Arena, the crowdsourced AI leaderboard that started as a UC Berkeley research project in 2023, has reached 100 million dollars in annualized revenue just eight months after launching its first commercial product. The platform is best known for letting users compare two anonymous AI model responses side by side and vote on which is better. More than 10 million of those evaluations have now been submitted.

The revenue comes from AI Evaluations, a paid service Arena introduced in September that gives model labs and enterprises detailed performance analytics drawn from its community of users. By December, the service had reached 30 million dollars in annualized revenue. It has more than tripled since then.

There is a caveat in the headline number. While Arena describes the figure as ARR, CEO Anastasios Angelopoulos told TechCrunch that customers pay for consumption, meaning the revenue is not recurring in the traditional SaaS sense. “A lot of people don’t even understand that our business is making any money at all, they still see us as like an open-source project,” he said.

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Arena has no direct competitor left standing. Yupp, the only other crowdsourced AI model-picking startup, shut down in March after raising 33 million dollars from a16z crypto’s Chris Dixon. Angelopoulos said Arena competes “for the same dollar” as human labeling companies like Mercor, Surge, and Scale AI, all of which help model makers refine their AI during post-training.

That market is growing fast. Handshake’s annualized revenue from AI training nearly doubled from 550 million dollars in January to nearly one billion dollars by April, according to The Information. Mercor’s annualized revenue also topped one billion dollars earlier this year, though a supply chain breach has since complicated its relationship with key clients including Meta.

Arena was co-founded by Angelopoulos and Wei-Lin Chiang, both postdoctoral researchers at UC Berkeley, along with Ion Stoica, the UC Berkeley professor and Databricks co-founder who advised the project before it incorporated in April 2025. The company raised 150 million dollars in a Series A round in January at a valuation of nearly two billion dollars, bringing its total funding to 250 million dollars from investors including Felicis, Andreessen Horowitz, Kleiner Perkins, and Lightspeed.

The platform now ranks AI models across text, coding, vision, and image generation, as well as complex agent workflows through a recently introduced Agent Mode. Its leaderboard has become the de facto scorecard for frontier AI models, with labs from OpenAI to Anthropic to Google routinely citing Arena rankings in their own launch announcements. Turning that influence into a 100 million dollar business in under a year suggests that evaluating AI may be nearly as lucrative as building it.

OS PLATFORMS

Polished Mandriva descendant still makes room for PCs the 64-bit world has left behind

Mageia 10 marks 15 years since the distribution’s first release in June 2011. The project began the previous year as a fork of Mandriva, itself formerly known as Mandrake Linux. We last looked at Mageia alongside the other Mandrake descendants in 2022.

What sets Mageia apart from OpenMandriva Lx, PCLinuxOS, and Russia’s ROSA Linux is its continued support for 32-bit x86 PCs. Its GNOME and KDE Plasma live images are available only for x86-64, while the Xfce edition comes in both x86-64 and x86-32 versions.

There is also a “Classic Installer” ISO, which lets you choose your own desktop from nine different desktop environments, plus another 16 window managers, as detailed in the release notes. Both the standard GNOME session and GNOME Classic are available, while Liquidshell provides a lightweight alternative to KDE Plasma.

Mandrake Linux started out in 1998 as an easier version of Red Hat Linux using the new KDE desktop, which, at that time, Red Hat refused to incorporate due to concerns over the licence of KDE’s Qt toolkit. Nearly three decades later, Mageia remains an RPM-based distro. Version 10 offers two RPM package-management tools: Mageia’s urpmi command and DNF. urpmi also has its own graphical wrapper called Rpmdrake, but Fedora’s dnfdragora is an optional install. Since RHEL and the RHELatives, Fedora, SUSE and openSUSE all use RPM as well, packages of big-name apps such as Google Chrome are available – but Mageia is a different distro, whose common ancestry dates back more than 25 years, and packages for Fedora or openSUSE may not install or work correctly. It comes with Flatpak preinstalled, although no Flatpak applications are installed by default. As with other niche distros, Flatpak may help when you can’t find a native package of something. For those with the 32-bit edition, though, we suspect that few Flatpaks support that architecture.

Mageia 10 is a polished, friendly graphical Linux, built from recent components such as kernel 6.18. True, it does feel a little old-fashioned in some ways: for instance, it uses separate root and user accounts – although sudo is installed, it’s not configured for use. However, it’s a solid choice if you want to get away from the Debian/Fedora mainstream – and if you have a capable 32-bit machine, like a Windows 10 32-bit box, or some other need to run a 32-bit OS such as specific hardware support, then this is one of the best choices around today.

