Coatue, one of the biggest names in venture capital and hedge funds, has a new plan to generate bigger returns on AI beyond its sizable stakes in Anthropic, OpenAI, xAI, and data center companies like Singapore’s DayOne and CoreWeave.

It has launched a venture called Next Frontier to buy up land near large power sources with the goal of turning those parcels into data centers, the Wall Street Journal reports. Sources tell the WSJ that Next Frontier has already signed a joint venture with Fluidstack, a cloud infrastructure startup that penned a $50 billion deal to build data centers for Anthropic. (Coatue did not respond to a request for comment.)

Although the U.S. already has 3,000 data centers, more than 1,500 new ones are in various stages of being built, according to Pew Research, most of them in rural areas. The frenzy is enticing land speculation and data center financing projects from lots of players, ranging from Blackstone to Kevin O’Leary from “Shark Tank.”

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Meta has acquired humanoid robotics startup Assured Robot Intelligence (ARI) for an undisclosed sum, the social media giant said.

“We acquired Assured Robot Intelligence, a company at the frontier of robotic intelligence designed to enable robots to understand, predict, and adapt to human behaviors in complex and dynamic environments,” a Meta spokesperson told TechCrunch in an emailed statement.

ARI’s team, including its co-founders, will join Meta’s AI unit, the Superintelligence Labs research division. ARI had raised an undisclosed seed round from AI seed firm AIX Ventures.

The startup was building foundation models for humanoid robots to perform all types of physical labor such as household chores. Co-founder Xiaolong Wang was previously a researcher at Nvidia, and an associate professor at UC San Diego, with a list of prestigious awards to his name. Co-founder Lerrel Pinto, who previously taught at NYU and co-founded the kid-size humanoid startup Fauna Robotics before Amazon snapped it up last month, has also won a string of prestigious awards.

ARI will help Meta with its humanoid ambitions. “This team, led by Lerrel Pinto and Xiaolong Wang, will bring a deep expertise in how we can design our models and frontier capabilities for robot control and self-learning to whole-body humanoid control.”

Meta researchers have been working on humanoid robotics tech for years. A leaked memo from a year ago discussed Meta’s ambitions to build such a robot, including AI models and hardware, aimed at consumers.

Even if Meta never releases a consumer humanoid product, many AI experts these days believe that the path to artificial general intelligence (AGI) — the theoretical point at which AI reaches or surpasses human-level intelligence across all domains — will require training AI models in the physical world, where robots learn through direct interaction rather than data alone.

The ARI and Fauna deals reflect a broader industry sprint — one where forecasts vary wildly, from Goldman Sachs’ projection of $38 billion by 2035 to Morgan Stanley’s estimate of $5 trillion by 2050 — a spread that reflects both the enormous potential and the uncertainty around tech that’s still finding its footing.

Apple’s earnings call revealed a few things that make it easy to see what products we can and can’t expect between now and September. The “not coming” list is much longer than the “is probably coming” one.

The Mac is supply-constrained, the iPad isn’t being updated, and iPhones don’t release again until the fall. So, there’s not much left that could arrive in the intervening months.

The Mac mini, Mac Studio, and iMac are all awaiting their M5 upgrades, but Apple’s supply chain is already backed up quite a bit. You can’t purchase an M4 Mac mini if you wanted to.

Memory prices and scarce parts could mean a longer-than-usual wait for new Macs. It’s pretty safe to say based on Tim Cook’s remarks during earnings that there won’t be any through the summer.

The iPad is a gimme because Apple said one isn’t coming without directly saying so. During the earnings call, Apple made it clear that it would be a tough compare since the iPad with A16 was released a year ago.

So if you’re holding your breath for that new budget iPad with A19 and Apple Intelligence, you’ll be waiting a little while longer.

We’ve already got iPhone 17e, so there won’t be any new iPhones until September. Also, Apple Watch won’t get touched until then either.

iPhone, Apple Watch, and AirPods are done with updates for now

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AirPods and AirPods Pro tend to be announced alongside iPhone too. AirPods Pro were just upgraded in September 2025, but if AirPods 5 are ready, those likely won’t be announced until the iPhone event.

