• The Air Force wants drones spanning hobbyist toys to full stealth fighters
• Fighter-mimicking drones must fly past Mach 1.6 at high altitude
• Bomber-sized drones need matching radar, heat, and physical dimension signatures

The US Air Force is seeking industry proposals for target drones capable of imitating threats from cheap commercial aircraft to stealth fighters.

A recent Request for Information issued by the service sets a deadline of September 28 2026 for companies to submit concepts.

Pop icon Cher’s 1969 album, recorded at the legendary Muscle Shoals Studio, was both a critical success and a commercial failure. Over the years, however, 3614 Jackson Highway has only grown in stature, prime evidence of her artistry and a tasty platter of choice covers arranged with that just-right blend of Southern soulfulness and pop sheen.

That said, finding original copies of this now much sought-after album is not as easy as it used to be, and thus prices can be costly on the collector’s market in top condition. Fortunately, there have been reissues over the years, and in fact, there is a new edition from Rhino’s generally excellent High Fidelity series which you may want to consider, albeit with a bit of caution.

The basic ingredients of this new Rhino reissue appear typically idyllic: 180-gram vinyl pressed at Optimal Media, with lacquers cut by Kevin Gray at Coherent Audio from the original master tapes. The label made this tip-on-style glossy laminated cover extra spiffy, adding a silver foil finish beneath Cher’s name. With three Dylan covers, as well as tunes by Dr. John, Stephen Stills, Steve Cropper, Otis Redding, Dan Penn, Spooner Oldham, and Chips Moman, 3614 Jackson Highway has all the ingredients for a terrific listen.

My listening process began well, actually, with the Rhino HiFi edition bettering my original white label promo “Monarch” pressing, the sound appearing wider, bigger, and richer overall.

Unfortunately, on Side 2, I began to hear nasty noises, which turned out to be a rather significant rash of “non-fill.” This production error can happen when the vinyl does not melt well enough to flow into the metal stamper grooves. An imperfect copy results, bearing tiny audible pits where the vinyl didn’t fill the grooves (thus the “non-fill” designation). You can usually easily see these telltale markings, such as in the photo following.

Trying to be fair, I wrote to the label and asked if they could send a replacement, hoping my problem album was a one-off anomaly. So far, Rhino’s High Fidelity series has been exemplary.

Unfortunately, my second copy also had non-fill issues, and this time there was also an audible hairline scratch on one side! This is a very unusual occurrence, in my experience, for records pressed at Optimal. I am not entirely surprised, given the strange, upside-down socioeconomic times our world is enduring these days, where understaffing and overwork are the norm. Someone clearly was rushing the process to meet deadlines.

Stuff happens. I get it. But this doesn’t make it any easier for consumers spending $39.98 on a new record who expect perfection! So, I went ahead with writing this “heads-up” type review. I am not trying to dissuade you from buying Rhino High Fidelity’s reissue of Cher’s 3614 Jackson Highway. If you are not in a rush, you might, however, want to wait for the non-numbered regular edition of this reissue series, which I hope will be a different pressing run that will showcase all the love and care we have come to expect from Rhino and Optimal.

If you do want to order it right away, however, you can go to Rhino’s website to order it for $39.98.

Our Ratings

★★★★★★★★★★ Music

★★★★★★★★★★ Sound

★★★★★★★★★★ Packaging

Mark Smotroff is a deep music enthusiast / collector who has also worked in entertainment oriented marketing communications for decades supporting the likes of DTS, Sega and many others. He reviews vinyl for Analog Planet and has written for Audiophile Review, Sound+Vision, Mix, EQ, etc.  You can learn more about him at LinkedIn. 

Related Reading:

For much of the past two years, the general belief in enterprise AI has been that more autonomy equals better performance. Build agents that can plan, decide, and act across multi-step workflows, and give them as much room to run as possible. That assumption is now being tested at scale, in real production environments — and in a lot of deployments it’s failing. The companies that end up benefiting from agentic AI won’t necessarily be the ones that have given their agents the most flexibility. They’re the ones who create AI agents with specific responsibilities and make sure they operate within clear rules.