The Welcome screen is rich and very helpful, offering the ability to install extra apps, switch repositories, and more. Alongside it is the Mageia Control Center, which can manage most aspects of the OS without going near a command line. The distro is also well documented, with a substantial Mageia wiki.

It does use systemd, but, even so, it’s relatively lightweight. In our testing on a 32-bit VirtualBox VM, the Xfce edition used just 633 MB of RAM at idle, which is low by modern standards, and 7.8 GB of disk space. If you choose the KDE Plasma desktop, you get Plasma 6.5.5 with a choice of X11 or Wayland. The installation occupies about the same amount of disk space, although the RAM usage rises sharply: about 1.7 GB at idle. Xfce has an unusual GNOME 2-style two-panel setup, while the Plasma layout is clean and simple. We installed the Liquidshell desktop to have a look, but it’s very basic and rather clunky. 

Mageia forked from Mandriva in 2011, before the company closed down, while OpenMandriva did so afterwards. They are still quite similar distributions, though, and we really wish that the two teams could settle their differences and merge the distros. Either way, Mageia’s 32-bit edition is an increasingly rare offering in an increasingly 64-bit world, which might win it some new admirers. ®

Waymo robotaxis are no longer available on Uber’s ride-hail app in Phoenix, Arizona, ending a nearly three-year partnership in the city, both companies confirmed to TechCrunch on Monday.

Uber said it is readying the launch of a separate autonomous vehicle partnership in the city, but did not name the partner. Waymo told TechCrunch that the vehicles Uber used for this “pilot” program have already been integrated into its own Phoenix fleet, available through its app. Waymo users started noticing that the company’s vehicles were absent from Uber’s network in recent days. Waymo’s vehicles are still available on Uber in Austin and Atlanta, for instance.

The quiet end to this partnership in Phoenix, which Waymo said happened in May, comes as the Alphabet-owned company is starting to put its newest robotaxis — the Zeekr-made van it calls Ojai — on the road. It’s also happening as the Uber-Waymo relationship appears to be wearing in some places, with the two companies poised to directly compete against each other in London as early as this year.

Still, both companies praised the collaboration in Phoenix as a successful jumping-off point for their respective robotaxi plans, which have gotten increasingly ambitious since 2023.

“This was a productive pilot that paved the way for future expansions and partnerships across the globe. After hundreds of thousands of trips with Uber, we have integrated these vehicles back into our Phoenix fleet, where they will continue to serve riders through Waymo, including our public transit integration with Via, and delivery with DoorDash,” Waymo told TechCrunch. “We’re grateful to all of the Uber customers who took fully autonomous trips with us, and we look forward to continuing to serve the Phoenix community.”

“Phoenix was our first pilot market with Waymo and was an intentionally limited deployment, reaching just over a dozen vehicles dedicated to the program. We learned a lot from that collaboration, which helped us to quickly scale Austin and Atlanta, where hundreds of Waymo AVs are available exclusively on Uber and our coverage area continues to expand,” Uber said.

The robotaxi landscape looks much different than it did when these two companies kicked off this collaboration in 2023. Back when it was first announced, the idea of Uber and Waymo partnering up still seemed unlikely given their messy legal battle that ended in a settlement in 2018. Robotaxis as a technology were in a far more uncertain place, as no operator had reached scale yet. Cruise was still seen as a viable competitor, as it had not yet gone through its own scandal and been absorbed into General Motors.

In the three years since, Waymo has grown its fleet to around 4,000 vehicles, and Uber has inked deals to add dozens of autonomous vehicle partners to its network.

This Phoenix partnership remained an unusual one, as it was the only city where Waymo operated directly and through Uber. Waymo is in the process of launching in around 20 new cities this year, is operating in 11 major U.S. metro areas, and the company offers more than 500,000 trips every week.

On June 28, the International Society for Transforming Education — the organization behind the editorially independent news site EdSurge — released an expanded version of its “Profile of an AI-Ready Graduate,” a framework designed to help K-12 educators teach students how to work with artificial intelligence.

The updated framework, designed with support from the nonprofit Britebound, goes beyond basic literacy to higher-order skills. It identifies six roles the organization says students should fill when using AI tools: Learner, Researcher, Synthesizer, Problem Solver, Connector and Storyteller.

