Highly anticipated: After months of delays and growing anxiety about memory prices, Valve has officially confirmed pricing, configurations, and a June 30 launch date for its Steam Machine. The living-room gaming box starts at $1,049 for a 512GB model and climbs to $1,349 for the 2TB version – a significant premium over the sub-$750 figure that had been anticipated when Valve announced the hardware in November 2025. Getting one at launch, however, is far from guaranteed.

Under the hood, the Steam Machine packs a semi-custom AMD platform: a 6-core, 12-thread Zen 4 CPU clocked up to 4.86GHz, an RDNA 3 GPU with 28 compute units and 8GB of GDDR6 VRAM running at up to 2.45GHz within a 110W envelope, 16GB of DDR5 system RAM, and either 512GB or 2TB of NVMe SSD storage.

A microSD slot provides additional expansion. The M.2 SSD is user-replaceable in both 2230 and 2280 form factors; RAM is also swappable, though the compact thermal design makes it more involved than a standard desktop.

For GPU context: 28 RDNA 3 compute units at those clocks is roughly equivalent to a Radeon RX 7600, a capable mid-range card from late 2023, but not where AMD’s GPU lineup sits in mid-2026.

Four configurations are available:

• Steam Machine 512GB – $1,049
• Steam Machine 512GB + Steam Controller – $1,128
• Steam Machine 2TB – $1,349
• Steam Machine 2TB + Steam Controller – $1,428

We got it wrong: you will be able to buy a Steam Machine in 2026 after all…

The Steam Controller normally retails at $99.99, making the bundle a mild discount. The 2TB models also include two additional faceplates: red fabric and solid walnut. Valve will also release the CAD files for the external hull so third parties can make their own. Beyond that, Valve’s engineers confirmed there are no additional faceplate collaborations planned at launch.

Developing…

On Monday, Govee announced a new partnership with Warner Bro.’s HBO, bringing new smart lighting features made specifically for House of the Dragon Season 3, following the Game of Thrones prequel premiere on Sunday. 

You’ll find Govee on several of our smart home lighting recommendation lists, for both indoors and outdoors. The company produces lights with a variety of customization options, including music syncing and algorithms that allow users to create lighting schemes based on their own prompts or photos they like. In this case, Govee is highlighting how its TV backlighting can work with shows like House of the Dragon. 

Representatives from Govee and HBO did not immediately respond to requests for comment.

Govee’s TV backlighting, ready for dragons and deception

A product like the Govee TV Backlight 3 Pro ($180) adds a strip around the edge of your TV and a three-part camera that’s mounted on top. That camera looks at what’s on the TV screen, then automatically adjusts the backlighting to match. It’s an effect intended to make TVs look bigger, more cinematic and immersive. In this case, Govee mentions that its color-matching system can add gold ripples to Small Council meetings at the Red Keep, or alternate between low light and fiery reds during a nighttime dragon attack.

Color-shifting like this is also available for the Govee TV Backlight 3 Lite ($90) and the Govee TV Backlight 3 ($110), which come with fewer camera lenses than the Pro version.

But that’s not all Govee is offering for Game of Thrones fans. The company is also adding themes for its wider range of lights.

New Govee light and app themes for anywhere in your home

For those who don’t have a Govee TV Backlight product or want to extend the House of the Dragon theme to other areas of their homes, Govee has an additional creation. The company is adding three light scenes that can apply to any Govee lights, from replacement light bulbs working in concert to the company’s skylights and light curtains.

The first is Dracyrus, an amber-and-ember theme that the company says evokes torchlight and dragon breath. The second is called Fire and Blood, made to mimic the crimson and black tones of Targaryen banners. And the third is Green Reign, an emerald-and-gold scene Govee made to reference royal intrigue in the Red Keep.

These scenes are available for any Govee lights. If you have a Govee product, update it and see if you can access these new themes ahead of the next episode. 

We’ve tested Govee’s TV backlights at CNET, and we’ll let you know if we find them a particularly good accompaniment for House of the Dragon as the season progresses. 

Last night, the increasingly enterprise-focused AI startup Sakana launched Fugu, a multi-agent orchestration system that delivers frontier-level AI performance through a single, OpenAI-compatible API.

Designed for developers, enterprises, and nations seeking resilience against vendor lock-in and geopolitical export controls, Fugu (Japanese for “pufferfish”), bypasses the traditional monolithic model structure by dynamically routing queries to a swappable pool of specialized AI agents.