Two numbers tell you almost everything about where agentic AI stands in mid-2026. By Gartner’s own forecast, more than 40% of the agentic AI projects running today won’t survive to see 2028. Not because the models fall short; because of escalating costs, unclear business value, and inadequate risk controls. McKinsey’s 2026 AI Trust Maturity Survey fits right alongside that prediction: Agentic AI deployment is accelerating across every industry, but average responsible-AI maturity sits at just 2.3 out of 4. Only about 30% of organizations have reached a maturity level of three or higher in governance and agentic AI controls specifically.  

Put those two numbers side by side, and the story tells itself. Capability is outrunning control.  

That shift is changing the competitive framing, too. The 2024-to-2025 race was about who could deploy the most autonomous agent the fastest. The 2026-to-2027 race is a trust race.  

It’s not about who can build the most capable agent. It’s about who can get an agent approved for production by risk, legal, and compliance teams, and keep it approved once it’s live. This is a different kind of engineering challenge than most enterprises are prepared for.

Why full autonomy breaks down in production  

Gartner lays out the failure pattern as specific and repeatable. Projects launch with ambitious, broadly autonomous workflows. They hit integration complexity within weeks. Then they stall, with no defensible path to production ROI. Part of the problem is vendor noise. Gartner’s own count puts it starkly: Out of the thousands of products being sold under the ‘agentic AI’ label, only around 130 actually have real autonomous capability behind them. The rest are largely automation or chatbots repackaged for the moment.

But even genuinely agentic systems run into a structural problem that has nothing to do with hype. Autonomy and accountability move in opposite directions.  

An agent capable of independently planning and executing a multi-step task is also an agent whose individual decisions get harder to trace after the fact. Let’s say something breaks a few steps into an autonomous chain. Figuring out why the agent made that decision and who is responsible can be a complicated process, not a simple lookup.

In areas like financial reconciliations, compliance processes, manufacturing quality checks, or clinical documentation, this lack of transparency can be the difference between a manageable mistake and a serious regulatory breach. It’s the reason legal, risk, and compliance teams block agentic projects from reaching production, regardless of how capable the underlying model is.  

Integration complexity keeps showing up as a leading cause of project cancellation. Bolting an autonomous agent onto a legacy workflow takes more than technical connective tissue. The workflow’s existing decision points, approval chains, and audit trails all need to be rebuilt around a system that can now act without waiting for a human. Enterprises that treat this as a pure integration problem, solvable with more engineering hours, tend to be the ones that stall.  

This isn’t a hypothetical risk. McKinsey’s research shows how exposed most enterprises currently are. Across nearly every category of AI risk, from data privacy to intellectual property exposure, the gap between the risks organizations say they’re aware of and the risks they’re actually mitigating remains wide.    

Awareness has surpassed action. This gap is reflected in the businesses that report it as an obstacle to further scaling of agentic AI. Nearly two-thirds now say security and risk issues are the greatest challenge for them, surpassing regulatory uncertainty and technical barriers.    

What governed orchestration actually looks like  

The enterprises that are leading the way are not halting their AI plans. They’re restructuring how autonomy is being distributed within the system. The four patterns that stand out in organizations that are governance-mature are:  

• Narrow-scope agents over general-purpose ones. Decompose end-to-end workflows into single-responsibility agents with tightly bounded mandates. A smaller scope of work results in a smaller scope of failure, and a smaller scope of failure is much easier to audit.  

• Human checkpoints at decision boundaries, before the outcome, not after it. Review agent decisions before high-stakes actions execute, not after the fact. That means checkpoints before sensitive data moves, a transaction posts, or an external system is triggered. McKinsey’s framework calls for real-time, data-driven monitoring built into the agent pipeline itself, with humans retaining final accountability specifically for high-stakes decisions.  

• Decision traceability as a design requirement. A full action log and decision lineage should be available on demand for any agent, any decision. It shouldn’t need to be reconstructed under pressure during an audit. Regulators are pushing the same way. The EU AI Act’s human oversight requirements for high-risk systems are still coming, even though this year’s Digital Omnibus agreement pushed the compliance deadline out to December 2027. Enterprises building agent systems now are effectively building toward that requirement, whether or not it’s technically enforceable yet.  