“Today, we are releasing a fully fleshed out version, 30 skills aligned with each of these roles to help model using AI to support our uniquely human skills,” said Richard Culatta, CEO of the organization. “Humans have always used tools to accomplish human tasks. AI is no different, but when we teach AI as a way to support us being better at being human, it is far more relevant and far more meaningful than when we just talk about what AI is.”

The announcement was made at the organization’s annual conference in Orlando, Florida, one year after the initial rollout of the Profile. While the original framework focused on basic technical understanding of AI, the updated version shows what those skills look like in practice — with role-by-role descriptions, classroom examples and articulations for middle and high school. 

The framework is intended to layer on to the work educators are already doing and aligns with the International Society for Transforming Education’s existing student standards and “Transformational Learning Principles.”

The updated Profile of an AI-Ready Graduate is available as a free download here.

(Editor’s note: EdSurge is an editorially independent newsroom of the International Society for Transforming Education.)

It’s time to update your Mac, iPhone, and iPad, as Apple has released a new trio of security patches for its operating systems.

Apple pushed out three new updates on Monday in an effort to patch an apparent security flaw. As of publication, Apple has not specified what issue the patch is meant to fix.

Because Apple has not announced what is in the update, it is also possible that it contains bug fixes as well.

To update, you can follow the steps below.

How to update to 26.5.2 on iPhone and iPad

1. On your iPhone or iPad, open the Settings app
2. Tap General
3. Tap Software Update
4. Tap Update Now

How to update to macOS 26.5.2

1. On your Mac, click the Apple Menu
2. Click System Settings
3. Click Software Update
4. If available, click Update Now or schedule an update with Update Tonight

AppleInsider and Apple suggest installing these kinds of minor patches. Security patches are essential for keeping your device safe and operational.

A single fake error report hijacked Claude Code in controlled testing — the agent ran the attacker’s code with the developer’s full privileges, and not one alert fired. EDR, WAF, IAM, and the firewall all missed it completely.

Tenet Security’s June agentjacking disclosure describes a single crafted Sentry error event — sent through a public credential that requires no breach and no authentication — that injected attacker instructions into error data that Claude Code, Cursor, and Codex then executed as trusted diagnostic output. Tenet tested 100-plus targets in controlled conditions and achieved an 85% success rate. Sentry called the flaw “technically not defensible.”

he Cloud Security Alliance classified agentjacking as a systemic MCP vulnerability class within days of the disclosure. No credentials were stolen, no policy was violated, no perimeter was breached: every step in the chain was authorized. That is the problem.

Tenet identified 2,388 organizations with publicly exposed Sentry credentials that could be used to inject malicious events at scale. The research is proof-of-concept, not confirmed exploitation across all 2,388. But one captured Claude Code environment held a live AWS secret access key and private repository URLs.

Here is the scope test: If your AI coding agents are connected to Sentry, Datadog, PagerDuty, Jira, or any MCP-connected data source your developers trust — and those agents can execute shell commands — then your stack has the same blind spot.

Organizations running Sentry should audit all publicly exposed DSNs immediately. Sentry’s architecture intentionally makes DSN credentials public for frontend error reporting, so the mitigation isn’t revoking the DSN — it’s restricting what agents can do with the data those DSNs return.

Why your stack can’t see it

Agentjacking works because every step is authorized: The attacker sends a valid Sentry API call using a public DSN, the MCP server returns the injected event as authentic output, and the agent executes the instruction using the developer’s privileges. No signature fired. The victim saw only benign diagnostics while the agent silently exposed cloud credentials and source-control tokens.

SOC teams have never needed to distinguish between a developer running an npm install and an agent running that command in response to a malicious error event. That distinction did not exist until AI coding agents became production tools. The stack that cannot make it is the stack agentjacking bypasses.

Five surveys, one pattern

Five independent surveys from the first half of 2026 found that enterprises trust their AI agents far more than their enforcement justifies.

Only 34% of organizations apply the same security controls to AI agents as to humans, according to an Okta/Apprize360 survey of 292 executives and 492 knowledge workers. Fifty-two percent of employees use unapproved AI tools, and 58% of executives reported an AI-related incident or close call in the prior year.

HiddenLayer’s 2026 AI Threat Landscape Report surveyed 250 IT and security leaders: 33% reported agents had already exceeded intended scope, and 31% could not confirm whether they had experienced an AI breach. One in eight AI breaches was linked to agentic systems.

Gravitee’s survey of over 900 executives and practitioners found only 14.4% of agents went live with full security approval, and 88% reported confirmed or suspected incidents. A follow-up of 750 leaders in April found agent estates had doubled while monitoring barely moved.