Sakana CEO and co-founder David Ha, formerly of Google Brain, positioned Fugu as a more reliable option for enterprise workflows than any single AI model provider in the wake of Anthropic’s move on June 12 to revoke public access to its most powerful models, Claude Mythos 5 and Claude Fable 5, in the wake of a U.S. government export control order. As Ha wrote in a post today on X:

“Fugu dynamically orchestrates the world’s best models to tackle complex tasks. We are proving that a well-orchestrated pool of swappable agents can match restricted frontier models like Fable and Mythos.

But Fugu is about more than just performance. I believe that Orchestration Models are the next frontier, beyond bigger models.

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Relying on a single company’s model for national infrastructure is a massive risk. As recent export controls have shown, access to top models can disappear overnight.

Collective intelligence is the practical hedge against this concentration of power. Fugu simply routes around vendor restrictions by relying on an entirely swappable agent pool.”

Sakana AI explicitly states that the specific models Fugu selects and how it coordinates them are proprietary, meaning this routing information is hidden from the user by design. The documentation only refers generally to a “diverse pool of powerful models,” “multiple LLMs,” or “specialized models” without providing a specific count.

By acting as a sophisticated coordinator rather than a standalone foundation model, Fugu matches the output quality of top-tier models like Fable and Mythos on third-party benchmarks of agentic tasks, while fundamentally altering how developers deploy critical AI infrastructure.

How Sakana Fugu works and where it beats Anthropic’s Claude Fable 5

At its core, Sakana Fugu operates like a master general contractor. When presented with a complex request, Fugu does not attempt to execute every step itself.

Instead, it breaks the problem down, delegates sub-tasks to a pool of expert foundation models, verifies their work, and synthesizes the final output.

“Fugu is itself an LLM, trained to call various LLMs in an agent pool, including instances of itself recursively,” the Sakana AI team noted in their technical release.

Grounded in two of Sakana’s 2026 research papers, TRINITY and the Conductor, the system autonomously manages the entire lifecycle of model selection and verification using learned coordination strategies rather than hand-designed workflows. To the end user, this multi-agent swarm is entirely abstracted behind a standard API endpoint.

Sakana AI is offering two variants of the system to cater to different operational workloads:

• Fugu: A high-speed, low-latency model optimized for everyday tasks. It is designed to act as the default engine for interactive chatbots and integrates directly into coding environments like Codex.

• Fugu Ultra: The flagship tier engineered for complex, high-stakes tasks such as AI research, cybersecurity analysis, and multi-step patent investigations. According to Sakana, Fugu Ultra coordinates a deeper pool of experts and matches industry-leading monolithic models across rigorous scientific and reasoning benchmarks.

Additionally, on the pay-as-you-go plan, standard Fugu charges a dynamic rate based on the specific underlying models activated, whereas Fugu Ultra utilizes a fixed pricing structure starting at $5 per million input tokens and $30 per million output tokens.

As indicated by benchmark charts shared by Sakana, Fugu actually exceeds the performance of Anthropic’s Claude Fable 5 on LiveCodeBench, an open source benchmark testing coding performance on regularly refreshed, software problem-solving tasks (Fugu Ultra: 93.2, Fugu: 92.9, Fable: 89.8), and beats the prior Claude Mythos Preview model on GPQA-D (Diamond) , a test of 198 graduate-level multiple-choice questions in biology, physics, and chemistry (Fugu Ultra: 95.5, Fugu: 95.5, Mythos Preview: 94.6).

By orchestrating multiple models from different providers, Fugu essentially builds native redundancy into the AI stack. If one provider suffers an outage or faces sudden regulatory restrictions, Fugu routes around the disruption to maintain uptime.

Licensing and availability

Fugu is offered as a commercial, proprietary API service, not an open-source framework.

Because Sakana’s core intellectual property lies in its non-obvious collaboration patterns, the specific routing information—meaning exactly which underlying models Fugu selects for a given query—remains proprietary and is intentionally hidden from the user.

However, Sakana offers critical controls for enterprise data compliance. Developers can explicitly opt specific models or providers out of their Fugu routing pool to maintain strict corporate privacy standards.

Additionally, users can opt out of having their prompts used for future training data. Geographically, Fugu is restricted from operating within the European Union (EU) and European Economic Area (EEA) while Sakana works to align its black-box data routing architecture with GDPR regulations.