• Data sovereignty does active governance work, not passive paperwork. Where an agent’s data sits, and who has access to it, decides how contained a failure can be. On-premise or controlled-environment deployment limit the blast radius of a misbehaving agent and simplifies exactly the kind of audit trail regulators and boards are starting to expect.  

The risk runs in both directions, of course. Agentic AI is supposed to cut friction. An agent that needs a human to sign off on every minor task hasn’t cut anything; it’s just automation wearing a manual process as a costume. That quietly undercuts the whole case for building the agent in the first place. The goal isn’t maximum control. It’s calibrated control, concentrated where the cost of an error is actually high.  

Agent deployment is scaling roughly 8x faster than governance maturity is improving.  

A practical framework for evaluating your agent stack  

If enterprise architects are reviewing an existing agent for deployment or are considering deploying an agent, they can begin by asking four questions.  

1. Can you reconstruct, six months from now, exactly why a specific agent took a specific action?  
If the honest answer requires digging through raw logs or guessing, decision lineage isn’t a design feature of the system. It’s an afterthought. And it will show up as a gap in the next audit.  

2. Does every agent in the stack have one clearly bounded responsibility, or is at least one agent authorized to “figure it out” across a broad task?  
Broad, open-ended mandates are exactly where compounding errors and untraceable decisions originate.  

3. Are human checkpoints placed at defined decision boundaries, or only as a final review after the agent has already acted?  
A review after the fact catches consequences. A checkpoint before the fact prevents them.  

4. If an agent were compromised or malfunctioning right now, how much data and how many downstream systems could it touch before anyone noticed?  
This is where data sovereignty and access scoping stop being compliance line items and start functioning as containment strategy.  

These are all questions that don’t need to slow down the adoption of agentic AI. They need direction on how and where autonomy is of value to their organization and how to open up to exposure. This suggests building the orchestration layer on that separation, rather than adding governance after a production incident forces the question.  

The real competitive advantage  

Gartner’s 40% cancellation forecast isn’t really a warning about AI capability. It’s a forecast about organizational discipline. Right now, agentic AI sits at what Gartner defines as the “peak of inflated expectations,” and there’s a fairly straightforward explanation. Enterprises spent 2024 and 2025 optimizing almost entirely for autonomy. Now they’re paying down the governance debt that approach accumulated.  

The winning position by 2027 won’t belong to whoever deployed the most autonomous agents fastest. It will belong to whoever built agent systems trustworthy enough that risk, compliance, and legal teams stopped being the bottleneck. The architecture answered their questions before anyone had to ask them.  

That’s a different design brief than most agentic AI roadmaps were written against. It’s about making scoped autonomy, checkpointed decisions, full traceability, and data sovereignty integral to the architecture from the start, not add-ons after a pilot project has been a success.

Midhula Mariyam Jeevan is a content writer specializing in AI, enterprise technology, software engineering, and SEO.

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LG Energy Solution says the unsolved problem in solid-state batteries is making large cells at scale, and expects the technology to reach smartphones roughly a decade before electric vehicles. Europe’s first solid-state gigafactory broke ground in France in February.

The company that has just opened America’s newest battery plant does not think solid-state cells are close for cars. “The problem with solid state is large-scale production,” said Robert Lee, North America president of LG Energy Solution, at a media roundtable at its new Lansing factory.

The difficulty scales with the cell. Small formats reach good energy density, Lee said, but “if you’re making very large form factors, most companies are struggling.”

His ordering of the market is the blunt part. Solid-state will appear in smartphones “probably a decade before you would see it in EVs,” with specialised applications arriving before either cars or grid storage.

Europe is building on the opposite assumption. ProLogium broke ground in February on a 12 GWh solid-state plant in Dunkirk, and a Dutch startup won public money to develop a factory of its own.

European carmakers are further along in testing than in production. Mercedes ran a prototype EQS almost 750 miles on cells from Factorial Energy, BMW has put a Solid Power pack in an i7, and Stellantis is testing a pack in a Dodge Charger Daytona.

Two of those three are semi-solid rather than fully solid. That distinction is precisely the gap Lee is describing, and it runs through most of the promised launch dates in this field.

China is moving faster on paper. BYD, CATL, Geely and others are targeting pilot production of solid-state cells in 2027, with wider deployment hoped for by the end of the decade.

