A $100 price hike and a pricing problem

Three months after launch, Apple raised the MacBook Neo price by $100. That’s a double-digit jump on a $599 machine. The culprit? A brutal memory crisis that’s squeezing the entire consumer tech industry.

It’s not just Apple. RAM and chip costs have soared as manufacturers race to build AI infrastructure. Enterprise demand is eating up supply. Everyday buyers are left with fewer options, and those options cost more than they did a year ago.

But here’s the thing: even after the hike, the Neo still works. It’s $400 to $500 cheaper than the entry-level M5 MacBook Air. It packs an aluminum unibody in a world of cheap plastic. And it brings Apple Intelligence features — previously reserved for premium models — to a much lower price point. That’s its magic.

Why the Neo sold like crazy

The MacBook Neo launched in March with an iPhone-class chip, 8GB of RAM, and 256GB of storage. It flew off shelves. Apple initially ordered a few million units, then upped that to over 10 million after just a month. The demand was that strong.

Why? Because it knew exactly who it was for. People who browse the web, manage documents, attend Zoom calls, edit a few photos, and stream Netflix. That’s the audience. They don’t care about local LLMs or on-device AI image generation. They just want a laptop that works, feels premium, and doesn’t break the bank.

The Neo checked every box that mattered. It became an aggressive gateway into the Apple ecosystem. And it did all that without chasing the AI hype.

The AI arms race is ruining budget laptops

Look at what’s happening with Windows OEMs. Most brands below $1,000 are scrambling to meet Microsoft’s Copilot+ PC requirements. That means at least 45 TOPS of on-device AI compute. That means more powerful CPUs, GPUs, or system-on-chips like Qualcomm’s. That means larger memory pools and faster memory. And that means higher prices.

The result? Budget laptops are getting expensive. They’re being stuffed with hardware most people don’t need. The MacBook Neo, with its modest 8GB of RAM and repurposed A18 Pro chip, made sense precisely because it ignored that race.

Apple already segments its AI features

Apple isn’t treating AI as a uniform experience. Older iPhones like the iPhone 15 don’t support Apple Intelligence at all. The new Siri AI is available on the Neo and the iPhone 17, but advanced features like on-device Siri voices are limited to the iPhone 17 Pro or iPhone Air.

Apple is comfortable drawing those lines. The Neo’s successor doesn’t need to chase parity. It just needs to hold its lane.

What the Neo 2 should (and shouldn’t) do

If Apple wants to improve performance, it could reuse binned A19 Pro chips, much like it did with the A18 Pro in the Neo. That keeps costs down. It could stick with older, cheaper DDR4 memory instead of jumping to DDR5. That’s perfectly fine for browsing and video calls.

The Neo doesn’t need a desktop-class NPU, a massive GPU, or 16GB of baseline memory. Those components would add $100 to $200 to the price, pushing the Neo closer to $1,000. That would cannibalize the MacBook Air and blur the Neo’s identity.

The 512GB storage variant already costs $800 in the US. Push it much higher, and the Neo loses its reason to exist.

Good enough hardware is a proven strategy

Apple wouldn’t be the first to take this approach. Intel is bringing back older processors for budget machines. Dell recently launched laptops powered by Nvidia’s aging RTX 3050 GPU. Neither company pretends everyone needs the latest CPU or GPU. They recognize the value of “good enough” hardware.

The Neo worked because it knew what it wanted to be: an affordable entry-level laptop that handles lightweight day-to-day tasks while being light on your wallet. Its biggest strength was knowing how few AI it actually needed to succeed.

The bottom line: don’t fix what isn’t broken

The best cheap MacBook is worth far more than the cheapest AI MacBook, which costs hundreds more. I hope the team in Cupertino keeps that in mind as they work on the Neo’s successor.

The Neo’s identity is its price and its simplicity. Adding AI hardware would ruin both. Apple should resist the trend, stay in its lane, and let the Neo be what it is: a genuinely affordable laptop that doesn’t pretend to be something else.

A $700 million bet on preventive health

Neko Health, the Swedish health-tech startup co-founded by Spotify founder Daniel Ek, just closed a massive $700 million Series C funding round. The money will fuel the company’s expansion into the United States, starting with a flagship clinic in New York City.

The round was led by Lightspeed Venture Partners and O.G. Venture Partners. Existing backers Atomico, General Catalyst, and Lakestar also participated, alongside new investors including Liberty City Ventures, Positive Sum, and BDT & MSD. David Ofer of O.G. Venture Partners is set to join Neko’s board, pending regulatory approval.

With this latest injection, Neko’s total disclosed funding since 2023 now exceeds $1 billion. The company previously raised $65 million in a Series A in 2023 and another $260 million in January 2025.

