Amazon Music has never lacked catalog depth or audio quality; the harder problem has been giving listeners a reason to choose it over Spotify, Apple Music or YouTube Music once the trial period ends and the monthly charge starts feeling less hypothetical.

Alexa+ is Amazon’s latest attempt to close that gap. The company has expanded the feature inside the Amazon Music mobile app to all U.S. customers, including listeners on free and paid tiers, with support on both iOS and Android at no additional charge.

Users can type or speak conversational requests, identify songs from partial clues, ask questions about artists and recordings, create playlists, refine them through follow-up instructions and save the results directly to their libraries. Amazon is not merely improving search; it is trying to replace it with a music assistant that can understand what listeners mean when they remember everything about a song except the title, artist and half the lyrics.

Alexa+ Is More Than an AI Playlist Generator

Creating a playlist from a text prompt is no longer particularly novel. Spotify, Apple Music, YouTube Music, and Deezer already offer some variation of that feature.

Amazon is trying to make Alexa+ broader.

Listeners can ask it to identify a song from remembered lyrics, an artist, a film or television appearance, or some other incomplete clue. Amazon’s examples include finding the track used during the opening credits of The Sopranos or identifying a song from a partial lyric.

Alexa+ can also answer follow-up questions about:

• Genres and regional music scenes
• Artist influences and discographies
• Song meanings
• Samples and their origins
• Album and release history
• Chart performance
• Band membership changes
• Festival lineups
• Relationships between artists and musical styles

A listener could begin by requesting contemporary electronic music, ask for more information about one of the recommended artists, remove slower tracks, increase the tempo, and then save the finished selection.

That moves the feature closer to a conversational music guide than a conventional recommendation engine. Whether it is a knowledgeable guide or the person at the record store who confidently invents an answer rather than admitting they do not know will depend on accuracy.

Amazon has not published an accuracy rate for Alexa+ music responses, so claims about samples, credits, chart history, release dates, and song meanings should still be treated as useful starting points rather than digital liner notes carved into stone.

Alexa+ Can Build and Edit Playlists

Alexa+ can create playlists from specific combinations of mood, era, genre, geography, tempo, language, and activity.

Amazon suggests prompts such as:

• Music for driving along the Pacific Coast Highway at sunset
• French-language songs that sound appropriate for a Parisian café
• Ambient electronic music combined with light classical piano and no lyrics
• Contemporary jazz that will not overpower a dinner party
• 1990s pop featuring Madonna but excluding boy bands

The resulting playlists can be renamed, saved to an Amazon Music library, shared, and edited later by asking Alexa+ to add or remove specific tracks.

That last part is useful. Many generative playlist tools produce a list and then leave the listener to repair it manually when the algorithm decides that “quiet Sunday morning” requires Imagine Dragons.

Alexa+ allows the user to continue the conversation and adjust the result rather than starting again.

Available Across Every Amazon Music Tier

The most important competitive distinction is availability.

Amazon says Alexa+ is available to all U.S.-based Amazon Music customers across every subscription tier, at no additional cost, through the iOS and Android apps.

That does not mean Alexa+ erases the differences between Amazon Music plans.

The underlying subscription still determines which songs can be played on demand, whether advertising is included, how skipping works, and which audio-quality formats are available. Amazon Music’s paid Unlimited and Standard plans provide access to HD, Ultra HD, Dolby Atmos, and Sony 360 Reality Audio where available; adding Alexa+ to a free account does not quietly turn it into a lossless subscription.

Amazon has also not announced when the Amazon Music integration will expand beyond the United States or reach desktop and web versions of the music service.

How Amazon Music Alexa+ Compares

Spotify Offers the Closest Direct Rival

Spotify’s new Talk to Spotify feature is the closest comparison to Alexa+.

Eligible Premium users can type or speak to Spotify from the Home and Now Playing screens, request unfamiliar artists, adjust the mood or tempo, add music to the queue, save tracks, follow artists, and ask questions about songs, albums, genres, podcasts, and audiobooks. Spotify can also answer questions about the user’s listening history, including when they first heard a song or which genres they have explored recently.