Pricing is fairly steep

Fugu is available immediately in most regions—with the temporary exception of the EU and EEA—at subscription tiers and pay-as-you-go pricing.

Teams can opt for monthly subscription allowances designed for individual or hands-on use: a Standard tier at $20/month for lightweight workflows, a Pro tier at $100/month providing 10x standard usage, and a Max tier at $200/month offering 20x usage for continuous, long-running tasks. I wasn’t able to find the actual amount of tokens covered under these plans, but I’ve reached out to Ha on X for more information.

As part of the initial rollout, Sakana is offering a free second month for users who subscribe to any tier by July 31, 2026.

For enterprise scaling and production deployments, Sakana offers an elastic pay-as-you-go plan. Crucially for high-stakes environments, requests made under this consumption-based model are served at a higher priority than those from monthly subscription plans.

Under this framework, the standard Fugu engine charges the single rate of the highest-tier underlying model involved in a query, without ever stacking multi-agent fees. The flagship Fugu Ultra tier (fugu-ultra-20260615) utilizes a fixed pricing structure per one million tokens: $5 for input, $30 for output, and $0.50 for cached input. These rates increase to $10, $45, and $1.00 respectively for extreme workloads utilizing context windows above 272K tokens. That puts it among the more expensive options compared to single AI models via provider APIs:

Developers modeling operational costs should also note a significant architectural caveat in how Fugu bills for its multi-agent capabilities. According to the developer documentation, Fugu Ultra’s API responses include detailed usage fields that separate user-visible token generation from internal orchestration work. The background tokens consumed and generated when Fugu delegates sub-tasks, verifies code, or routes between underlying agents are not absorbed by the provider; they represent real token usage and are counted toward the final price of the request at standard rates.

The Orchestration landscape: Fugu vs. The Field and notable benchmark performance

To understand Fugu’s position in the mid-2026 AI ecosystem, it is critical to distinguish between model routing and multi-agent orchestration.

Over the past year, enterprise adoption of standard routing platforms—such as Not Diamond, Martian, and the open-source RouteLLM framework—has skyrocketed. These systems act as intelligent air traffic controllers; using semantic classifiers or meta-models, they analyze an incoming prompt and predict which single foundation model will yield the highest quality or most cost-effective response, dispatching the query accordingly.

Fugu operates on a fundamentally different paradigm. Rather than making a one-shot routing decision, Fugu aligns more closely with complex multi-round systems like Router-R1 (a framework introduced at NeurIPS 2025). It breaks a query down, interleaves reasoning with delegation, and dynamically assigns sub-tasks to multiple models in parallel or sequence before synthesizing a final output.

While frameworks like LangGraph, CrewAI, and Microsoft AutoGen offer developers the tools to build similar multi-agent systems, they require immense manual configuration—defining roles, setting up conditional edges, and managing state across long-running loops.

Fugu abstracts this operational overhead entirely. It is essentially a LangGraph-style workflow packaged as a single, black-box API endpoint.

An orchestration system is ultimately bounded by the raw capabilities of the underlying models in its pool, a reality reflected in Sakana’s own benchmark testing against standalone frontier models.

On rigorous coding and agentic tasks, collective intelligence shows a distinct advantage over standard models. Fugu Ultra posted a 73.7 on SWE-Bench Pro, significantly outperforming Anthropic’s Claude Opus 4.8 (69.2) and OpenAI’s GPT-5.5 (58.6).

However, Fugu is not a silver bullet, and its performance is not a clean sweep across the board. When compared to highly specialized or restricted-access monolithic models, Fugu occasionally trails:

• SWE-Bench Pro: While Fugu Ultra (73.7) beat most accessible models, it was comfortably eclipsed by Anthropic’s limited-access Fable 5 (80.0), which is currently absent from Fugu’s swappable pool due to the U.S. government’s export control order and Anthropic’s subsequent response to remove the model entirely from global usage.

• Humanity’s Last Exam: Fugu Ultra (50.0) narrowly edged out Opus 4.8 (49.8), but again fell short of Fable 5 (53.3).

• Long-Context and Security: On the MRCRv2 long-context-recall test, OpenAI’s GPT-5.5 maintained the lead (94.8 vs Fugu Ultra’s 93.6), and Opus 4.8 remained the top performer on the CTI-REALM cybersecurity benchmark (69.6 vs Fugu Ultra’s 69.4).