LG’s answer is to improve what already works. It is developing lithium manganese rich cells with General Motors that use far less nickel and cobalt, promising more than 400 miles of range from 2028 at roughly the cost of cheaper lithium iron phosphate packs.

It is also hedging across chemistries. The company is preparing 46-series cylindrical cells at its Arizona plant and running a sodium-ion pilot for storage, in a market where CATL commercialised sodium first.

For Europe the timing question matters more than the technology. The continent lost its own champion when Northvolt collapsed, and the plants now being built here belong to Taiwanese, Korean and Chinese companies.

Which makes Lee’s caution worth reading twice. If large-format solid-state cells are a decade out, Europe’s route back into batteries runs through incremental lithium-ion improvements it does not currently manufacture at scale.

• Army AI agents now perform specialized cyber work under human supervision
• Human commanders still decide which operational risks machines can handle
• AI agents train for cyber roles using standards similar to personnel

The US Army is training AI agents to work alongside human forces in defined cyber roles, assigning specialized responsibilities within operational units.

According to Army Cyber Command (ARCYBER), these systems are being prepared to perform technical duties alongside personnel, while humans retain responsibility for decisions involving risk.

Following a Bloomberg report that the Department of Justice is investigating venture firm Andreessen Horowitz for holding board seats with rival AI companies, VCs told TechCrunch that they were baffled.

On the latest episode of the Equity podcast, Kirsten Korosec, Sean O’Kane, and I were similarly puzzled by the news. Yes, VC conflicts-of-interest are worth taking seriously, but as Kirsten wondered, “Of all the things that the DOJ would focus on in terms of level of importance, why does this one rise to the top?”

The news was also surprising because of a16z’s ties with the Trump administration, and because the firm has been so quiet about the investigation. It’s a real contrast with the firm’s activity during the Biden years, when — as Sean put it — “every little policy change, especially related to crypto, generated a day’s worth of posting.”

But Sean also speculated that by targeting a16z, the DOJ might also be setting an “example” that “smaller firms would follow.”

Keep reading for a preview of our conversation, edited for length and clarity.

Kirsten Korosec: I think that you bring some interesting perspective to this, Anthony, because you actually worked at a VC firm, whereas Sean and I have never been inside the confines of the VC world. We’ve only reported on it.

Anthony Ha: Long-time equity listeners will know that I did spend a couple years working at an early stage VC firm — a much smaller firm than Andreessen Horowitz, as different as you can be while still technically being in the same industry.

It did color my response when I was reading this article, which is essentially about these board seats that Andreessen Horowitz holds, and the idea that they have board seats in some companies that have become competitors.

It was really surprising to me, as apparently it was to a number of other VCs. And again, I’m not a lawyer, I was not on the legal team of this VC firm. There is, obviously, an understanding, and apparently laws, around the idea that you should not be on boards of competing startups. But this is not something that is, generally, enforced very closely. 

Founders don’t feel great if you are on the board of their biggest competitor. But also, startups evolve. And I think that was true in this case — you invest in a startup doing one thing, and then, say, the AI boom happens, and suddenly they’re doing something completely different. So the idea that you would have a year-long DOJ investigation into this — we don’t really know what the results are, there’s a lot of questions about this, but it just seems very strange.

Kirsten: In this case, Ben Horowitz sits on the Databricks board. Partner Martin Casado sits on Fivetran board. And to your point, especially in this AI-driven boom cycle, a lot of companies are changing what they’re doing, and either to jump into the AI space or to take advantage of specific subcategories within it. And a company the size of Andreessen, which makes so many investments and is on a lot of board seats, you can see how this would happen. 

To me, what was interesting — and Sean, I wonder if you have an opinion on this — is of all the things that the DOJ would focus on in terms of level of importance, why does this one rise to the top? 

Sean O’Kane: I’m just excited we’ve got another chance for disruption here. I mean, we could create a startup that is just going to put an AI on your board, and there are no conflicts of interest. Let’s do it. 

No, you’re right. It’s weird for a number of reasons, and when I first saw the headline, it certainly piqued my interest because I wanted to know what it was that they were looking at.