It is a deeper listener-history tool than Amazon has described so far, particularly because Spotify can examine years of personal playback behavior.

The limitation is availability. Talk to Spotify is rolling out gradually in English to selected Premium users aged 18 and older in the United States, Ireland, and Sweden. Amazon is offering its music assistant to every U.S. customer, including people who are not paying for Amazon Music.

Spotify also warns that the beta remains a work in progress and that responses will not always be correct. That is unusually honest language from a technology company introducing AI and should probably be printed on the front of every device containing a microphone.

Apple Music Keeps Its AI Focus Narrower

Apple Music’s Playlist Playground creates playlists from text requests involving moods, genres, activities, and musical eras. Users can refine the results, rearrange songs, accept suggested additions, rename the playlist, and save it to their libraries.

It is available in beta to Apple Music subscribers aged 13 and older in the United States and currently works only in English.

Playlist Playground competes directly with Alexa+ for playlist generation, but Apple does not currently describe it as a broader conversational music expert. It is designed to build playlists rather than explain musical relationships, identify tracks through extended dialogue, or answer detailed questions about what is playing.

Apple’s approach is cleaner and more focused. Amazon’s is more ambitious.

Ambition is useful, provided Alexa+ does not explain that John Coltrane joined Black Sabbath in 1973.

YouTube Music Uses AI for Radio and Podcast Discovery

YouTube Music’s Ask Music allows paid YouTube Music Premium and YouTube Premium subscribers in selected countries to describe what they want to hear and generate a personalized radio station or playlist.

Google has expanded the feature to include podcast recommendations based on mood, genre, or programs the listener already enjoys.

YouTube Music has an enormous advantage when a request involves live performances, remixes, covers, rare recordings, and material that may not exist in a conventional streaming catalog.

Alexa+ appears to provide a more extensive conversational layer and more direct playlist editing, while Ask Music remains primarily focused on generating something to play.

Deezer Gives Listeners More Control Over the Algorithm

Deezer began testing Playlist with AI globally in 2024, allowing selected paying subscribers to create playlists through text prompts involving activities, decades, genres, and moods. The initial test was limited to 5% of Deezer’s paying audience.

More recently, Deezer introduced Flow Tuner, which allows users to activate, deactivate, or add genres and subgenres that influence the service’s recommendation algorithm. Deezer also excludes identified AI-generated recordings from Flow recommendations.

Flow Tuner may be less theatrical than asking a chatbot to build “music for driving through New Jersey while wondering why the Garden State Parkway is not moving,” but direct control over the recommendation engine is arguably more useful over time.

TIDAL and Qobuz Are Taking Different Paths

TIDAL continues to rely on My Mix, which creates as many as six personalized mixes from recent listening behavior and saved music. Those mixes change gradually as the service learns more about the listener.

TIDAL does not currently document a consumer-facing conversational playlist assistant comparable to Alexa+, Spotify’s new chat feature, or Apple’s Playlist Playground.

Qobuz has moved even further in the opposite direction. Its redesigned Discover section combines personalized mixes, radio stations, listening history, and recommendations with extensive human editorial content, including reviews, interviews, Essential Discography selections, and curated playlists.

Qobuz uses technology to personalize discovery but continues to make human curation part of its identity. In a market racing to replace the record-store employee with a language model, Qobuz is betting that some listeners still want advice from people who have actually heard the album.

What Makes Amazon Music Alexa+ Different?

Alexa+ combines four capabilities that competitors usually separate:

1. Natural-language music search
2. Contextual questions about artists and recordings
3. Dynamic playlist generation and editing
4. Direct playback and library control

The feature is also available across all U.S. subscription tiers rather than being reserved for paying customers or a small test group.

Amazon reports that Alexa+ users explore music three times as often as users of the original Alexa, while customers who request recommendations listen to almost 70% more music. Those are Amazon’s internal engagement figures, and the company has not disclosed the sample size, time period, or methodology behind them.