Apple Vision Pro just got the M5 chip in October 2025 after about 20 months on the market, so that won’t be touched anytime soon. And no, that product line hasn’t been abandoned even if rumors attempted to say as much.

There is one product category Apple could touch upon due to its unpredictable release cycle.

Apple Home products are always possible

The Apple TV 4K is still rocking the A15 processor that first debuted in the iPhone 13 in 2021. It is still supported by Apple’s modern operating systems, but at nearly 5 years old, it’s time for an update.

It’s time for Apple to update the HomePods

Since Apple TV 4K is the brains of an Apple Home, it might make sense to make that product capable of Apple Intelligence. I know I’d appreciate the upgrade to my new smart home.

The HomePod and HomePod mini are both rocking Apple Watch processors — the S7 and S5 respectively. The S7 debuted in Apple Watch Series 7 in 2021, while the S5 was included in the Apple Watch Series 5 in 2019.

It might not be entirely relevant, but watchOS doesn’t even support the S5 chipset anymore. While HomePods run a version of tvOS, that does indicate exactly how old these chipsets are.

It might be time for Apple to do a basic chipset upgrade of the HomePod and HomePod mini. While they likely won’t support Apple Intelligence natively, it would do them good to have modern networking standards for use in Apple Home.

Those are the only Apple Home products Apple offers today, but there are some rumored products too.

Home Hub and cameras are unlikely

Apple is expected to debut what we’ve been calling the Home Hub tablet at some point in 2026. There are also Apple security cameras in the pipeline, or at least a doorbell, but that release window isn’t known.

Apple security cameras, doorbell, and Home Hub are all waiting on AI

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WWDC 2026 is expected to be filled with announcements regarding Apple Intelligence. One of the biggest announcements will be about Siri and its new Apple Foundation Model backend.

That Siri upgrade is what the Home Hub has been waiting for. However, while Apple could show off the Home Hub during WWDC to demonstrate AI advancements, it is unlikely to put it for sale until later.

Since the Home Hub product and Apple doorbell don’t have an Apple-equivalent, the company can safely pre-announce them at any point. I believe WWDC would be the best place to demonstrate the Home Hub, but the already-packed event may not have room for it.

Likely nothing until the fall

Since Apple has a bundle of smart home products waiting in the wings, it is safe to assume there might be an Apple Home-focused event in the future. So, even if Apple TV and the HomePods are ready to go, Apple might hold off on them for now.

If you’ve been keeping count, that means we should all have little to no expectations for hardware before the iPhone event in September. While many are likely waiting for their pet product to get an update, they’ll just have to make do with WWDC instead.

The OS 27 cycle will be an important one for Apple. It will be among the first things released to the public under the new CEO John Ternus.

An odd rumor led to premature calls of Apple Vision Pro’s death, rumors of AI and Home Hubs abound, and Apple’s App Store troubles continue on the AppleInsider Podcast.

AppleInsider Managing Editor Mike Wuerthele joins host Wesley Hilliard as a guest this week to catch up on CEO transition news. It’s clear that the silly coverage surrounding the upcoming transition is already becoming exhausting.

The Apple vs Epic trial continues to be an ongoing event that seems to have no end. This time, Apple has to go to the Supreme Court and Circuit Courts at once.

Your hosts dive into the odd Apple Vision Pro rumor that said Apple had given up on the product. They discuss why this likely isn’t the case and how the Vision product line will continue forward.

There’s also a lot to discuss around incoming products like the Home Hub and security cameras. Wes asks Mike if Apple makes too many products.

The show concludes with a discussion around WWDC and Apple’s AI efforts.

BONUS: Subscribe via Patreon or Apple Podcasts to hear AppleInsider+, the extended edition. This week, Wes and Mike discuss their work at AppleInsider and some odds and ends surrounding that.

