Here’s a number worth sitting with: fewer than 20% of buyers say AI features are the main reason they pick a phone or laptop. Camera quality, battery life, and price still win the day. So when Apple leans hard into calling the MacBook Air M5 an “AI-ready” machine, you should ask what that phrase actually buys you. I’ve spent enough time testing chips across generations to tell you the honest answer: the hardware is genuinely better. The marketing category built around it is mostly noise.
The Neural Engine Gains Are Real, Not Hype
Let’s give credit where it’s due. Apple’s Neural Engine has made steady, measurable jumps generation over generation. The M3’s Neural Engine ran up to 60% faster than the M2’s on machine learning tasks like image processing and speech recognition, according to Apple’s own hardware team. That’s not a rounding error. Independent lab testing across the M1, M2, and M3 chips shows throughput gains of 20% to 60% per generation on tasks ranging from image classification to local transformer inference.
If that pattern holds, and there’s no strong reason to think it won’t, the M5’s Neural Engine should keep pushing that curve upward. This matters for anyone running on-device AI tools: photo culling, voice transcription, local language models for drafting or coding help. The chip work is legitimate engineering progress, and I don’t want to undersell that just to make a contrarian point.
But a fast Neural Engine and an “AI-ready laptop” are two different claims. One is a spec you can benchmark. The other is a category invented by marketing teams to sell you on urgency you may not need.
What “AI-Ready” Actually Means (And Doesn’t)
The AI PC label has ballooned fast. IDC projects that AI PCs will account for nearly 60% of all PC shipments by 2027, as chipmakers shift entire product lines to NPU-equipped platforms [1]. On the phone side, more than 70% of premium smartphones are expected to ship with hardware-accelerated AI chips as of the most recent projections. That’s a massive hardware transition happening in a short window.
Here’s the problem: hardware adoption and software usefulness are not the same curve. Gordon Mah Ung, an executive editor who covers PC hardware closely, put it bluntly: right now, “AI PC” is mostly a marketing term. The chips are real, but the software ecosystem that would make those chips essential is still young. I’ve tested enough “smart” devices claiming AI capabilities to know this pattern well.
The hardware ships first. The compelling use case shows up months or years later, if it shows up at all.
Analyst Anshel Sag flagged a version of this same concern: there’s a real risk that vendors slap “AI” onto every new machine without delivering an experience different enough to justify an upgrade for most people. That’s not cynicism. That’s just how consumer tech marketing has always worked, going back to “HD Ready” TVs and “4G LTE” phones that weren’t quite there yet.
What History With Apple Silicon Actually Predicts
If you want a grounded way to think about the M5, look at the generational track record instead of the launch-day slide deck. The M1 to M3 progression gives you real data: consistent double-digit percentage gains in ML throughput, a Neural Engine that keeps getting faster at tasks like speech recognition and image processing, and a chip architecture Apple has clearly committed to improving year over year.
That track record is exactly why I’d trust the M5’s Neural Engine improvements more than I’d trust the phrase built around them. Apple’s Johny Srouji has said the company designs its chips to enable sophisticated machine learning on device so users get intelligence features without giving up privacy. That’s a real design philosophy, and the hardware backs it up generation after generation.
The gap is on the software side. Apple Intelligence rollout has been slower and choppier than the chip improvements underneath it. A fast engine with nothing meaningful to run on it doesn’t help you edit a video faster or answer emails better. The chip is ready. Whether the software stack you actually use daily is ready is a separate question entirely, and it’s the one marketing conveniently skips.
RAM Matters More Than the AI Label Does
If you’re actually trying to run local ML workloads, whether that’s transcription, photo library indexing, or a small local language model, the spec that matters most isn’t the Neural Engine TOPS count. It’s RAM. Testing on MacBook Air and Pro models shows RAM pressure significantly affects ML workflows, and 16GB or more is increasingly the practical minimum, not the old 8GB base configuration.
