocloud, raised a $15 million seed round at a $60 million post‑money valuation and booked a $300 million order for SambaNova’s upcoming SN50 chips. The company promises 600‑700 tokens per second per chip and a deployment model that fits into existing, air‑cooled data‑center infrastructure. General Compute’s Funding and Strategic Partnerships Seed round led by FUSE VC with participation from Carya Venture Partners and Village Global Ventures. Co‑founders Finn Puklowski (CEO) and Jason Goodison (CTO) partnered with SambaNova, an Intel‑backed chipmaker focused on inference. General Compute will be the first neocloud to deploy SambaNova’s SN50 chips, ordering $300 million worth of hardware. Colocation strategy includes traditional data‑center providers and repurposed crypto‑miner facilities. Financial Snapshot: $15 Million Seed and $300 Million Chip Order Seed funding: $15 million raised, valuing the company at $60 million post‑money. Chip commitment: $300 million of SN50 chips on order, enough to power a large inference fleet. Comparable market moves: Nvidia’s $20 billion acquisition of Groq (Dec 2025) and Cerebras’ $57 billion IPO (May 2026) illustrate the scale of inference‑focused investments. Implications for the AI Inference Landscape The shift from GPU‑centric training to specialized inference hardware is accelerating. SambaNova’s memory‑rich, flexible architecture claims to outperform GPUs, Groq, and Cerebras on token‑throughput, delivering 600‑700 tokens/sec versus ~250 tokens/sec for GPUs. Air‑cooled, low‑power chips lower the barrier to entry for colocation, enabling rapid deployment in existing facilities and even in repurposed crypto‑mining sites. This could democratize high‑speed inference, pressure pricing, and spur a wave of niche cloud providers focused on agent‑to‑agent workloads. What the Next Year May Hold for Inference‑First Cloud Providers When SambaNova releases its next‑gen chips later in 2026, General Compute’s early access positions it to capture a sizable share of the fast‑inference market. Expect: Increased competition among inference‑only clouds (e.g., CoreWeave, OpenRouter) to offer multi‑model routing and token‑cost optimization. More venture capital flowing into inference‑focused startups, mirroring the recent $113 million Series B for OpenRouter. Potential consolidation as larger players (Nvidia, Intel) seek partnerships or acquisitions to secure the most efficient inference stacks. Speed and cost efficiency will become the primary differentiators, shaping the architecture choices that dominate the AI future.