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For years, California’s streets have hosted a quiet double standard: a human driver caught making an illegal U-turn got a ticket, but a driverless car doing the same thing got away with it, with perhaps a call to the manufacturer. That changes now.

The California DMV has announced what it calls the most important autonomous vehicle regulations in the United States. For the first time, self-driving cars can now be formally cited for breaking traffic laws (via Futurism). 

What exactly can authorities do now?

Quite a lot, actually. Under the new rules, authorities can issue a “Notice of AV Noncompliance” directly to manufacturers whenever their autonomous vehicle (AV) commits a moving violation. All the notices add up as a formal paper trail that feeds into the DMV’s permit review process. 

Beyond traffic citations, AV companies are bound to respond to first-responder calls within 30 seconds, provide access to manual override systems, and comply with emergency geofencing directives (clearing restricted zones within two minutes of being notified).

If self-driving carmakers fail to comply, they risk suspension of permits, fleet size restrictions, speed caps, and geographic operation limits, all of which could have a negative effect on the companies’ operations and revenue. 

Does this affect self-driving trucks, too?

The same set of regulations also opens California roads to heavy-duty self-driving vehicles for the first time, with new permits now available for trucks weighing over 10,000 pounds. Aurora, which has been operating autonomous freight trucks in Texas, has welcomed the development.

What’s good is that AV companies have until summer 2026 to comply with the new communication, after which, the DMV’s enforcement kicks in. Given that the robotaxi services in America are scaling quickly, establishing a citation system tied directly to operating permits could keep things in check. 

The regulations, in totality, were partly inspired by a September 2025 incident in San Bruno, where police were powerless in front of a Waymo that had allegedly made an illegal U-turn, and by repeated cases of robotaxis clogging emergency response routes across San Francisco. 

Uber has a long-term ambition that goes well beyond shuttling passengers: the company eventually wants to outfit its human drivers’ cars with sensors to soak up real-world data for autonomous vehicle (AV) companies — and potentially other companies training AI models on physical-world scenarios.

Praveen Neppalli Naga, Uber’s chief technology officer, revealed the plan in an interview at TechCrunch’s StrictlyVC event in San Francisco on Thursday night, describing it as a natural extension of a nascent program the company announced in late January called AV Labs.

“That is the direction we want to go eventually,” Naga said of equipping human drivers’ vehicles. “But first we need to get the understanding of the sensor kits and how they all work. There are some regulations — we have to make sure every state has [clarity on] what sensors mean, and what sharing it means.”

For now, AV Labs relies on a small, dedicated fleet of sensor-equipped cars that Uber operates itself, separate from its driver network. But the ambition is clearly much larger. Uber has millions of drivers globally, and if even a fraction of those cars could be transformed into rolling data-collection platforms, the scale of what Uber could offer the AV industry would dwarf what any individual AV company could assemble on its own.

The insight driving the program, Naga said, is that the limiting factor for AV development is no longer the underlying technology. “The bottleneck is data,” he said. “[Companies like Waymo] need to go around and collect the data, collect different scenarios. You may be able to say: in San Francisco, ‘At this school intersection, I want some data at this time of day so I can train my models.’ The problem for all these companies is access to that data, because they don’t have the capital to deploy the cars and go collect all this information.”

Becoming the data layer for the entire AV ecosystem is a pretty smart play, particularly considering Uber years ago abandoned its own ambitions to build self-driving cars (a move that co-founder Travis Kalanick has publicly lamented as a big mistake). Indeed, many industry observers have wondered if, without its own self-driving cars, Uber might one day be rendered irrelevant as AVs increasingly spring up around the globe.

The company currently has partnerships with 25 AV companies — including Wayve, which operates in London — and is building what Naga described as an “AV cloud”: a library of labeled sensor data that partner companies can query and use to train their models. Partners, which Uber plans to more aggressively invest in directly, can also use the system to run their trained models in “shadow mode” against real Uber trips, simulating how an AV would have performed without actually putting one on the road.

“Our goal is not to make money out of this data,” Naga said. “We want to democratize it.”