The runtime gap nobody closed

“Securing agents looks very similar to securing highly privileged users,” said Elia Zaitsev, CTO of CrowdStrike, in an interview with VentureBeat. “They have identities, access to underlying systems, they reason, they take action.”

Zaitsev pointed to the gap the industry left open. “No one has been talking about securing agents at runtime. We are doing that now. What is your safety net? If all these controls fail, how do you prevent them from failing silently?”

CrowdStrike’s fleet data quantifies the exposure: more than 1,800 agentic applications on enterprise endpoints, approximately 160 million instances under monitoring. On June 15, CrowdStrike shipped Continuous Identity for AI Agents at Identiverse, replacing static policies with continuous enforcement that authorizes every agent action in real time. The control class that announcement reflects — continuous action-level authorization with verifiable agent identity — is now a baseline procurement criterion regardless of vendor.

“People have kind of forgotten about runtime security,” Zaitsev said. “We did this with endpoint, virtualization, and cloud. People focused on patching vulnerabilities, locking down permissions. Somehow, they always seem to miss something. The safety net is runtime.”

Zaitsev was equally direct about sandbox approaches. “If you start with an agent in a sandbox that has no ability to touch anything, it is worthless. Very quickly, you are in this race of giving it more capabilities. And then what is the point of your sandbox?” Agents derive their value from access. Every access grant is an attack surface.

The governance gap is a budget problem

Kayne McGladrey, an IEEE Senior Member, described the structural challenge in an exclusive interview with VentureBeat. “The CISO doesn’t have the budget. The CISO doesn’t have the staff. We can observe risks, we can advise on business risks, but we don’t own the business systems affected by those risks,” McGladrey said. When agent governance spans six departmental budgets, no single executive can confirm whether agents get the same access reviews as humans.

The Okta survey quantifies the disconnect. Only 43% of workers say agent policies are clear, compared to 65% of executives, and nearly two-thirds apply weaker controls to agents than to humans. The people deploying agents daily do not recognize the governance posture their leadership claims to have built.

Assaf Keren, chief security officer at Qualtrics and former CISO at PayPal, put it plainly. “The real risk starts not by the implementation of AI systems. It is the fact that baseline architecture is not well established. When we put an AI system on top of something not architected well, we are accelerating the fractures.” Keren called runtime behavior analytics “an unsolved problem right now.”

The 5-question gap test

The five-question gap test draws on five surveys from the first half of 2026. Each question maps to a gap that agentjacking exploits. Run this before any Q3 vendor evaluation.

Security director action plan

EU AI Act high-risk compliance obligations take effect August 2, 2026. Worth factoring into Q3 planning timelines.

1. Run the five-question gap test above before any Q3 vendor evaluation — it costs nothing to administer, and the procurement clarity it creates is worth far more than the 30 minutes it takes.

2. Consider mandating agent-specific runtime detection. If your stack cannot tell what an agent did from what a developer did, agentjacking will bypass it the same way it bypassed every layer in Tenet’s testing. That distinction is the one that matters now.

3. Treat every agent as a privileged insider. According to the Okta/Apprize360 survey, only 34% of organizations apply the same controls to agents as to humans; closing that gap is the single most impactful thing most security teams can do this quarter.

4. Test the perception gap before investing in new tooling. One question to 50 knowledge workers. Do you know your company’s AI agent policies? If the gap between their answer and leadership’s answer exceeds 15 points, that is the problem to solve first. No vendor product fixes a governance posture your own workforce does not recognize.

5. Make agent census completion a procurement gate — every agent, every MCP connection. The security teams getting this right are the ones that started with a complete inventory and worked forward from there.

Agentjacking stripped away an assumption that has survived every security architecture since the first firewall went live. Authorized does not mean safe. When every step in the chain is legitimate, the only defense that matters is the one watching what agents do. Not what policies say. What agents do.

AI and ML

Central bank for central banks sees shades of dotcom mania in hyperscaler capex binge

The central bank for central banks is concerned about the eye-watering sums being invested into AI, and it’s raising the specter of a global recession should the bubble burst. 

In its annual report for 2026, the Bank for International Settlements compared the current craze to historical events, including canal and British railway mania in the 1800s, electrification exuberance of the 1920s, and the dotcom boom of the 1990s. 

The report states: “all shared one common trait: a genuine technological breakthrough that attracted capital in excess of what commercial returns could ultimately justify.