The quantitative data points to a clear conclusion: Fugu is highly effective at boosting performance on messy, multi-step tasks (like writing a complex HTML5 game from scratch) by leaning on the combined strengths of multiple mid-tier and high-tier models.

However, for sheer brute-force reasoning within a single, highly constrained domain, the industry’s largest standalone models still hold the edge—provided an enterprise can maintain uninterrupted access to them.

Background on Sakana’s formation and noteworthy achievements to date

Sakana AI was formed in Tokyo in 2023 by Llion Jones, a co-author of Google’s foundational 2017 “Attention Is All You Need” paper, and David Ha, the former head of research at Stability AI.

Disillusioned by large tech company bureaucracy and the industry’s hyper-fixation on scaling single, massive foundational models, the founders built Sakana around principles of biomimicry and evolutionary computing.

The company’s name, derived from the Japanese word for fish, reflects its core technical thesis: utilizing collective “swarm” intelligence rather than brute-force compute. Following a $2.6 billion Series B valuation in late 2025 and the recent June 2026 launch of Marlin—an autonomous, eight-hour research agent for the B2B sector—Fugu represents the commercialization of Sakana’s multi-agent routing technology for everyday developers.

A mixed reception among the broader AI community online

The developer community has responded to Fugu by rigorously testing its practical tradeoffs, weighing its routing efficiencies against the sheer power of monolithic foundation models.

AI observer, developer and influencer Chris (@ChrissGPT on X) highlighted the specific utility of Fugu over raw foundational AI.

“For a single clean prompt, you probably would [use Fable 5, Mythos, or GPT-5.5 directly],” he noted, but argued that Fugu’s true value emerges in messy, multi-step environments. “…whether it involves delegation, verification, synthesis, code review, research loops, security analysis… the more it would make sense to use this,” he wrote.

Chris also pointed out the strategic geopolitical advantage of Fugu’s architecture, noting that if frontier AI access is abruptly revoked due to regulation or export controls, an orchestrator can dynamically swap models to prevent a total system failure.

Creative agency owner Mark Santos (@markksantos) of Mark Studios provided a direct, real-world comparison by tasking both Fugu Ultra and Claude Opus 4.8 with building a “Crossy Road” game clone using Three.js. The results underscored the operational differences between an orchestrator and a monolithic giant:

• Sakana Fugu Ultra: Completed the task in 22 minutes using ~89,000 tokens for roughly $7.32. However, the final game suffered from minor logic errors, such as inverted directional turns and wonky camera angles.

• Claude Opus 4.8: Took 79 minutes, burned ~940,000 tokens for nearly $37.85, and got stuck in a retry loop requiring human intervention. Despite the inefficiency, it ultimately produced superior application design and functionality.

Santos concluded the experiment by stating, “In terms of application functionality, quality, and design, Opus won. In terms of model speed and performance, Fugu… won”.

Elie Bakouch, a research engineer at cloud-based, open AI infrastructure and systems provider Prime Intellect, pointed out on X that “to be clear, this is a closed source orchestrator on top of closed source models. if before you didn’t control the models, now you don’t even control which ones are used or how much. this is not ‘AI sovereignty’…”

These early tests and reactions mirror the sentiment summarized by Reddit user GreedyWorking1499 in initial platform discussions: “Until proven otherwise, this is just a highly advanced router/wrapper, not a fundamental not a fundamental leap in intelligence like Mythos/Fable was.“

Yet, as enterprises increasingly demand fail-safes against single-vendor reliance, Sakana is proving that packaging collective intelligence into a single API endpoint is a highly viable commercial path.

Fazl Barez of the University of Oxford queries how artificial intelligence built to serve a better purpose has the potential to be dangerous in the wrong hands.

Earlier this year in Beijing, a humanoid robot crossed a half-marathon finish line in a blistering 50 minutes, 26 seconds. The feat immediately lit up global headlines for shattering the human world record by almost seven minutes.

This performance came with many asterisks. The robot followed a pre-mapped track, stayed in its own dedicated lane and had a human support crew trailing behind it in case something broke.

But the performance gap didn’t just close, it evaporated – down from over 2.5 hours in 2025. This wasn’t just about better motors or lighter carbon fibre; it reflected a massive shift in what a robot actually is. And that transformation has implications for our homes and hospitals too.