Given the obvious commercial value of what Uber is building, that positioning may not last long. The company has already made equity investments in numerous AV players, and its ability to offer proprietary training data at scale could give it significant leverage over a sector that right now depends on Uber’s ride marketplace to reach customers.

While Elon Musk faces off against his former colleague and OpenAI co-founder Sam Altman in court, Musk’s rival firm xAI, founded to take on OpenAI, isn’t slowing down on launching competitive new products and services.

Last night, xAI shipped a new, proprietary base large language model (LLM), Grok 4.3, and a new voice cloning suite on the web.

The new products arrive after months of tumult from xAI that saw all of Musk’s 10 original co-founders of the lab and dozens more researchers exit the firm and Grok was eclipsed on performance by many new competing LLMs from the likes of OpenAI, Anthropic, Google, and Chinese firms DeepSeek, Moonshot (Kimi), Alibaba (Qwen), z.ai, and others.

While Grok 4.3 does mark a significant leap in performance on third-party benchmarks over its direct predecessor Grok 4.2, according to the independent AI model evaluation firm Artificial Analysis, it still remains below the state-of-the-art set by OpenAI and Anthropic’s latest models.

But the marquee feature of the Grok brand has — other than Musk’s stated opposition to “wokeness” and its more freewheeling personality and image generation policy — increasingly been its low price point when accessed by developers and users via the xAI application programming interface (API), a trend only furthered by Grok 4.3, which costs $1.25 per million input tokens and $2.50 per million output tokens (up to 200,000 input tokens, at which point costs double, a common pricing strategy of leading AI labs) compared to its direct predecessor Grok 4.2’s initial API pricing of $2/$6 per million input/output tokens.

According to xAI’s release notes, Grok 4.3 began beta testing in April for subscribers to xAI’s SuperGrok ($30 monthly) plan, and those of its sibling social network, X, through its Premium+ plan ($40 monthly with 50% for first two months). Now it’s available to all through the xAI API and through partner OpenRouter.

Reasoning baked-in and agentic tool-use capabilities

At the core of Grok 4.3 is a fundamental shift in how the model processes information. Unlike previous iterations where “chain-of-thought” or reasoning could often be toggled or configured by effort levels, Grok 4.3 is built with reasoning as an active, permanent state.

This means the model is designed to “think” before it speaks for every query, a strategy intended to maximize factual accuracy and the handling of complex, multi-step instructions.

The model’s memory is equally expansive, featuring a 1 million-token context window. To put this in perspective, a million tokens is roughly equivalent to several thick novels or the entire codebase of a mid-sized application.

This allows Grok 4.3 to maintain coherence over massive datasets, though xAI has implemented a “Higher context pricing” structure for requests that exceed the 200,000-token threshold.

This tiering suggests that while the “long-term memory” is available, the computational cost of managing that much information remains a significant overhead.Technically, the model accepts both text and image inputs, outputting text.

It is specifically optimized for agentic workflows—scenarios where an AI is not just answering a question but acting as an autonomous agent to complete a task.

For the first time, Grok has access to the same tools and environments a human professional would use. Evidence of this shift is visible in early user interactions:

• Spreadsheet Engineering: In one instance, the model spent 6 minutes and 22 seconds in a “thought” phase to build a comprehensive OSRS Sailing Combat DPS analyzer. The resulting .xlsx file wasn’t a simple table but a multi-sheet dashboard including a “Reference_Data” set and a complex “DPS_Calculator” with formulaic auto-calculations.

• Professional Documentation: Grok now generates formatted PDFs, such as 12-page reports on SpaceX products. These documents incorporate branding, logos, hero images, and structured tables, moving well beyond the markdown blocks of previous iterations.

• Visual Presentations: The model can design 9-slide PowerPoint decks, utilizing a “Sandwich Structure” (dark titles/conclusions with light content) and integrating data-driven decision matrices and humor.