The numbers still explain why every major streaming company is investing heavily in conversational discovery. More questions lead to more recommendations, which lead to more listening, more data, and a lower chance that a subscriber remembers they are paying for four music services.

Who Is It For?

Alexa+ will be most useful for listeners who know what they want emotionally or stylistically but cannot express it through artist names and conventional search terms.

It should also appeal to people who frequently use Amazon Music through Echo products and already think of Alexa as the front door to their music library. The mobile integration allows those users to continue the same kind of interaction without standing within shouting distance of a smart speaker.

It is less compelling for listeners who prefer choosing complete albums, already maintain carefully organized libraries, or view algorithmic playlists as the musical equivalent of allowing a supermarket to plan dinner.

Audiophiles should also understand that Alexa+ is a discovery and control feature, not an audio upgrade. It does not improve the mastering, increase the bit depth, repair a poor recording, or stop the Amazon Music app from occasionally making basic navigation feel like filing a tax return.

The Bottom Line

Amazon Music Alexa+ is one of the more complete AI music assistants currently available because it goes beyond generating playlists. It can search conversationally, identify partially remembered songs, explain artists and recordings, refine selections through follow-up requests, and make direct changes to playback and the user’s library.

Making it available to every U.S. Amazon Music customer gives Amazon a wider launch audience than Spotify, Apple, YouTube Music, or Deezer currently offer for their closest comparable tools.

That does not automatically make Alexa+ the best music-discovery system. Spotify knows more about the listening history of many users, YouTube Music has access to a wider universe of rare and unofficial material, Apple’s implementation is more focused, and Qobuz still understands that human editorial judgment has value.

The larger shift is impossible to miss. Music streaming services no longer want listeners to browse catalogs or build playlists by hand. They want us to describe a mood, surrender the queue, and keep listening while the assistant learns enough about our habits to become increasingly difficult to leave.

Convenient? Absolutely.

A little unsettling? Alexa already knows what is in the shopping cart. It may as well know which album we play while regretting the purchase.

Related Reading:

from the this-time-we-could-wait-for-discovery dept

We’ve written in the past that people online often get way too excited about theoretical pending “discovery” in frivolous lawsuits filed by bad actors. Because while there are certainly a few cases where (1) a frivolous case even reaches discovery and (2) some elements of that discovery are revealed to the public, in the vast majority of cases, that doesn’t happen. The legal strategy for most defendants is to get a case thrown out before it reaches discovery because discovery is incredibly expensive. And, even then, most often what is handed over in discovery never goes public.

But… hey, sometimes, “can’t wait for discovery” turns out to be an accurate sentiment.

Last year we noted that Donald Trump had filed an obviously frivolous lawsuit against the BBC, asking for $10 billion. At issue was an edit in the documentary he didn’t like which might be considered mildly misleading (though Donald Trump repeatedly falsely claimed that the BBC used AI to fabricate quotes, the reality was they edited two separate parts of the same speech to sound like they were said together, when they were really many minutes apart). That’s not defamation, though.

Either way, the case has not been going well for Trump. Because he argued that this documentary (which was only shown once in the UK and not in the US) harmed Trump’s business interests in Florida (where he sued), the BBC asked for Trump’s financial records as part of their discovery requests. Given that Donald Trump made more money last year (around $2 billion) than ever before, even as he remains the President of the United States, it seems like a reasonable request.

Trump and his (not very bright) lawyers tried to wriggle out of this by dropping some of the initial claims that were about how much harm the documentary did to his business, saying instead that it just harmed his reputation. The BBC said it still needed his financial records anyway. And now, Magistrate Judge Enjoliqué Lett has agreed, noting in court that the financial records would be relevant to the claims of reputational harm as well.

“All of President Trump’s brand, properties and businesses are impugned or said to have been impugned. Reputational, economic damages, all of that is now at issue in this case,” Lett said at the conclusion of a three-hour hearing.