We should back up just a real quick second. Bloomberg reported that this [investigation] has been going on for nearly a year, so this started under the Trump administration. The people who run Andreessen Horowitz are very friendly with the Trump administration, involved in the Trump administration in some ways, sitting on some councils and things like that.

And so, for as much rhetoric as the [second] Trump administration was putting out there when it was still incoming, about being antitrust forward and fighting against the big forces of consolidation or whatever, that really hasn’t borne out. I mean, they settled with Live Nation. They didn’t break up Ticketmaster. We could spend all day talking about how that was a pretty hollow promise.

The Justice Department should not be a tool for the president to just direct at enemies or friends or whoever. But it’s just interesting that this is a bridge they were willing to cross, knowing how close they are with this administration.

And I’ve reported on some things that Andreessen Horowitz have done that made me wonder if that was going to be part of this. Two years ago, we published a story about how Ben Horowitz was personally making connections between the startups that Andreessen Horowitz backs and [the] local police department in Las Vegas, and things that have maybe more of an anti-competitive flavor to it than this — which, like you’ve said, is maybe a bit more by chance because of the way that these companies evolved and wound up competing against each other.

[It] certainly doesn’t seem like it’s something that Andreessen Horowitz feels [is] such an overreach that we have seen them complaining about it on Twitter, like they were during the Biden era — where every little policy change, especially related to crypto, generated a day’s worth of posting. We haven’t really seen that here. So maybe it’s all going to be copacetic in the end.

Anthony: To Sean’s point about the DOJ, without getting too deeply into the politics of it, I think anyone who’s been reading the news knows there’s a lot of turmoil there. There’s a lot of questions about sort of how politicized the office has or hasn’t become. Which, again, just makes it very surprising and strange that there has been this long investigation into an ostensible ally of the Trump administration.

The last thing I wanted to say for now is also just the length of the investigation is very surprising. If it was just about, “Well, we don’t think you should be on these two board seats,” there’s not that much to investigate there. You [would] just tell them, “Don’t do that,” or, “Here’s what we need you to do instead.” [Yes, I may be a little naive about how DOJ investigations work.]

So, what that suggests [is that] there must be a lot that we don’t know.  There has to be some other, more serious allegation of something going on. Otherwise, what is there to investigate for that long?

Kirsten: Well, I think that you can have two truths. Meaning, you can have a slow, arduous process because of an inefficient DOJ, and also, you can have some sort of smoking gun or bigger issue. We don’t want to get too speculative and make assumptions. But I do think that their reaction and how quiet they’ve been — perhaps they’re listening to their lawyers, perhaps that there is something a little bit bigger here.

My question is: How does this now affect other VC firms? Are they taking notice, or is this considered a weird outlier, and they’re just going to proceed as normal and continue to take board seats and not worry too much about potential competition between boards down the line?

Sean: I think that’s a good question. If this is really something that is such a concern inside the antitrust division of the DOJ, then maybe this is a way for them to … how do I best put it? Set the example that then those smaller firms would follow. Instead of, if you perceive this as [an] antitrust violation, going after all these other, smaller firms for doing something like this, you go after Andreessen Horowitz and you set the example. And then maybe that puts these other people in more of a wary, cautious position, and so I could see that being the case. 

The other thing to think about here is, this administration and the agencies that work for it and next to it have really backed off on public company prosecution in very explicit ways. The SEC and the DOJ have both said that public company investigations and things like that are just not a priority. They would rather go after individuals. 

Obviously, we’re talking about Andreessen Horowitz as a company here, but it just makes me wonder if this is, in some ways, some sort of side effect of how the priorities have shifted. If you’re not going after corporate prosecutions — you’re backing off on guilty pleas that Boeing has entered, and again, settling with Live Nation and really backing off some of those Fortune 500-type companies — maybe one of the side effects is, you wind up paying a bit more attention to stuff like this.

Nvidia’s largest customers have been notified that servers containing its AI chips will rise in price by more than 15% in many cases from early next year, driven by memory costs. The company reports quarterly earnings next week.

Nvidia’s biggest customers have been told what the memory shortage is going to cost them. Servers containing its AI chips will rise in price by more than 15% in many cases, on systems shipping from early next year.