A notable detail: the investor list includes Meta CEO Mark Zuckerberg and his wife Priscilla Chan, tennis legend Maria Sharapova, musician will.i.am, and former footballer Thierry Henry. Earlier individual investors include Reddit co-founder Alexis Ohanian and actor Zoë Saldaña.

What happens inside a Neko Health scan?

Neko’s clinics offer a 60-minute, non-invasive, radiation-free health assessment. The service combines full-body imaging, blood tests, custom-built sensors, and artificial intelligence — all reviewed by a clinician during the same visit.

The scan includes an electrocardiogram, arterial measurements, body-composition analysis, and more than 2,000 high-resolution images that map a customer’s skin. Blood samples are processed on-site, so results are ready before the patient leaves. A doctor or nurse discusses findings in person.

The company screens for potential signs of skin cancer, cardiovascular disease, diabetes, metabolic syndrome, and stroke risk factors. Many of these measurements — blood pressure, cholesterol, blood glucose — are available through standard healthcare. Neko’s pitch is convenience: one appointment, proprietary imaging, automated data collection, and immediate results.

But the company’s public materials don’t include a comparative study showing whether this bundled approach actually improves clinical outcomes or saves money versus established preventive care pathways.

US expansion: New York first, more cities to come

Neko plans to open clinics in New York and other US cities, though it hasn’t named specific additional locations or provided a detailed timeline. A waitlist for the New York clinic is already live on its website. Pricing for US scans hasn’t been announced.

Currently, the company operates eight clinics across the UK and Sweden: two in Stockholm, one each in Manchester and Birmingham, and four in London (Marylebone, Spitalfields, Covent Garden, and Victoria). In the UK, a scan costs £299 (about $400); in Sweden, it’s 2,750 Swedish kronor (roughly $285).

Since launching in 2023, Neko says it has completed 100,000 scans. More than 350,000 people have registered or joined waitlists. The company reports that 75% of customers book and prepay for a second scan at the end of their first appointment — a strong retention signal.

That repeat-booking model lets clinicians compare measurements and skin images over time. But public information doesn’t establish whether annual screening is the right interval for every age or risk group.

Regulatory clearance and the US healthcare puzzle

Two of Neko’s internally developed devices have received FDA 510(k) clearance — Derma-2 as an adjunctive telethermographic system, and Spectrum-2 as a tissue-saturation oximeter for cardiovascular measurements. These clearances apply to the specific devices and their intended uses, not to the complete Neko Health Scan as a single FDA-approved screening service.

The company positions its US clinics as preventive health and wellness providers, not full-service medical practices. Its privacy notice explicitly advises customers to continue seeing their existing doctors for diagnoses and treatment, including for conditions flagged during a Neko scan.

Specialist clinicians — dermatologists and cardiologists — review findings that need further examination. Follow-up appointments, referral letters, and introductions to outside specialists are included when recommended. But Neko’s US clinics don’t currently participate in health insurance plans, and the company says most services aren’t covered by a payer. Customers will pay out of pocket for the initial assessment.

Neko hasn’t disclosed what customers might pay for diagnostic tests or treatment delivered by external providers, nor whether employers or insurers will subsidize access.

What about the evidence?

Publicly available information doesn’t include a completed peer-reviewed study validating the full screening service. A trial registered on ClinicalTrials.gov is evaluating Neko’s multimodal skin-imaging technology for screening and diagnostic-support applications, including skin cancer and Raynaud’s phenomenon. But that trial is still ongoing.

Neko’s materials don’t disclose how often its scans produce false-positive findings, how many customers undergo additional procedures, or how many flagged abnormalities turn out to be clinically unimportant. The FDA clearances for individual devices don’t establish the performance of every algorithm used to combine or interpret the resulting data.

The company did share health-outcome data from 1,469 customers who completed a second scan about a year after their first. The group recorded improvements in blood pressure, cholesterol, and blood sugar, while body weight stayed broadly stable. But Neko itself says this wasn’t a scientific study — there was no control group. Customers could have started treatment or changed their behavior between appointments, so the figures don’t prove the scans caused the improvements.

CEO Hjalmar Nilsonne said part of the new capital will fund further research and development. Neko recently added body-composition measurements and clinician reviews of wearable-device data. It also introduced updated versions of its Derma, Echo, and Spectrum medical devices, which capture more health data and automate more of the scanning process.

Neko didn’t disclose its valuation after this round. The Financial Times, citing unnamed sources, pegged it at around $7 billion.

For a deeper look at how AI is reshaping diagnostics, check out our coverage of NHS AI blood test reducing invasive womb cancer checks. And if you’re interested in the broader trend of preventive health screening technology, we’ve got you covered.

