Honor has unveiled its mid-range 600 series in Malaysia, and we’re keen to see how the specs measure up to its competitors ahead of its UK launch.

We’ve compared the Honor 600’s specs to the four-star Google Pixel 10a, and highlighted the key differences between the two handsets below.

We’ll be sure to update this versus once we review the Honor 600. In the meantime, visit our list of the best Android phones and best mid-range phones to find your next investment.

Price and Availability

At the time of writing, the Honor 600 and Honor 600 Pro are only available to buy in Malaysia. While they will eventually launch in the UK and Europe, Honor is yet to reveal the RRP for the series.

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Having launched earlier this year, the Pixel 10a is available to buy now and has a starting RRP of £499/$499. 

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Snapdragon 7 Gen 4 vs Tensor G4

Powering the Honor 600 is Qualcomm’s Snapdragon 7 Gen 4 chip, the same processor that’s behind the Nothing Phone 4a Pro. We found that the Phone 4a Pro was able to handle everyday, casual use-cases with enough speed and responsiveness for most users, while less-demanding games can be played reliably too. With this in mind, we’d expect a similar performance with the Honor 600, though we’ll have to wait until we get our hands on the phone to confirm this.

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However, it’s worth noting that the Snapdragon 7 Gen 4 is a mid-range chip and can’t compete with the likes of Snapdragon 8 Elite Gen 5. In addition, during its benchmark tests the Nothing Phone 4a Pro couldn’t quite reach the results of the Pixel 10a.

Speaking of which, the Pixel 10a runs on Google’s 2025 Tensor G4 chip – the same as the Pixel 9a and rest of the 2025 Pixel 9 series. While it’s a shame Google didn’t fit its budget-friendly handset with the newer Tensor G5 processor, G4 is still perfectly capable and can handle just about anything you can throw at it with ease. 

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While Tensor G4 doesn’t quite measure up to Qualcomm’s 2025 flagship, Snapdragon 8 Elite, it’s still fast and smooth in everyday use and can handle basic gaming too.

Honor 600’s bezel are more narrow

One of our biggest issues with the Pixel 10a’s design is its thick bezel. Sure, they’re slimmer than the ridiculously large Pixel 9a’s, but overall the bezel makes the handset look more dated than many of the best Android phones.

With this in mind, Honor’s promise that the 600 series boasts the “narrowest black bezel on the market” all the more impressive. At just 0.98mm, the Honor 600’s bezel is near-on invisible and should help the handset feel more premium as a result.

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Honor 600 has a larger battery and supports faster charging

Unsurprisingly, the Honor 600 is equipped with a significantly larger battery and faster charging compared to the Pixel 10a. While the Pixel 10a’s cell is pretty average at 5100mAh – and larger than the premium Samsung Galaxy S26 Ultra – the Honor 600’s battery is 7000mAh instead. Even so, it’s worth noting that we found the Pixel 10a’s battery life to be solid, and saw us comfortably through a day’s use before conking out. 

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Honor promises that the 600 should also offer a full-day of battery life alongside five years battery health protection too.

When it does come time to recharge, the Pixel 10a supports 45W wired and 10W wireless speeds whereas the Honor 600 boasts support for 80W wired speeds. While Honor has disclosed that the 600 can support 27W reverse charging, its exact wireless speeds are still at large.

Honor 600 has a 200MP main camera

Both handsets are equipped with two rear lenses: a main and an ultrawide. However, the Honor 600 sports a whopping 200MP main lens while the Pixel 10a is fitted with a 48MP main instead. 

Although the difference may seem pretty hefty, we should note that the Pixel 10a is a brilliant camera phone, especially when you consider its price tag. We found that pictures are detailed with true-to-life colours, while the lenses can handle even complex lighting conditions with ease.

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In comparison, Honor promises the main lens offers an “industry-leading” low-light performance, true-to-life authentic colour reproduction and AI enhanced night photography too. However, as we’re yet to review the Honor 600, we’ll have to wait and see how its camera fares.

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Perhaps one of the key reasons to opt for a Pixel phone is its plethora of AI-powered features. Alongside the likes of Circle to Search, Live Translate and Call Assist, there’s built-in Gemini and Google’s Photo Editing tools too.

While the Honor 600 isn’t quite as equipped, that’s not to say there aren’t AI tools to play around with – including Gemini. In fact, one of Honor’s headline features is AI Image to Video 2.0 which allows users to turn up to three images and prompts into a video.

Early Verdict

It’s difficult to give even an early verdict as we don’t know how much the Honor 600 will cost in the UK. However, with a 200MP main lens, a near-invisible bezel and mighty battery, the Honor 600 is undoubtedly a promising Android phone.

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On the other hand, the Pixel 10a is one of the best mid-range phones you can get your hands on, thanks to its solid and reliable camera set-up, all-day battery life and plethora of AI tools.