That’s a boring, unsexy detail compared to “AI-ready laptop.” But it’s the one that actually determines whether your machine chokes when you’re running a local model alongside your normal browser tabs and apps. If you’re shopping the M5 lineup, RAM is the upgrade worth paying for. The Neural Engine improvement comes standard whether you spend extra or not.
This is the same lesson I keep relearning testing smart home hubs and gear: the spec sheet number that gets marketed hardest is rarely the one that determines your day-to-day experience. Storage speed, RAM, thermal headroom, these unglamorous specs usually matter more than whatever buzzword sits on the box.
Consumers Aren’t Buying the AI Pitch, and They’re Right Not To
Consumer analyst Carolina Milanesi said it plainly: people aren’t buying a phone because it’s an “AI phone.” They’re buying it for the camera, the battery, the price, and maybe a feature or two that happens to be powered by AI under the hood. Marques Brownlee has made a similar point about phones, calling most current AI features “nice-to-have extras” rather than must-have reasons to upgrade. Cool demos. Things you might use once or twice.
That skepticism tracks with what the data shows. Consumer surveys from 2024 and 2025 consistently find that fewer than 20% of buyers name AI features as a primary purchase driver. Camera, battery life, and price still dominate the decision. The industry keeps building the AI narrative around devices, and buyers keep making decisions based on the same practical factors they always have.
I think that instinct is correct. Ask yourself what you’ll actually do with a laptop in the next three years, not what a keynote slide says you might do someday. If your answer is video editing, web browsing, spreadsheets, and the occasional Zoom call, the Neural Engine improvement is a nice bonus, not a reason to upgrade on its own.
How to Actually Evaluate an “AI-Ready” Device
Skip the label entirely and ask a few concrete questions instead:
- What specific tasks does this chip run faster, and do you actually do those tasks regularly?
- How much RAM does the configuration include, and is it enough for the workloads you care about?
- Does the software you already use take advantage of the on-device AI hardware, or is that support still “coming soon”?
- Would you buy this device on its non-AI merits alone, camera, screen, battery, build quality?
If a machine only makes sense because of a feature marketed as AI, wait. Features that matter tend to get better with software updates anyway, and you’re not locked out of future improvements just because you bought last year’s chip. If the machine makes sense on its fundamentals and the AI capability is a bonus, buy it. That’s basically my verdict on the MacBook Air M5: buy it for the build quality, the battery life, and the Neural Engine’s real generational gains. Don’t buy it because a marketing team decided “AI-ready” was this year’s headline.
Frequently Asked Questions
Is the MacBook Air M5’s Neural Engine actually faster than previous generations?
Yes. Apple’s own testing showed the M3’s Neural Engine ran up to 60% faster than the M2’s on ML tasks, and independent lab testing across M1 through M3 shows consistent 20% to 60% gains per generation. The M5 continues that trend.
Does “AI-ready laptop” mean anything specific, or is it just marketing?
It’s largely a marketing category. The underlying hardware, NPUs and Neural Engines, is real and improving. But the software ecosystem that would make those chips essential for everyday tasks is still catching up, according to reviewers like Gordon Mah Ung.
How much RAM do I need for AI features on a MacBook Air?
Testing suggests 16GB or more is increasingly necessary for local ML workloads like transcription or small local language models. The old 8GB base configuration is becoming a real bottleneck for anyone using these features regularly.
Should I buy a laptop or phone because it’s marketed as an “AI” device?
Probably not on that basis alone. Consumer surveys show fewer than 20% of buyers cite AI features as their primary purchase driver, and analysts like Carolina Milanesi argue camera, battery, and price still drive real purchase decisions. Buy for the fundamentals first.
If you’re weighing a MacBook Air M5 purchase right now, don’t let the “AI-ready” branding do your thinking for you. Look at what you actually do with a laptop day to day, check the RAM configuration against your real workload, and treat the Neural Engine gains as a solid bonus rather than the headline reason to buy. I’ll keep testing these chips as real workloads roll out, and I’ll tell you honestly when the software finally catches up to the hardware.
Sources
Researched from 14 vetted sources · average source authority DR 76