The increases reach the newest hardware. Systems built around the Vera Rubin and Grace Blackwell chips are affected, with the size of the rise depending on the chip generation and the memory configuration.

The warnings came through the middlemen. Companies that assemble servers under contract for operators including Microsoft, Google and Oracle have notified their customers, and Nvidia did not respond to requests for comment.

What makes this notable is who is passing on the cost. Nvidia holds a gross margin of about 75% and is the most valuable listed company in the world, and it is still not absorbing this.

The leverage sits with three companies. Samsung, SK hynix and Micron produce most of the world’s DRAM, and while output is rising it has not caught up with demand from AI infrastructure.

Consumers met this first. Apple and Qualcomm have both said component costs are pushing prices up, Nvidia raised its gaming card prices this month and AMD followed within days, while Amazon Web Services has already put GPU prices up 20%.

Europe’s exposure runs through public money as much as private. The EU has committed around €20bn to a set of AI gigafactories, and a French consortium has bid $10bn for one site.

Those bids and budgets were built on last year’s hardware prices. A 15% increase on the servers such a facility exists to buy is a material change to a plan drawn up before the memory market tightened.

Commercial European operators are buying into the same market. Nebius is tripling Nvidia capacity at its Finnish data centre, on economics that have just moved.

The build-out was already getting harder. Project delays, labour shortages, tighter capital markets and local opposition have complicated data centre plans, and this adds cost to all of it.

Nvidia reports quarterly earnings next week, which is when the useful question gets asked. Not whether demand is holding, but who ends up paying for memory, because at the moment the answer appears to be everyone downstream.

People who used Grok Lite this week were no doubt surprised when the bot started sending long, large paragraphs of total gibberish. This is only slightly worse than its usual output, but it still had people in a tizzy, and understandably so.

The trouble started earlier in the week, with multiple people posting on Reddit that Grok had “lost its mind,” meaning the chatbot was acting strangely. Though the bot would respond normally at first, it would devolve into buggy, nonsensical rants. The problem appeared to only impact Grok’s website interface, not mobile apps.

In response to a perfectly reasonable question, Grok responded: “Some is local but is add, and safety font but proper if research use glue for already.” Even the basic autocorrect in my web browser gets angry at that sentence.

Users reached out to Grok directly to ask what the problem was. The chatbot responded on X that the “pure word salad is a rare temporary generation glitch” and that users could fix the problem by refreshing their browser tab or starting a fresh chat. That solution appears to have worked for most people.

Instances of the glitched responses seem to have subsided as of Friday, and we were unable to replicate the problem.

LLMs are machines, and they break as machines do

The cause of the glitch remains a mystery, as xAI hasn’t released any technical details. While it’s amusing to make fun of Grok for its mediocre performance, dwindling user base and rock-bottom satisfaction ratings, the fact is that it’s little more than computer components and code, and sometimes those things just break. 

ChatGPT had a similar issue in 2024 as did Gemini, where users were getting incomprehensible feedback to mundane questions and tasks. Claude had its own bout of silliness in 2025, though it seemed to be limited to people who used it for coding purposes.

Ultimately, all large language models, or LLMs, are word-guessing games that work similarly. Chatbots use complex mathematical formulas to predict the most likely next words in their responses. When this process breaks down, gobbledygook happens. Such a breakdown can occur at any point, whether due to a broken code or a math glitch. 

AI companies tend to keep these issues close to the vest, and it appears xAI is doing the same with Grok. So, it’s not you. It’s it.

The Pixel Watch 5 is here, but it probably shouldn’t be – just yet, anyway.

Google’s latest smartwatch has finally launched in the UK, US and beyond, bringing with it a familiar design, familiar hardware and the promise of several genuinely useful new features. 

The problem? Most of those features aren’t actually available yet, making the Pixel Watch 5 feel like a placeholder for the smartwatch Google actually wants to – and will – sell in a few months’ time. 

The Pixel Watch 5 is here, but most new features aren’t

The Google Pixel Watch 5 is finally available to buy in the UK, US and most other regions around the world – but it’s not really ready yet. 

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At the Made By Google event earlier this month, the company outlined key new features coming to the wearable. These include sleep breathing quality monitoring, which helps track your breathing quality while you sleep, improved exercise tracking with support for weight workout plans and HIIT, blood pressure and insulin trends, and even the ability for the watch to detect whether you’ve stopped breathing and call emergency services on your behalf.