We’ll be sure to update this versus once we review the Honor 600.

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The Shiller cyclically adjusted price-to-earnings ratio for the S&P 500 stands at approximately 38 to 40, depending on the day you check. In 155 years of recorded data, the CAPE has been higher exactly once: March 2000, when it reached 44.19, one month before the Nasdaq began a decline that would erase 78% of its value over the following two and a half years. The ten largest companies in the S&P 500 now account for 36% to 40% of the index’s total market capitalisation, nearly 50% above the dot-com peak concentration of roughly 27%. Deutsche Bank’s latest fund manager survey found that 57% of institutional investors now identify an AI valuation crash as the single greatest risk to markets. Jeremy Grantham, the co-founder of GMO who correctly called the dot-com and housing bubbles, has said there is “slim to none” chance the current AI rally does not end in a bust. These are the numbers that make the comparison to 2000 feel inevitable. They are also, by themselves, incomplete.

The case for alarm

The structural parallels between the current AI equity rally and the dot-com bubble are not superficial. They are mechanical. Market concentration has exceeded dot-com levels by a wide margin. The Nasdaq-100’s performance is dominated by a handful of companies whose valuations are predicated on AI revenue growth that has not yet fully materialised at the scale the market is pricing. Hyperscaler capital expenditure, the combined infrastructure spending of Microsoft, Google, Amazon, and Meta, is approaching $660 billion to $690 billion in 2026, a figure that represents the largest corporate investment programme in history outside of wartime mobilisation. That spending is being funded, in part, by converting human labour into AI infrastructure: Meta and Microsoft collectively cut up to 23,000 jobs while simultaneously committing to record capital expenditure, a direct transfer from payroll to data centre construction.

Bank of America’s Savita Subramanian has set a year-end S&P 500 target of 7,100, with a bear case of 5,500, and expects multiple compression as earnings growth slows in the second half of 2026. The Motley Fool identified four factors it associates with bubble conditions: retail investor euphoria, speculative capital concentration, decoupling of valuations from fundamentals, and a narrative so compelling that scepticism feels intellectually disreputable. All four are present. OpenAI’s $852 billion valuation prices a company that has never earned a profit at roughly double the market capitalisation of Coca-Cola, a company that has earned profits continuously since the 1890s. Accel’s $5 billion AI-focused fund, the largest in venture capital history, exemplifies the capital flooding into AI at the private market level. The public and private markets are reinforcing each other: venture-backed AI companies raise at extraordinary valuations, public AI companies spend at extraordinary rates to stay ahead of them, and the cycle pushes both valuations and capital expenditure higher.

The case for calm

The most important difference between 2000 and 2026 is profitability. At the dot-com peak, the technology companies driving the market were, in aggregate, destroying capital. Cisco traded at 200 times earnings. Pets.com had no earnings. The entire thesis rested on future revenue from an internet economy that, while real, was years from generating the cash flows the market was discounting. In 2026, the companies driving the AI rally are among the most profitable in corporate history. Nvidia reported net income exceeding $120 billion for fiscal 2026. Its forward price-to-earnings ratio is approximately 41, elevated but not in the same postcode as Cisco at 200. The technology sector’s aggregate forward P/E is roughly 30, compared with 50 at the dot-com peak. Apple, Microsoft, Alphabet, Amazon, and Meta generated a combined $350 billion in free cash flow in their most recent fiscal years. These are not speculative enterprises burning venture capital. They are cash-generating machines that have chosen to reinvest at historically unusual rates.

Capital Economics analyst John Higgins has made the most nuanced version of this argument. He distinguishes between a “stock bubble” and a “fundamental bubble.” The stock bubble, in his analysis, may already be deflating: the Nasdaq-100 corrected more than 10% from its February 2026 highs before recovering on trade deal optimism and strong earnings. But the fundamental bubble, the one built on actual earnings growth, is still expanding. Nasdaq-100 earnings grew 19% year over year in the most recent quarter. As long as AI-related revenue continues growing at that pace, the earnings justify elevated multiples. The bubble pops not when P/E ratios are high, but when the “E” stops growing. JPMorgan has suggested the S&P 500 could reach 8,000 if earnings momentum continues. Goldman Sachs sees a multi-year AI “supercycle.” The bull case is not that valuations are reasonable. It is that earnings growth will make today’s prices look reasonable in retrospect, the same argument that was wrong about Cisco in 2000 and right about Amazon.

The capex question

The variable that will determine which analogy holds is capital expenditure returns. Hyperscalers are spending $660 billion to $690 billion this year building AI infrastructure. Meta’s $27 billion deal with Nebius for AI cloud capacity is one transaction among dozens, each individually larger than most companies’ entire capital budgets. The question is not whether this infrastructure will be used. It almost certainly will. The question is whether it will generate returns that justify the investment at the price paid. The fibre-optic cables laid in 1999 carry today’s internet. The companies that laid them went bankrupt. The technology was correct. The financial model was not.