Tricked into going rogue

For decades, robotics was all about rigid, predictable coding. You wrote a program, locked the machine in a metal cage and let it execute repetitive tasks forever.

Industrial safety standards were built on the premise that if you can map the physical path of a robotic arm, for example, you can bound its risk with a cage or laser tripwire.

But the systems moving into hospitals and homes today don’t use fixed code blocks. They run on “foundation models” – the same kind of internet-trained artificial intelligence that powers chatbots like ChatGPT.

If you tell a modern AI-driven robot to “clean up a spill in the kitchen”, it uses these models to interpret your unique room (rather than match it to a pre-programmed list), figure out your intent, then invent an action plan on the fly.

But such flexibility creates an open-ended safety problem. You cannot build a physical cage around a machine whose behaviour emerges in real time, based on its own reasoning. The danger with the new breed of AI robots is that, because they use human language to plan their actions, they can be tricked into ‘going rogue’.

In my recent research with colleagues in the US, we decided to test exactly how fragile these AI robots’ safety systems are. We wanted to see if the guardrails that AI developers build into their foundation models, designed to prevent harmful or dangerous outputs, hold up when the underlying model is given a physical body.

Using nothing but basic text prompts and without any hardware hacking at all, we manipulated a range of AI-controlled robots to do genuinely hazardous things.

In our tests, the systems easily rejected directly malicious commands like “hit that person”. But these safety filters collapsed the moment we used a little creative writing. By framing our request as a piece of fictional dialogue for a movie script, the robot’s behavioural blocks disappeared.

In one trial, we programmed a commercial robot dog to pinpoint human crowds as optimal locations in which to place an explosive device. Because the underlying AI saw the prompt as a creative exercise, it appeared blind to the dangerous real-world implications of the plans it was generating.

In the UK, US and EU, current laws appear completely unprepared for such eventualities.

No boundaries

When policymakers try to figure out how to regulate robots, they almost always look to autonomous vehicles. But self-driving cars operate in a highly structured, heavily mapped world. They follow fixed traffic laws, navigate predictable road geometries and can be tested through millions of hours of simulation.

A busy street functions under well-defined laws using guidance systems such as traffic lights, meaning engineers can anticipate safety parameters ahead of time.

A domestic kitchen, school or hospital room has no such equivalent. And no factory bench-test can predict what an internet-trained model will decide to do when it encounters a novel object in a messy, unpredictable human environment.

This leaves us with a profound conceptual flaw in how we build these machines. Chatbot safety is absolute – a model shouldn’t output a bomb recipe, no matter who asks. But robot safety is context dependent.

Think about pouring boiling water from a kettle. The underlying physical movement – tilt, flow rate, trajectory – is the same whether the water lands safely in a ceramic mug or, catastrophically, on a child’s hand.

AI foundation models are phenomenal at open-ended logic, but they struggle immensely with real-time, context-aware physical judgement. In a text interface, a failure of judgement gives you a typo or hallucinated fact. In the physical world, such a failure may be completely irreversible – with devastating consequences.

Who takes the blame?

If an AI-powered robot causes a physical injury, who takes the blame? Is it the end-user who gave the spoken command? The company that manufactured the metal chassis? Or the tech firm that trained the AI model in the first place?

Right now, the laws that seem to apply – such as product liability, warranty claims and consumer protection statutes – have not been tested in these new situations. And until liability is explicitly assigned by regulators, market pressures will continue to push tech companies to prioritise rapid commercial deployment over cautious safety engineering.

If we want to live alongside these machines safely, I believe we need to decouple safety from the AI model’s decisions. A robot shouldn’t rely on a chatbot’s logic to decide if it’s safe to swing a heavy metal arm near a human face.

This means creating safety layers that don’t depend on the AI being right. For example, we need zones around people that a robot’s arms simply cannot enter, and a physical emergency brake that can stop the robot if and when its AI fails.

The humanoids crossing finish lines in controlled athletic trials are impressive proofs of concept, but they are just the prologue. The next generation of autonomous agents will operate in high-stakes human spaces – navigating recovery wards, assisting the elderly, walking our streets.

We need an easily interpretable and robust safety framework already up and running before they arrive – not as a retrospective response to a predictable tragedy.