These are all more important than usual because, in terms of hardware, the Pixel Watch 5 is almost identical to the Pixel Watch 4. It has the same design, the same weight and thickness, the same screen with the same peak brightness and even the same battery. 

The only tangible difference in hardware comes from the processor itself – but even the jump from the Watch 4’s Snapdragon W5 Gen 2 to the Watch 5’s Snapdragon W5 Gen 2 Accelerated isn’t that dramatic. 

Google itself only claims around 20% improved performance year-over-year, and even with the accelerated version under the hood, it’s still not quite as powerful as the Snapdragon Wear Elite found in the similarly priced Samsung Galaxy Watch 9. 

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What I’m trying to say is that Google is relying entirely on software smarts to entice fans to the Pixel Watch 5 instead of the near-identical, and much cheaper, year-old Pixel Watch 4 – which makes the fact that most of the features are MIA at launch all the harder to accept. 

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A staggered, rather confusing rollout

Rather than booting up your new Pixel Watch 5 and having all features available to you, it depends on whether it’s available – and even if it is, it might not be available in your territory.

Take one of the headline features of the Watch 5 as an example. The breathing emergency detection feature is available at launch in select countries in the EU, including France, Spain, Germany and others, along with the UK – but not in the US, as it didn’t get regulatory approval in time for the wearable’s launch. As such, there’s no hard timeline for when the feature will become available to those in the States.

Then there are features like the improved weight workout plan support and sleep breathing quality monitoring that are on the way to all users, but not until sometime in September. It’s a similar story with blood pressure and insulin trends; the feature will roll out in September, but as it needs a full month of data, the first reports won’t arrive until the end of October. 

And without most of those key features on the watch from day one, it feels almost identical to the Pixel Watch 4 experience – except it now costs more money, because, of course it does. 

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A hard pill for Google’s biggest fans to swallow

It makes my job as a tech reviewer all the more difficult, as I can only evaluate what’s here today, not what might appear a little further down the road. 

It’s not like we can take companies’ promises at face value either; Apple promised AI smarts with the iPhone 16, including a redesigned Siri experience, but the latter is only launching in the next few weeks, likely alongside the iPhone 18 Pro, two years later. And we wouldn’t be doing our jobs properly if we recommended products based on features we’ve not seen or tested, which in part contributed to me giving the Pixel Watch 5 four stars instead of something closer to five. 

But really, it’s the die-hard Google fans who rush out to pre-order Google’s latest tech that really get the worst deal. These people could wait a few months for the prices to start dropping, but no, they want the best Pixel Watch experience possible, as soon as possible, and they’re willing to drop money before even reviews are available to guarantee that. 

And after all that, they’ll boot up the Watch and struggle to spot anything that’s actually new – and it’s because right now, there just isn’t. For all intents and purposes, it’s the same Pixel Watch experience we’ve seen for a year, just with a few minor UI changes and promises that things will get better in the coming months. 

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Well, if that really is the case, why not just delay the Pixel Watch by a month or two until it’s actually ready?

Just wait until it’s actually ready to go

I can’t help but feel that Google would’ve had a much warmer reception to its Pixel Watch 5 if it had simply delayed it by a month or two until all the new features were ready, present and accounted for. 

It’s not like Google hasn’t had long gaps between announcement and release before; the Pixel Watch 4 was announced in August 2025, but didn’t materialise on shelves until two months later, in October 2025. 

Of course, we all know the real reason why. It’s the same reason why it shifted from its original October release schedule to August: to get ahead of Apple’s yearly iPhone launch, which takes place in early September. 

If Google delayed the Pixel Watch 5 until October, it’d risk it essentially getting forgotten about in the noise of whatever Apple is set to launch in September, not only in terms of wearables like the Apple Watch Series 12, but also in general excitement around the next-gen iPhones – especially if the long-rumoured foldable materialises, as expected. 

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So, instead, Google has stuck to its guns to cash in on early interest, even though that has seemingly come at the expense of early adopters who now have to wait a few months before really getting the Pixel Watch 5 experience promised at launch. Bad move there, guys.