It’s not about the model anymore

For years, the AI industry has been obsessed with one question: who builds the smartest model? That race is far from over, but a new bet from Anthropic and Blackstone suggests the next trillion-dollar opportunity lies elsewhere. It’s not about the model. It’s about what you do with it.

Ode with Anthropic is the name of a new $1.5-billion joint venture. Backed by Blackstone, Hellman & Friedman, Goldman Sachs, and others, the company is designed to do one thing: help the world’s largest businesses actually use AI. Not just buy a license. Not just run a pilot. Rewire core operations around it.

The move mirrors OpenAI’s own The Deployment Company, launched earlier this year. Both labs have quietly acknowledged a hard truth: selling enterprise AI requires more than a better benchmark score. It demands engineers on the ground, custom integrations, and a willingness to get your hands dirty.

How a Blackstone frustration became a company

The idea for Oe didn’t start inside Anthropic. It started inside Blackstone. The private equity giant had been trying to implement AI across its portfolio companies, bringing in both large consulting firms and smaller AI services boutiques. The results were mixed.

One boutique stood out: Fractional AI, an AI engineering services startup. Blackstone noticed. Shortly after the joint venture was announced, it acquired Fractional, turning the startup into the foundation of what is now Ode. Fractional had ended an 11-month partnership with OpenAI when the deal went through.

Chris Taylor, CEO of Ode and co-founder of Fractional, is blunt about the ambition. “It’s pretty easy to imagine this as a trillion-dollar company someday if we execute well,” he told TechCrunch. The real challenge, he says, is scaling fast without sacrificing quality.

Ode’s approach: boutique quality, private equity scale

Ode currently employs 100 engineers. It works directly with Anthropic’s applied AI team to identify where the technology can have a real impact, then builds custom systems tailored to each client’s operations. Anthropic’s internal team will continue to handle strategic, mission-aligned deployments. Ode handles the rest.

The venture will operate under a “Claude-first” principle, meaning it will use Anthropic’s technology — including features like Claude Tag in Slack — whenever possible. But it’s not locked in. If a rival model works better for a specific problem, Ode will use it.

Eddie Siegel, Ode’s chief technologist and a Fractional co-founder, puts it this way: “I think model selection matters, but it’s not where the majority of calories are spent. It’s one ingredient in a system that has to be engineered.”

The ideal customer: a CEO who’s all in

For Ode, the right customer isn’t the one with the biggest IT budget. It’s the one whose CEO is personally committed. Taylor says the work Ode does tends to be the top priority for the CEO — “the most important product feature that the company is going to build over the course of the next two years, or reworking the most important business process they have.”

That level of buy-in matters, because the work is not trivial. Taylor describes AI as “this magic, hallucinating ingredient” that needs to be carefully integrated into core business processes. Most companies simply don’t have the talent to do it themselves.

Who are Ode’s engineers? The ‘special forces’

Ode’s executives describe their team as elite generalist software engineers. Over half are former founders. Siegel calls them the kind of people who can “juggle a really challenging technical problem, but also own something end-to-end.” One Blackstone executive put it more bluntly: this is the “special forces,” not an army of forward-deployed engineers (FDEs).

Demand for such teams far outstrips supply. That’s a problem, because Ode plans to scale internationally while keeping its boutique positioning. It runs constant evaluations to measure the business impact of its implementations. But finding enough “grown-up” engineers who combine entrepreneurial experience, systems thinking, AI expertise, and enterprise product judgment is not easy.

Siegel isn’t worried. “It has never been an easier time to become an entrepreneur,” he says. “You learn so much by trying to own problems end-to-end. That’s the skill set that fits really well with Ode.”

The competition: consulting giants and rival labs

Ode is not alone in this market. OpenAI’s The Deployment Company is a direct competitor. So are consulting giants like Deloitte and Accenture, which have built their own forward-deployed engineering teams. The race to own enterprise AI implementation is already crowded.

But Ode’s backers believe the market is big enough for multiple winners. The private equity firms involved will funnel their own portfolio companies to the venture as potential customers, though Ode is not limited to selling to those companies.

The founding belief, Taylor says, is that “non-AI companies are going to be among the big winners of this whole AI moment if they adopt the technology the right way.” That’s a big if. Ode is betting it can be the one to help them get there.

The bottom line: deployment is the new frontier

Whether Ode can train enough engineers, maintain quality, and fend off competitors remains an open question. But the signal from Anthropic, Blackstone, and OpenAI is clear. The next great AI race will not be won on a leaderboard. It will be won inside the world’s largest companies, one custom integration at a time.

Models are becoming commodities. Implementation is the moat.