There are structural reasons to believe the AI capex cycle is better supported than the fibre-optic buildout. Cloud computing operates on a consumption model where customers pay for usage, providing revenue visibility that speculative fibre networks lacked. The hyperscalers building the infrastructure are also the primary consumers of it, reducing the demand uncertainty that destroyed independent fibre companies. Oracle’s $553 billion in remaining performance obligations, Microsoft’s Azure backlog, and Amazon’s AWS contract pipeline all represent committed future revenue. But committed revenue is not collected revenue, and the concentration of AI demand in a small number of large model developers and enterprise customers creates fragility. If OpenAI, the anchor tenant of Oracle’s Stargate project, were to experience financial difficulty, the ripple effect through the infrastructure financing chain would be severe. If enterprise AI adoption plateaus at the “copilot” stage without progressing to the autonomous agent workflows that justify the next order of magnitude in compute spending, the return on $660 billion in annual capex would fall below the cost of capital.

The verdict the market cannot reach

Both sides of the debate are correct, which is what makes the current moment so difficult to navigate. The bears are right that market concentration, CAPE ratios, and speculative euphoria have reached or exceeded dot-com levels. The bulls are right that the underlying companies are profitable in ways their dot-com predecessors were not. The resolution depends on a variable that neither side can observe directly: the long-term return on the hundreds of billions being invested in AI infrastructure this year. If those returns materialise, the current valuations will be seen as fair prices paid early for a genuine technological transformation. If they do not, the CAPE chart will add a second peak to match the one from March 2000, and the comparisons that feel alarmist today will feel prescient.

The Federal Reserve’s benchmark rate sits at 3.50% to 3.75%, providing less of a cushion than the near-zero rates that inflated asset prices between 2020 and 2022 but not the restrictive rates that typically trigger corrections. Section 122 tariffs of 10% to 15% on a range of imports expire on July 24, 2026, and their renewal or escalation will affect corporate earnings forecasts and consumer spending. The trajectory that brought technology markets to this point, a year of accelerating AI investment, record venture funding, and corporate restructuring around artificial intelligence, has created conditions that resemble a late-stage expansion more than an early-stage bubble. Late-stage expansions can last longer than sceptics expect. They also end more abruptly than optimists imagine. The honest answer to whether AI stocks are in a bubble is that the question cannot be answered until the capex cycle produces results, and the capex cycle has barely begun. Grantham is betting it ends badly. Goldman is betting it does not. The market is pricing in both possibilities simultaneously, which is why it has been volatile in both directions, and will remain so until the revenue either arrives or does not.

Smartphones like the Oppo Find X9 Ultra prove that a slightly thicker phone isn’t a flaw – it’s the reason it’s actually good.

For the better part of a decade, the smartphone industry has been chasing thinness like it’s the ultimate sign of progress. Every launch cycle brings another round of applause for shaving off a fraction of a millimetre, as if that alone makes a phone better. 

But the truth is, thin phones aren’t improving the experience any more. In many cases, like with the Samsung Galaxy S25 Edge and iPhone Air, they’re making it worse.

After spending the past few weeks using the Oppo Find X9 Ultra – a phone that is unapologetically thick, weighty and, by modern standards, a bit of a brick – it’s not hard to question why we’re still pretending thinness is the goal.

The trade-off we keep ignoring

At 9.1mm thick and 236g, the Find X9 Ultra is not trying to win any design awards for slimness. Put it next to something like an iPhone Air and it feels noticeably chunkier. But that extra bulk isn’t wasted space – it’s there for a very good reason.

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It’s the reason why the phone packs a 7050mAh battery that comfortably sails through a full day – and then some – without anxiety. It’s the reason there’s room for a genuinely versatile camera system, with large sensors across multiple focal lengths instead of one standout lens and a couple of token extras.

It’s even part of why the device feels more durable in the hand, especially with materials that prioritise grip over gloss.

In other words, the thickness is the feature. 

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Thinness for whose benefit?

The push for ever-slimmer phones made sense once upon a time. Early smartphones were bulky, awkward and pretty uncomfortable to use over longer periods of time. But we crossed that threshold years ago. Today’s ‘thick’ phone would have been considered impressively slim not all that long ago.

So, what is this all for?

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It’s certainly not for battery life, that’s for sure, with ultra-thin phones struggling to last all day with even light use. Users, whether commuting, travelling, or just dealing with patchy signal in rural areas, benefit far more from endurance than from shaving off another 0.8mm.

It’s not for camera performance either, where as much as manufacturers hate to admit it, physics still play a massive role. Bigger sensors and better optics need space, and there’s nothing you can do to change that fact.  