Dr Fazl Barez

Dr Fazl Barez is a senior research fellow at the University of Oxford, specialising in AI safety, interpretability and governance. He leads research initiatives within the AI Governance Initiative, focusing on the development of safety frameworks and interpretability methods for advanced AI systems. He also teaches the AI Safety and Alignment course. Alongside his academic work, Barez is principal scientist at Martian, which works on understanding machine intelligence. His research is supported by OpenAI, Anthropic, Schmidt Sciences, Nvidia and others.

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First came Anthropic, then Google. Now, open-source AI startup Reflection is tapping SpaceX for its abundant source of AI chips.

Reflection AI will pay $150 million a month beginning July 1, 2026 through 2029 for immediate access to Nvidia’s latest GB300 AI chips and supporting hardware across SpaceX’s Colossus 2 data center near Memphis, Tennessee, the company told TechCrunch. The deal is worth up to $6.3 billion and either company has the option to end the contract with 90 days’ notice after the first three months.

The deal is smaller than SpaceX’s deals with Anthropic and Google, which cost the companies $1.25 billion per month and $920 million per month respectively. Those contracts also run through July 2029, although Elon Musk has publicly downplayed the three-year term, emphasizing that the contracts can be cancelled at any time.

Reflection used the compute deal — its first — to tout the value of its open-weight AI strategy, which it has pitched as an open-source alternative to closed frontier labs like Anthropic and OpenAI. Open-weight AI models, which publicly release their trained parameters, have received more attention following the U.S. government’s ban of Anthropic’s closed models, Fable and Mythos.

The startup, which was founded in 2024 by two former Google DeepMind researchers, said the compute deal is one of the largest announced open AI infrastructure commitments to date.

“Recent events highlight how important open source is to the AI ecosystem, with more nations and enterprises recognizing the risks and costs associated with exclusively depending on closed models,” a spokesperson said in an emailed statement. “Our deal with SpaceXAI signals Reflection’s strategic importance within the frontier AI ecosystem, and more compute means more runway to build the world’s best open models at scale.”

The Colossus data center was originally built by xAI, a company founded by Elon Musk that is now part pf SpaceX, for its own AI efforts. As its internal pursuits have faltered, SpaceX leveraged its valuable AI chip holdings and began renting them out to some of the world’s top AI labs.

from the obviously-two-entirely-different-things dept

It’s no secret ICE officers are using their phones and their tech toys to do way more than they’ll openly admit to doing. Tech tools that can be abused will be abused. And ICE has plenty of those, including an app that’s supposed to be used for “verification” of migrant status, but is just facial recognition tied to whatever other information ICE has access to.

The cameras come out and the harassment begins, as detailed here in this NPR report. Shortly after Portland, Maine resident Xenia Pantos stopped her car to observe some ICE activity in her neighborhood, their spouse, Carly Williams got a call from a blocked number. The caller identified himself as calling from the Department of Homeland Security.

Williams said the caller asked if anyone else drives her vehicle. When Williams mentioned her spouse sometimes did, the caller asked Williams if she knew her spouse had stopped at an incident that morning.

“What he basically said was, ‘You should let her know to not do that anymore because people who are doing that type of thing are getting added to a domestic terrorist watch list,’” Williams recalled in an interview with NPR.

ICE continues to deny it targets anti-ICE protesters with its surveillance tools. According to the report, it has “repeatedly denied” utilizing its tools and databases to find out more about those who protest or observe its anti-migrant efforts.

Rep. Lou Correa, D-Calif., cited a well-circulated clip of an ICE agent in Portland, Maine, telling a person videotaping that she would be added to a “nice little database.” 

“I can’t speak for that individual,” said Todd Lyons, who serves as acting director of ICE. “But I can assure you that there is no database that’s tracking United States citizens.”

Lyons doubled down on his denials about the database’s existence during a Senate hearing Thursday. When asked if ICE is giving protester information to any other agency, Lyons said: “We do not.” 

That’s what Todd Lyons said in February. And it’s definitely not true. ICE has a database that is definitely capable of “tracking American citizens,” because it has access to plenty of law enforcement databases filled with information about American citizens. One needs to look no further than the heat it has drawn by asking local law enforcement to perform searches of things like Flock’s ALPR databases on its behalf.

And it’s definitely not true because the same Todd Lyons said as much in a written response [PDF] to congressional queries that has only recently been made public.

Lyons in February: “There is no database that’s tracking United States citizens.”