Of course, Trump’s lawyers can (and almost certainly will) ask the Article III Judge (Roy Altman, who is a Trump appointee) to overrule the magistrate, but it might not work. After all, earlier in the case, Trump’s lawyers had sought to remove Lett from the case, claiming that she was biased against him, because before she became a Magistrate Judge, she had represented a client in a case against Trump. Judge Altman rejected that claim back in May, siding with his colleague, Magistrate Judge Lett:

The Plaintiff asks us to withdraw our referral of discovery matters from Magistrate Judge Lett and reassign them to a different Magistrate Judge. … He advances two arguments in support of this request: First, he cites our unrelated referral of discovery matters in Donald J. Trump Revocable Trust et al. v. Capital One…. Second, he argues that “Magistrate Judge Lett had appeared as counsel of record on behalf of a party directly adverse to President Trump in active federal litigation: Trump v. Clinton… The Plaintiff’s first argument is unavailing. “Effective April 19, 2026,” Magistrate Judge Hernandez replaced Magistrate Judge Lett as our “paired” Magistrate Judge for Miami-based cases…. We reassigned discovery in Capital One the next day based on case workload and the parties’ compressed discovery period…. Nothing about that decision mandates a withdrawal of the referral in the different circumstances of this case. The Plaintiff next argues that Magistrate Judge Lett previously “represent[ed] [a] defendant directly adverse to President Trump.” … Despite his claim to the contrary, the Plaintiff effectively seeks Magistrate Judge Lett’s recusal. … But 28 U.S.C. § 455 is clear that: “Any justice, judge, or magistrate judge of the United States shall disqualify himself in any proceeding in which his impartiality might reasonably be questioned.” Accordingly, we’ll leave any decision regarding Magistrate Judge Lett’s recusal to her sound judgment. Signed by Judge Roy K. Altman on 5/19/2026.

So, at this point, Judge Altman seems willing to trust Magistrate Judge Lett’s judgment on the recusal question — and that deference may well carry over to the financial-records dispute too.

Of course, even if discovery does move forward, Trump could still file for a protective order to keep most of the records secret, outside of whatever has to be used in court. Alternatively, he could try to dismiss the case to get out of having to provide discovery.

Either way, this was a stupid, vexatious, obvious SLAPP suit designed to punish the BBC and waste its time and money. So it’s quite nice to see that backfiring on the censorial bully that is the President of the United States.

Filed Under: 1st amendment, defamation, documentary, donald trump, florida, panorama, slapp suit

Companies: bbc

Drivers who’ve been blinded by the lights, it’s not just your imagination. There are cars on the road with headlights that are so bright they’re illegal.

Two years after Tesla informed the National Highway Traffic Safety Administration that low-beam headlights on some Tesla EVs exceed brightness limits, the company’s move to make those headlights street-legal has failed. The NHTSA denied Tesla’s petition to avoid a recall, meaning the company will need to issue a fix for nearly 20,000 Model 3 and Model Y vehicles from 2017 to 2023 for a problem the company called “inconsequential.”

A representative for Tesla didn’t immediately respond to a request for comment.

Tesla argued in its petition that because the bright lighting in question falls outside of the field of vision of oncoming drivers, “noncompliance is inconsequential as it relates to motor vehicle safety.”

In its response, NHTSA associate administrator for enforcement Eileen Sullivan wrote, “NHTSA disagrees with Tesla’s conclusion that there is no increased risk of glare for surrounding traffic or the driver of the subject vehicle in any driving conditions.”

The agency argued, citing public comment, that the headlights can still produce glare that could affect other drivers.

The group referenced a similar decision it made in 2022 involving GM Terrain vehicles.

For Tesla owners, the NHTSA ruling means the company must provide a free fix for the affected cars.

Additional steps are possible

The NHTSA isn’t commenting on pending legislation, including its denial of Tesla’s petition, but in an email to CNET, a spokesperson said that the organization is assessing studies about LED-headlamp glare and may take additional steps based on what it finds.

“Some issues may arise from the inappropriate use of high beams, illegal aftermarket lamps, improperly maintained lamps or misaligned lamps,” the spokesperson wrote.

Extra-bright headlights are illegal, and devices that don’t comply can be outlawed from sale on platforms like Amazon, while drivers using overbright headlights can face escalating fines, the suspension of their driver’s license or civil liability.