“These episodes ended with an eventual reversal in investment, inducing economy-wide recessions. The scale and pace of the current AI investment boom accompanied by expectations of large productivity payoffs bear resemblance to these precedents, highlighting potential downside risks in the near term.” 

The Register has already reported that Amazon forecasts capital expenditures of $200 billion for 2026, Microsoft is projecting $190 billion, Google some $180 billon and Meta up to $140 billion. Oracle is also betting big on AI. 

BIS estimates the five largest hyperscalers are set to spend more than a trillion dollars on AI-related capex in 2026 – and given the inflationary conditions regarding memory and that each rival is trying to outdo each other, that seems plausible.

“These commitments are outpacing earnings and the free cash flow of these firms, leading some to issue debt to raise additional financing. This investment race may be partly driven by the perception that only a small number of players with superior technology will ultimately dominate the market shares.”

Intense competition is leading to the risk of the tech giants overcommitting resources to “investment projects with still uncertain returns, leaving all firms vulnerable to disappointments in AI payoffs.” This is because as competitive pressure drives spending ever higher, the net economic surplus for the tech industry declines and “could turn negative in adverse scenarios.” 

“Disappointment in returns could trigger a sudden pullback in financing and turn the capex boom into a protracted investment bust with potential knock-on effects on the financial conditions,” the annual report continues. 

The report also cited concerns about a looming “supply side roadblock” around issues like  electricity availability, chip shortages and grid connection bottlenecks. AI datacenters are already putting pressure on energy prices and input costs with “potential spillovers to inflation.” 

“Looking ahead, these temporary shortages may also amplify over-investment, as firms attempt to lock in future capacity through long-dated contracts that further expose them to any disappointments in demand.” 

Should inflation spike or AI-led investment collapse, the macroeconomic consequences could be amplified by “existing financial vulnerabilities.” Policy rates being tightened to get a hold on inflation may precipitate a “sharp pullback in asset prices after a prolonged period of exuberant risk-taking, triggering disruptive macro-financial feedback loops.” 

Given AI companies’ “rising leverage” and a “growing footprint in credit markets”, a major change in optimistic sentiments towards these businesses could have serious financial knock-on effects. ”Vulnerabilities extend to their supplier ecosystem, including engineering, procurement and construction contractors whose balance sheets are comparatively weak, leaving them exposed to any Capex pullback by hyperscalers.” 

The “opacity” of AI-sector financing is compounding vulnerabilities as corporations create a web of private arrangements – circular financing – and the terms of datacenter facility leases are often not fully disclosed, BIS adds. 

The backdrop to all of this is that, while enterprises running pilots report some efficiency gains at a employee level, few report discernible productivity gains from AI projects that went into production environments at scale. 

The Register has long discussed concerns about the dynamics of the AI industry, as outlined in the many links in this article above. It now seems that suits in the finance industry are waking up to the potential pitfalls too.  ®

One of WhatsApp’s most in-demand features is finally coming out of beta. Later this year, the messaging app used by over 3 billion people plans to add usernames. It’s an additional (and more privacy-friendly) way for WhatsApp users to connect without sharing phone numbers.

But that means the race to grab the best WhatsApp usernames is about to begin. Hold on tight.

WhatsApp says username reservations open up this week on the platform, and you’ll see a notification in the app when it’s available. Check in the app by going to Settings and then Account. Here’s where you’ll find the Username tab if it’s enabled. Then, you’ll have the option to create a new username or port over your existing name from Instagram or Facebook. WhatsApp offers a username generator, but you can also just go with your gut (or really whatever you’re feeling at the moment).

“Usernames are designed to give you control over who gets to see your phone number in the first place,” says Alice Newton-Rex, vice president of Product at WhatsApp. “It’s an optional feature; you choose your own username, you can change it or remove it, and it doesn’t have to match your handle or account name on any other app.”

Newton-Rex stressed that this WhatsApp feature was designed around user privacy. There’s no public list of usernames for people to search through. Users can also add an extra layer of security by only allowing people who know a unique four-digit key, in addition to their username, to contact them.

Courtesy of WhatsApp

These usernames will remain fully optional, but Newton-Rex sees this new choice as a privacy measure many existing users have already expressed excitement about. “I do think that we’ll see a lot of adoption, but that’s going to be one of the things that we learn as we start rolling it out,” she says.