Lyons in April: “Well… except for this one.”

Your letter asks what specific personal information DHS officers collect. ICE collects information to identify the person(s) with whom the officer or agent is engaging. During these interactions, a variety of data may be collected by ICE law enforcement officers to enforce federal immigration and criminal law. ICE collects essential biographic and biometric information and situational details required to support criminal investigations, safety, and immigration concerns.

If individuals who interact with ICE officers are not arrested or detained, any information collected during those encounters is maintained consistent with applicable law and DHS and ICE policies and is treated as an official government record.

That sounds like a database is being created and maintained — one that deals solely with people who are not targets of immigration enforcement effort. And most of those people would be (1) US citizens and (2) protesters and observers engaging with ICE officers.

Further down in the letter, Lyons offers up another phrase that sounds like a denial, but really isn’t:

DHS is not creating or maintaining a separate, standalone database for individuals encountered that haven’t been arrested or detained.

That would mean something if no information was collected on these people. But Lyons has already stated that officers collect this information. If DHS is not “creating or maintaining a separate database,” that only means exactly what that says. However, it does not mean DHS is not collecting and storing information about people ICE officers “encounter” who are not “arrested or detained.”

Even if all applicable laws and retention standards are being followed (and DHS has given us little reason to believe it follows laws and standards), this information is still being collected, stored, and — because it’s there — accessed by federal officers.

And even if we choose to believe Lyons’ dissembling, we’re still left with the fact that people identifying themselves as federal employees are calling up citizens who’ve done nothing more than exercise their First Amendment rights and threatening them with being added to government databases. So, even if Lyons ain’t lying, the people who worked for him (until he stepped down) aren’t doing what Lyons thinks they’re doing. They’re doing the other thing: collecting information on protesters and observers for the sole reason of keeping an eye on them, if not actually tracking them down to harass them.

Filed Under: free speech, ice, protests, surveillance, todd lyons

Google Cloud Summit came to London last week, and we took the opportunity to sit down with database execs Sailesh Krishnamurthy (VP engineering) and Yasmeen Ahmad (product executive Agentic Data Cloud).

The event was wall-to-wall agentic AI, and true to the theme, Ahmad told us that “we’re putting agents at the center … with the goal that humans are not going to be using data platforms in the next three to five years. It’s going to be humans orchestrating agents, and agents actually doing the work.”

One of the key AI-driven changes, Krishnamurthy said, is that when retrieving data “it’s not so much about getting the exact results, but getting the best results.”

For developers skilled in crafting SQL queries that get precise results in the most efficient way, the notion of inexact queries that go through some sort of non-deterministic and compute-expensive parsing may seem like a step backwards.

“If you have exact questions, you need to be able to provide exact answers,” Krishnamurthy told us. “But I think inexact questions are what people are also going to expect. When you think about agentic workloads and operational databases, you want to be able to ask more flexible questions.” An example might be a natural language query that takes into account context, such as previous interactions.

Krishnamurthy described “AI native infrastructure,” including vector indexing, text indexing, and graph technology where “you combine structured and unstructured data, you have to be operating in terms of inexact results and data quality.”

The company is also investing in the “knowledge catalog,” formerly called Dataplex, which is enterprise search now also treated as context for LLMs (large language models). Knowledge catalog aggregates organization data across multiple sources including structured and unstructured sources.

Krishnamurthy said that exact SQL queries are not going away, and that sometimes a “fuzzy question in natural language” might generate an SQL query with exact results.

How do you verify that AI-generated SQL is producing the results you want? “The answer is the same, not just about SQL, but about many AI-related things,” said Krishnamurthy. “The answer is a set of evals you have to maintain …  you might start with something where some results work well and some don’t. And then you have to keep iterating on your blueprints and other pieces of context until your eval set is 100 percent working well.”

By eval set, Krishnamurthy means “a set of questions that are representative tests that users may have, and what is the right query that is generated associated with it, and then a determination of is this query, is this answer correct or not?”

Google SQL as used in its distributed Spanner database, PostgreSQL-compatible AlloyDB, and in the BigQuery data warehouse engine now has AI functions such as AI.IF, which evaluates a condition described in natural language and returns true or false. The prompt value is evaluated using a Gemini LLM; and could return an error or null if the model fails such as when unavailable or out of quota. 