“Although headlight technology has changed over the years, NHTSA’s lighting standard has remained constant in limiting the amount of glaring light directed toward traffic both coming and going,” the spokesperson said.

SUVs, which sit taller on the road than sedans, have become the most popular vehicles in the US. That, combined with LEDs taking over as the prime technology for headlights, has led more people to notice that high-intensity low beams seem brighter and harder to avoid.

Some modern cars have benefited from adaptive lighting technologies that can adjust brightness, direction or lighting pattern as driving conditions change, but many cars on the road aren’t equipped with these systems.

Headlights in the congressional spotlight

Earlier this year, the US House of Representatives joined the discussion about bright headlights with the introduction of a bill from Rep. Marie Gluesenkamp Perez, a Democrat from Washington state.

The LIGHT Safety Act would set maximum allowable brightness limits for low-beam headlamps on vehicles in the US. So far, the proposal has only been sent to committee, meaning it’s been assigned to a House panel for initial review, with no further action taken on it since March.

The bill seeks to revise existing federal standards by establishing a maximum allowable brightness for low beams.

U.S. Treasury secretary Scott Bessent doubled down on his warnings to Chinese AI companies on Wednesday, saying that sanctions remain on the table after a White House official accused Moonshot of improperly distilling Anthropic’s Fable model. 

Model distillation is a common AI training technique in which a smaller model learns from the outputs of a larger one. While this process can infringe on intellectual property rights, it’s also widely used as a legitimate optimization method. 

“Open source is not open season on American IP,” Bessent posted on X. “When [Chinese] firms conduct covert, industrial-scale distillation attacks that cross the line into IP theft, sanctions and Entity List designations will be on the table.”

Earlier this week, Bessent stated that the U.S. government would examine open source models from China for signs of intellectual property theft and impose sanctions if found. 

Bessent’s latest remarks come hours after the White House’s science and technology policy chief Michael Kratsios accused the China-based Moonshot of conducting large-scale distillation against U.S. models. He alleged that Moonshot had acquired Nvidia’s “GB300-equipped servers and has accessed GB300s in Thailand, likely to train its AI models,” raising questions about whether the firm violated U.S. export-control rules.

The GB300 servers are part of Nvidia’s Blackwell generation, which are banned from being sold to Chinese companies.  

Some experts dispute the idea that Kimi K3 could have been developed primarily through distillation from Fable, which has only been publicly available since July 1. Moonshot released K3 last week as an open-weight model, and its advanced capabilities have called into question the underlying business models of leading U.S. AI labs, casting doubt on whether they can continue to justify the enormous capital requirements underpinning the frontier AI race. 

The episode has also intensified a broader debate in Washington over the influx of Chinese open models. Some, including former White House AI adviser and current OpenAI Head of Strategic Futures, Dean Ball, have argued that the U.S. should restrict or effectively ban the use of Chinese open-weight models to preserve America’s technological advantage and mitigate potential national security risks. 

TechCrunch has reached out to Moonshot and the Treasury for comment.

On Wednesday, IBM officially reported earnings and the news was as bad as everyone knew it would be.

While the 115-year-old company still generates boatloads of cash — $17.2 billion in revenue, $9.9 billion in gross profit, nearly 58% margins, and $2.2 billion in net earnings for the quarter — its results fell well short of Wall Street’s expectations.

It was such a bad miss that IBM CEO Arvind Krishna and the board took an unprecedented step of warning investors ahead of time that the earnings “was worse than our expectations,” offering everyone a sneak peek.

He published a “letter to investors,” last week sharing preliminary results. It warned of abysmal revenue in the company’s all-important “infrastructure” category and said that profit margins were also going to take a hit. The company’s stock instantly tanked 25%, it’s biggest single-day decline ever. Until then, the stock had performed well under Krishna’s six years of leadership, buoyed by the AI data center boom that had been lifting all boats.

On Wednesday, IBM also lowered its full-year growth forecasts, meaning this horrible quarter would impact the rest of the year. The culprit? IBM’s cash-cow mainframe business was down 42%.