WhatsApp is not shy about this feature’s similarities to competitors. “Signal usernames are probably a good comparison,” Newton-Rex says. “This will work in a very similar way.” Signal rolled out usernames on its platform in 2024. Many messaging apps are still experimenting with different ways for users to connect without sharing numbers. For example, Germ DM allows its users to create “burner cards” so people can connect with multiple groups in different ways.

Proception, a robotics startup founded by former Tesla Optimus engineer Jay Li, has settled a year-long trade secret lawsuit with Tesla and raised an $11 million seed round led by First Round Capital to build dexterous robotic hands. The company told TechCrunch it is now shipping the first batch of its high-dexterity hand to researchers and robotics companies while opening to wider orders. Y Combinator and early-stage fund BoxGroup also participated in the round.

Tesla sued Li and Proception in federal court in Northern California in June 2025, accusing Li of downloading confidential files related to robotic hand actuation onto personal devices before resigning and founding the startup six days later. The lawsuit alleged that Proception’s hands bore “striking similarities” to Tesla’s internal designs. After months of legal proceedings, the two sides reached a settlement and Tesla dismissed the case earlier this month.

Li told TechCrunch he views the experience as “a resilience test, or pressure test” and believes the company emerged stronger for having survived it. He also said he would not be surprised if Tesla eventually comes to Proception for help with its own hand problem. Tesla did not respond to a request for comment.

Dexterous manipulation, the ability to grasp, rotate, and manipulate objects with human-like precision, remains one of the most stubborn unsolved problems in robotics. Even Elon Musk has called robot hands one of the biggest engineering challenges yet to be solved. Kevin Lynch, the director of Northwestern University’s Center for Robotics and Biosystems, told the Wall Street Journal last year that his team believes it will be a decade before robot hands become functional and useful enough to do what humans do.

Li thinks Proception can move faster, largely because of how it collects training data. Most companies training humanoid robots use teleoperators, where a human wearing a virtual reality headset controls a robot remotely and the system learns from the commands. A key drawback, according to Li, is that the operator receives no tactile feedback from the objects the robot touches, and the approach is limited to however many robots a company has available.

Proception’s alternative is a sensor-laden glove that captures human hand interaction data without requiring a robot in the loop. The same glove also serves as the sensor-packed “skin” on the robotic hand Proception is developing, which has 22 degrees of freedom and multiple joints per finger. Li argues this combination of scalable data collection and high-dexterity hardware is what the market is missing.

The dexterous hand market has attracted significant capital this year. China’s Linkerbot, which holds 80 percent of the global market in high-degree-of-freedom hands, is targeting a six billion dollar valuation after shipping more than 1,000 units a month. Genesis AI, a European startup, raised $105 million for a wheeled robot with dexterous hands, and Chinese competitors like Xynova have raised nearly one billion yuan.

Proception is betting that most humanoid robot companies will buy hands rather than build them in-house, mirroring how the automotive industry treats specialised components. First Round partner Bill Trenchard, who led the investment, told TechCrunch that dexterous manipulation is “the last mile of getting these robots to be truly performant.” He also praised Li’s leadership under the pressure of the Tesla lawsuit.

Tesla has discussed producing Optimus at its Shanghai Gigafactory and has deployed more than 1,000 Gen 3 units across its own facilities, but the robot’s hands remain its weakest link. Musk has set a target price of $20,000 to $30,000 per unit and projected production scaling to tens of thousands by 2028. Whether Tesla builds its hands internally or eventually sources them from companies like Proception is one of the open questions in the humanoid robot supply chain.

More than 150 companies are now chasing the humanoid robot market, with billion-dollar valuations common and only 23 percent of enterprise buyers satisfied with the products available. In that environment, a startup selling the component everyone agrees is the hardest to get right has a clear pitch, even at the seed stage. Whether Proception can scale from its first batch of shipments to a position where it shapes how an entire category of machines uses its hands is the bet First Round Capital just made.

• Grand Theft Auto 6 is selling eight times faster on PS5 than Xbox says IGN
• Xbox disputes this, however, in a statement to Windows Central
• This potential bad news comes just as Xbox announced console price hikes

IGN has reported that, based on its internal affiliate data, Grand Theft Auto 6 preorders on PS5 are surging ahead of Xbox preorders of the game at a rate of eight to one — Xbox is now saying this is far from the full picture. Though, I have a hard time believing Xbox is doing a heck of a lot better than this data suggests.

In a statement to Windows Central, an Xbox spokesperson explained that “This doesn’t represent pre-order data. We’ve had record orders. People should wait for real data and not clicks on affiliate links.”