However, its knowledge of the world is not infinite; the release notes list a knowledge cut-off date of December 2025. Yet, thanks to built-in web search, Grok can reference and use up-to-date information.

In fact, Grok 4.3 arrives with an enhanced ecosystem of tools designed to make it a functional digital employee. The xAI platform now offers a robust set of server-side tools that the model can invoke autonomously based on the complexity of the query.

• Web and X Search: These tools allow Grok to bypass its knowledge cutoff by browsing the live internet or searching X (formerly Twitter) posts, user profiles, and threads.

• Code Execution: The model can run Python code in a sandboxed environment to solve mathematical problems or process data.

• File and Collections Search: A built-in Retrieval-Augmented Generation (RAG) system allows users to query uploaded document collections or search through specific file attachments.

xAI’s Custom Voices let you clone your voice at high quality in a minute or two

Beyond text, xAI has introduced Custom Voices, a sophisticated voice-cloning API and web-based voice cloning creation suite.

This product allows developers to clone a voice from a reference audio clip as short as 120 seconds. Once cloned, the “voice ID” can be used across xAI’s Text-to-Speech (TTS) and Voice Agent APIs.

xAI’s documentation emphasizes that this is not merely about timbre; the model is designed to pick up delivery patterns.

If a user records a reference clip in a “customer support” style, the resulting AI voice will mimic that helpful, professional inflection.

Despite the creative potential, xAI has placed strict geographic limits on this feature, making it available only in the United States, with a notable exception for Illinois due to regional biometric and privacy regulations.

While the console playground is open for general use, programmatic access via the POST /v1/custom-voices endpoint is currently gated to teams on an Enterprise plan.

I tried it myself and after moving through the requisite voice sampling screens on the web — the tool asks you to read aloud several passages of unrelated dialog — I indeed had a copy of my voice that sounded eerily identical to mine and accurately pronounced new words the same way I would when reading allowed from a new script it was given.

You can delete your custom voices in one click on xAI’s Custom Voices web application and create up to 30 new ones at a time.

In terms of licensing, the Custom Voices feature is strictly “scoped to your team” and is never made available to other users, ensuring a private, commercial license for corporate assets.

Access to the new Voice Agent API (grok-voice-think-fast-1.0) is billed at a flat rate of $3.00 per hour ($0.05 per minute) for speech-to-speech interactions. This is on the low-medium end of costs for other competing voice agents, according to my research:

Complementing this is the standalone Text-to-Speech (TTS) service, which offers five distinct voices (Eve, Ara, Rex, Sal, and Leo) and is priced at $4.20 per 1 million characters.

For transcription needs, the Speech-to-Text (STT) API provides real-time streaming at $0.20 per hour, while batch processing is available at a discounted rate of $0.10 per hour.

To ensure security for client-side applications, xAI utilizes Ephemeral Tokens, allowing for secure WebSocket connections without exposing primary API keys.

Once created, these voices are private to the user’s team and can be used across all voice APIs by referencing a unique 8-character alphanumeric voice_id.

For highly regulated sectors, xAI maintains production-ready standards, including SOC 2 Type II auditing, HIPAA eligibility for healthcare workloads, and GDPR compliance.

Aggressively low API pricing as a differentiator

The most aggressive aspect of the Grok 4.3 announcement is its pricing structure. Bindu Reddy, CEO of enterprise assistant startup Abacus AI noted on X that the model is “as smart as Sonnet 4.6 and 5x cheaper and faster”.

The standard API rates are set at $1.25 per million input tokens and $2.50 per million output tokens. This reflects a significant reduction in cost compared to its predecessor, Grok 4.20, with Artificial Analysis reporting an approximately 40% lower input price and 60% lower output price.

According to our calculations at VentureBeat, that places Grok-4.3 firmly in the lowest cost half of all major foundation models, far closer to Chinese open source offerings than its U.S. proprietary rivals:

However, the “reasoning” nature of the model introduces a new billing category: Reasoning tokens.