That’s a cascading problem, because as CFO Jim Kavanaugh explained on the quarterly call with investors, IBM earns $3 in software revenue for every $1 of mainframe hardware it sells.

However, the CEO and CFO spent the call insisting that this was a temporary blip and all would be well soon.

What happened, they said, was that “tens” of customers that were due to buy a new mainframe during the quarter opted not to do so. That may not sound like a lot of customers, but mainframes are systems that cost hundreds of thousands to millions of dollars, and with maintenance contracts and software, generate many millions more.

The same AI boom that lifted IBM’s boat also sank it.

Instead of buying a new mainframe, these clients bought other hardware, Krishna explained. They were faced with astronomically high cost increases of 15% to 30% for data center gear and PCs.

“When they were faced with that issue, then they decided to move budget to those areas where they were having that extreme price,” Krishna said.

Enterprise hardware makers like Dell and HP have warned that rising costs on components like memory, caused by the AI build-out boom, have forced them to raise prices. Apple has said the same.

But Krishna promised that those customers will still buy their new mainframes eventually — along with their new software contracts. In fact, he said some of them have already done so this quarter. “We see no evidence of clients moving off the mainframe,” he said.

We’ll have to wait and see. But the tech industry has predicted the death of the mainframe for many decades now. Maybe even AI won’t kill it.

Big quote: Jensen Huang just spent a week watching his company’s valuation get hit by the threat of a Chinese open-weight model. His response was to go on the record defending it. Speaking to Axios in Fort Worth, Texas, at the opening of a new phase of the Wistron plant that builds Nvidia’s AI infrastructure, the Nvidia CEO said American companies should “absolutely” be free to run Chinese models. “These Chinese models are excellent,” he said. “Open-source models that are excellent should be used.” Asked whether China could displace American labs, he was blunt: “Zero possibility.”

The timing is what makes it interesting. Moonshot AI released Kimi K3 on July 16. Independent evaluators put it third on Artificial Analysis’ Intelligence Index, only behind Anthropic’s Claude Fable 5 and OpenAI’s GPT-5.6 Sol, and first on LMArena’s blind Frontend Code Arena board.

The market took it about as well as it took DeepSeek. The Philadelphia Semiconductor Index fell 12.5% in a week, its worst stretch in 15 months. Taiwan’s benchmark dropped more than 6%, Japan closed down 4%, and Chinese AI stocks actually got hit harder than American ones, with Zhipu down as much as 30% in Hong Kong and MiniMax off 16%.

“The market misunderstood the impact of DeepSeek the first time,” Huang told Axios, adding that Wall Street has “misunderstood the impact of Kimi again this time.”

His pitch is one Nvidia has made before, delivered without much subtlety this time: “Free AI should be great for hardware. Free AI should be great for chips. Free AI should be great for data centers.” The logic is that as cheap, capable models get used more, not less, more usage means more chips running somewhere. He also argued open models don’t cannibalize OpenAI and Anthropic, but they give people a free first taste, and plenty of those people end up paying for something faster and more reliable.

Bloomberg’s analysis of K3 noted that where DeepSeek’s story was about cheaper training, Moonshot’s is about a much larger model that leans harder on memory infrastructure.

The security argument, flipped

Huang waved off the idea that a downloaded Chinese model is some kind of backdoor to Beijing, pointing out you can inspect the weights, tweak them, and run the whole thing sealed off from the internet if you want. His argument is that openness actually makes things safer, because more people are looking for problems. Lock everything into one closed system, he said, and “if everything just becomes one single model, one single point of attack, one single source of failure, I think the world is much, much more vulnerable.”

– Kimi.ai (@Kimi_Moonshot) July 16, 2026

Then he turned that same logic on an American company. Huang said Anthropic should open up Claude Mythos, its cybersecurity model that’s currently restricted to a vetted group of partners, calling it something that “should be available as a service” and arguing “holding Anthropic back is not in the benefit of the United States.” His framing: “Just because Mythos is not available, open models are available anyhow.”