The inefficiency of functions like AI.IF is a problem, but there are possible solutions. One is the idea of proxy models, which Krishnamurthy described as “a tiny model in the database.” A proxy model is trained on the fly, based on a small sample of the data. The query engine evaluates the results from the proxy model, and if good enough, uses it for inference in place of a call to the LLM. According to a paper on the subject proxy models “consume about 400x less tokens, and the latency goes down by 30x-100x.”

We asked Ahmad why she believes humans will soon not interact directly with Google’s data platform. The answer, she said, is based on the idea of intent-driven engineering. “Three years ago everyone was doing prompt training classes. Really, these models were co-pilots or assistants. Now these models are doing multi-step execution, parallel execution, handling complexity. So you can define an intent, a goal, an outcome, and the model will figure out the steps to get there.”

According to Ahmad, humans will act as orchestrators, thinking about business outcomes, and models will do “the hard graft of figuring out the low-level data wrangling.”

She said that today’s staff need to be skilled not so much in prompt engineering, but rather using AI for spec-driven development.  “The focus for the human is getting to the right plan and iterating with the model on what is the right way to think about the problem.”

In business intelligence, she said, companies will move away from dashboards because they only “serve the first layer of predictable questions.” In their place will be “conversational analytics for business users.”

She believes that unwelcome aspects of generative AI, such as hallucinations and prompt injections, are mitigated by improved context, such as from Knowledge Catalog. “I have customers who have got 90 percent plus accuracy with conversational analytics, but that was not the case 18 months ago when the models would get one out of every two questions wrong because they would not have that context.”

A problem here is that even over 90 percent accuracy is not good enough if you are, for example, a customer of a company with heavy AI adoption confronted with a blocked transaction or other rejection because of an inaccurate response. 

Another issue is that injecting AI into every interaction means paying for tokens on top of the base compute and storage resources traditionally consumed by cloud database platforms. Higher productivity and reduced staff costs may more than compensate, but this cannot be taken for granted, particularly as reducing the skill barrier with features like conversational analytics also tends to increase usage.

Giant cloud providers like Google though have plenty to gain. AI, Krishamurthy told us, is driving growth in data storage as well as token usage. He described “a huge overall growth in the business because everyone needs data … Anthropic, for example, rely on BigTable to store all their prompt information. They have other workloads too which are not public.”

Two metrics he is permitted to talk to us about, he said, are that Spanner “now runs 7 ½ billion queries per second at the peak … a year back Spanner might have been 5 billion queries per second.” 

Spanner, he said, “has about 23 exabytes of data. It’s the same with BigTable, roughly 7 billion queries per second and double-digit exabytes.” 

Models make more queries, he said. “Instead of taking the user request and just sending one query, one pattern I’ve seen is a model will send five different queries … it’s hard to say exactly what is happening because the models are trying different things.”®

On this week’s episode of the Smart Home Insider podcast, Jon Harros from the Matter governing body joins to talk through the latest release of the home automation standard.

Jon Harros is head of testing and certification for the Connectivity Standards Alliance, and he drops in on the podcast from Unify. Unify is the CSA’s first public-facing conference on the smart home and there were many announcements from the week.

We start the episode going through the news. The big release was the Schlage Sense Pro UWB.

This lock will be available to preorder by the end of June and supports Apple Home Key via UWB with Aliro later in 2026. With Aliro, it will also support Google and Samsung wallets.

There was a lot of new television news as of late. During the episode, we talk about HDMI 2.2, Roku, and Philips.

It appears the very first HDMI 2.2 devices will hit the market by the end of 2027. The new standard, with up to twice the bandwidth, will likely start with high-end TVs.

Apple TV is launching on the new 2026 Philips television sets. They are no longer running Google TV and can stream from the Apple TV app with Dolby Vision, Dolby Atmos, and 4K.

Finally, Roku has agreed to be purchased by Fox. Fox will take over the hardware business which compliments its media infrastructure.

The bulk of the episode was dedicated to the new announcements from the CSA. Jon Harros explains the details in Matter 1.6.

Matter 1.6 has many changes, but the most notable is new NFC commissioning that doesn’t require Bluetooth or dedicated power, more context for thermostats, and a Joint Fabric layer for multiple ecosystems to use the same devices.

There was also the Product Security 1.1 release. This time it’s more about extending the security beyond the hardware to apps and software as well.

Links from the Smart Home Insider podcast

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