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The reality is that thinness has become a bit of a technical flex for companies, and something they can tout on a spec sheet, rather than actually being useful for consumers. 

The illusion of premium

There’s also a perception problem here. Thin phones are often marketed as more premium, as if engineering restraint is superior to capability – but that idea is starting to feel outdated.

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What actually feels premium in 2026 isn’t how thin a phone is, it’s how little you have to think about it. It’s about not worrying about your battery before heading out for the day, or second-guessing whether the zoom lens will hold up. 

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And, by that logic, slightly thicker phones like that Find X9 Ultra that solve these problems are far more premium than wafer-thin phones that introduce them. 

A shift that needs to happen

The Oppo Find X9 Ultra isn’t perfect; it’s big, it’s heavy, and yes, some people will prefer something lighter and thinner. But it makes a compelling argument that we’ve been optimising for the wrong thing.

Instead of asking “how thin can we make this?”, manufacturers should be asking “what do we gain if we stop trying?” Because right now, the answer is quite a lot; better battery life, better cameras, better durability and, ultimately, a better all-round experience.

Thin phones aren’t inherently bad – I really enjoyed using the S25 Edge – but the industry’s obsession with them is. And if devices like the Find X9 Ultra are anything to go by, it might finally be time to move on. 

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Intelligence operatives have used some innovative techniques to snoop on information without ever having to step foot inside a target building. A simple approach that involves lasers has been around for eons, and transforms a standard window into a makeshift bug.

The Soviets got onto this kind of thing way back in the 1940s. They dubbed it Buran, and it basically shined an invisible infrared beam onto embassy windows in Moscow, detecting minuscule movements made by voices within. American agencies then took up on this idea and refined it using actual lasers, which produced far crisper results and had a much longer range.

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A laser microphone works by converting sound to light and then bouncing it back, causing the very small area it strikes to vibrate. These minuscule movements are invisible, but they exist and are very little, only a fraction of the thickness of a sheet of paper. These vibrations then alter the distance the beam needs to travel on its return voyage. A person with a receiver nearby collects the reflected light and transmits it via an optical system that converts changes in path length into brightness fluctuations. The variations are then converted into an electrical signal that corresponds to the original audio you heard.

Typically, you’d set everything up from the luxury of a parked automobile or an office building nearby. You’d aim the beam in at an angle so that the reflection landed perfectly on the sensor. The infrared versions are undetectable to anyone gazing out the window, but by carefully filtering out noise from traffic and wind, you may get a rather clear picture.

If DIY projects are your thing, you can actually create a working version using parts that can be purchased online. One recent project used a basic red laser pointer, a photodiode to catch the returning light, and a small amplifier circuit wired to a computer It took some care to get the alignment just right, but once he did, the reflection was slightly off center on the sensor for the greatest results. When music was played inside a sealed test box, the recovered audio came through loud and clear, despite some static in the background caused by equipment vibration.

Governments, on the other hand, just achieve a much clearer sound by stabilizing the beam and employing some quite clever signal processing. Sometimes the places they’re seeking to listen in on will try to outsmart them by hanging thick curtains or installing little motors that provide random vibrations to drown out the real voice. Nonetheless, this method leaves no wires or bugs behind and completes the task from a safe distance.
[Source]

Snabbit, an Indian instant house-help startup, is close to raising fresh funding at a valuation of around $400 million in a round led by Susquehanna Venture Capital, TechCrunch has learned.

The Bengaluru-based startup is in talks to raise around $50 million in the round, according to three people with knowledge of the deal. One of the people added that the round could be around $55 million or higher, as strong investor demand may prompt the company to raise more than initially planned.

The round is expected to include participation from Mirae Asset, FJ Labs, and existing investors including Lightspeed Venture Partners and Bertelsmann India Investments, the people told TechCrunch. It would mark a significant jump from the $180 million valuation at which Snabbit raised $30 million in October 2025. The deal could be announced as early as next week.

Founded in 2024, Snabbit connects households with on-demand domestic help for cleaning, dishwashing, laundry, and other chores, offering quick turnaround times through a managed network of workers. Before this round, the startup had raised $55 million in total funding.

The fundraise comes amid growing investor interest in instant house help startups in India. Rival Pronto is finalizing a funding round led by tech investor Lachy Groom at about a $200 million valuation. Urban Company, a top player in this space, said its instant home services offering crossed one million bookings in March.

The rising demand is partly driven by India’s young, urban workforce, which has grown accustomed to ordering services such as groceries on demand through apps.

Snabbit founder and CEO Aayush Agarwal said in a recent LinkedIn post that the company had completed more than one million jobs in March alone. He had earlier told TechCrunch that it recorded over 10,000 daily jobs and more than 300,000 total orders in October.

The startup worked with about 5,000 professionals on its platform at the time, all of whom were women, he added.

Snabbit and its investors did not respond to requests for comment.