It is worth noting that Nvidia is one of the partners with access to Mythos already, through Anthropic’s Project Glasswing program. Glasswing has grown from around 50 partners to roughly 200 across about 15 countries, and Anthropic has said models this capable will likely be widely available within 6 to 12 months regardless of what it decides.

Meanwhile, in Washington…

– Treasury Secretary Scott Bessent (@SecScottBessent) July 22, 2026

Huang’s comments landed hours after Treasury Secretary Scott Bessent told Fox Business the administration is looking into whether Chinese AI models were built on stolen US intellectual property.

“If we see … that overseas models are stealing from our great companies, we have the ability to sanction them because of this theft,” Bessent said, pointing to what he called “watermarks” of US models showing up inside Chinese ones, with action possible within days or weeks. He also floated whether US companies should have to disclose to customers when they’re running Chinese models.

– Director Michael Kratsios (@mkratsios47) July 22, 2026

Behind the scenes, Axios says this fight has been simmering for a while. US Commerce has been considering blacklisting Chinese AI labs since last year, and the White House had a draft executive order that would’ve made US companies liable for running Chinese models. All of it stalled, but Kimi’s release seems to have brought it back to life.

Jensen Huang split the difference on the “they stole our work” question: “Distillation, learning from AI, learning from other sources of knowledge, is fundamental to intelligence.”

Read the incentives

Granted, all this commentary is not coming from a neutral party. Nvidia’s China revenue is basically zero right now, down from a business Huang once said could be worth $50 billion a year. Huang has spent two years arguing against export controls, so of course he’s going to say demand for AI is elastic and that restrictions can backfire. That said, he has also endorsed keeping Blackwell and Rubin out of China’s hands.

Inside Wistron’s AI infrastructure manufacturing facility in Fort Worth, Texas.

The good news is that his core claim could soon be verified. Every number on Kimi K3 so far comes from Moonshot itself or from early API testing. The full weights go public on July 27, and at that point the benchmarks either hold up under independent testing or they don’t.

Anthropic has accused Moonshot of training on 3.4 million Claude conversations earlier this year, though analyst Nathan Lambert’s take is that even if some of that happened, it’s not enough to explain how good K3 actually is. Separately, Epoch AI estimates the gap between the best open and closed models is now down to around three months.

If that gap keeps shrinking, the real question stops being whether US companies should be allowed to use Chinese models, and starts being whether banning them would even do anything once the weights are already sitting on servers everywhere.

Apple is already working on a refreshed version of the MacBook Neo, only a few months after launching its cheapest laptop.

According to Bloomberg’s Mark Gurman, Apple has been testing an updated MacBook Neo with an A19 Pro chip and more memory. The report backs earlier claims that a second-generation model could arrive in 2027, bringing better multitasking performance and improved long-term usability.

More memory could be the most important upgrade

The current MacBook Neo runs on a binned A18 Pro chip paired with 8GB of unified memory. It handles browsing, streaming, and everyday productivity reasonably well, but the limited memory can become noticeable once several apps and dozens of browser tabs are running together.

Bloomberg does not reveal the exact memory capacity of the new model. Previous reporting pointed to 12GB, which would give the Neo more breathing room for multitasking and future macOS features. Not to mention that 12GB memory is also a requirement to run advanced Apple Intelligence features locally.

The A19 Pro, which is onboard the iPhone 17 Pro and Pro Max models. should also bring improvements across CPU, graphics, and Neural Engine performance. Apple is reportedly planning to refresh the laptop periodically with new colors as well.

More memory could also push the price higher

The bigger question is how much Apple will charge. The MacBook Neo launched at $599 in March, but its starting price climbed to $699 in June after rising memory costs forced Apple to increase prices across the Mac lineup. Adding more unified memory could make it difficult to hold the current price, although the Neo would still sit comfortably below the MacBook Air.

There is no word yet on whether Apple will address some of the other compromises found on the first model, including the non-backlit keyboard, mechanical trackpad, and inconsistent USB speeds. The refresh is expected to arrive at the 2027 spring launch event, along with the standard iPhone 18 and the iPhone 18e.