These are tokens generated during the model’s internal thinking process and are billed at the same rate as standard completion tokens. Effectively, users pay for the AI to “think” before it provides the final answer. xAI has also introduced several unique fee structures:

• Prompt Caching: Repeated prompts are significantly cheaper, at $0.20 per million tokens, incentivizing developers to reuse context.

• Tool Invocations: While token usage for tools is billed at standard rates, the act of invoking a tool carries a flat fee—$5.00 per 1,000 calls for Web Search or Code Execution, and $10.00 for File Attachments.

• Usage Guideline Violation Fee: In a move that may set a new industry precedent, xAI charges a $0.05 fee for requests that are blocked by their safety filters before generation even begins.

The model itself remains accessible via a standard commercial API, with xAI recommending that all developers migrate to grok-4.3 as their “most intelligent and fastest model”.

Third-party benchmark evaluations and analysis

The reception of Grok 4.3 has been polarized, depending largely on the specific use case. Professional benchmarkers and developers have highlighted a “stark gap” between the model’s domain-specific strengths and its general reasoning consistency.

According to independent AI evaluation firm Vals AI, Grok 4.3 has taken the top spot on several specialized indices. It currently ranks #1 on CaseLaw v2 (79.3% accuracy) and #1 on CorpFin.

This 25-point jump in legal reasoning over Grok 4.20 suggests that the “always-on reasoning” architecture is particularly well-suited for the dense, logical structures of law and finance.

Artificial Analysis corroborated this performance, noting a massive improvement in agentic tasks, scoring an Elo of 1500 on the GDPval-AA benchmark, surpassing competitors like Gemini 3.1 Pro and GPT-5.4 mini.

Conversely, users focused on general-purpose agents and coding have highlighted deficiencies.

AI automated brick-and-mortar retail company Andon Labs reported that Grok 4.3 is a “big regression” on the Vending-Bench 2, which measures an AI’s ability to take consistent actions in a simulation.

They colorfully described the model as having “narcolepsy problems,” preferring to remain inactive for multiple simulation days rather than taking the required actions.

The sentiment was echoed by Vals AI, which noted that while the model improved in some coding areas, it remains weak on general coding tasks and “struggles with difficult math problems,” scoring only 11% on ProofBench.

Should your enterprise use Grok 4.3?

The launch of Grok 4.3 represents a calculated bet by xAI that the market wants specialized brilliance and extreme cost efficiency over a perfectly balanced generalist.

By achieving a score of 53 on the Artificial Analysis Intelligence Index while remaining on the “Pareto frontier” of cost-per-intelligence, xAI is positioning itself as the “value” leader for enterprise applications in legal and financial tech.

The “always-on reasoning” is a double-edged sword. While it provides the depth needed to navigate complex case law, the community reports of “narcolepsy” suggest that a model that is always “thinking” may occasionally think itself into a state of paralysis, or at least a state of excessive caution that inhibits agentic action.

In addition, prior Grok model scandals including an X chatbot version referring to itself as “MechaHitler” and posting antisemitic content, sexualized deepfake imagery generation and investigations, and references to racial conflicts and right-wing dog whistle framing of social issues — which appear to mirror many of founder Musk’s own positions, to the point that the model was at one point, checking Musk’s own X account before responding in its X implementation — nearly certain to give some enterprises pause when considering adoption. It’s unclear whether any of those issues remain with Grok 4.3, but one user did note that Grok’s system prompt appears to instruct it “you do not assign broad positive/negative utility functions to groups of people.”

For developers, the decision to adopt Grok 4.3 will likely come down to the nature of their data. For those needing to process a million tokens of legal documents at a fraction of the cost of Claude 4.6 or GPT-5.5, Grok 4.3 is a clear front-runner.

For those building high-frequency autonomous agents or complex math solvers, the “narcolepsy” and coding regressions suggest that xAI’s latest model may still need a few more “tuning passes”.

As OpenRouter noted on X upon making the model live, the “large jump in agentic performance” at a lower price point is an undeniable milestone. Whether that performance can be sustained across all domains remains the primary question for the summer of 2026.