They’re one of the only companies that license their training data (though in this entire field, I’d wager plenty of these data sets themselves are built on stolen data).
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Report: Apple's AI Strategy Could Finally Pay Off in 2026
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I disagree. In the beginning, some chipped cards had to be swiped while others were inserted. Merchants were hit or miss accepting chipped cards. It was considered “too slow” so quick chip had to be implemented.
A lot of executives should be in jail. Tim is one of them. Apple Intelligence and the iPhone 16 advertising slogan was fraud for investors and consumers. I’m sure there’s more corruption under the hood at Apple than any of us can see.
Apple’s AI strategy: Wait till it’s too late while saying it’s all part of the plan. 🤦♂️
This is massive cope. Apple had better hope their predictions about AI are correct or they will find themselves in the dustbin as badly as they were in the 1990s.
😆
Apple aren’t facing billions in fines for violating copyright law via incorporating pirate books off the internet into their model.
Far safer to just license it from Google/anyhropic/openai and let Google fight that legal battle when it inevitably comes to pass.
Scraping the internet of questionable material and running it through their own datacenter is no their core business.
Let someone else do that; they can work on inference hardware instead.
Too late for…what exactly? There isn’t a single company doing anything but lighting billions of dollars on fire with AI right now. *Nobody* is running a profitable business, not even close to break even.
They certainly can’t make Siri any worse. All I ask from Apple as they wrap Siri in a Gemini blanket is please don’t announce “That was from Google Gemini” after every single response.
Lets use your example.
generated images are helpful for designers for example. The AI will whip a quick concept images where you can pick and choose elements you like and then you can proceed with your work. Artists always look for inspiration anywhere and if this gives you fast results then its also another source of inspiration.
generated images are helpful for designers for example. The AI will whip a quick concept images where you can pick and choose elements you like and then you can proceed with your work. Artists always look for inspiration anywhere and if this gives you fast results then its also another source of inspiration.
The issue really isn't IP in general; it is copyright. If I learn calculus from a textbook, I am free to use that knowledge however I wish. What I can't do is copy sections of the book and publish them as my own. The same holds for anything that is publicly readable without contractual restriction (e.g. paywall or NDA). Learning is not a copyright violation. If that principle applies to human learners, then I see no reason it shouldn't apply it to machine learners (i.e. LLMs).
The deeper issue is that the law draws a categorical distinction between human and machine cognition. It calls human cognition "learning" and digital cognition "copying". As long as the material used to train the AI was accessed lawfully, the difference between human learning and machine learning is merely one of relative physical and algorithmic limitations. In other words, a difference in degree, not kind. The problem seems to be that the law and society have not caught up to the fact that we have crossed a threshold. Artificial intelligence is no longer just a theoretical concept; it is a functioning reality. Epistemically, there is no categorical difference between human intelligence and artificial intelligence. The distinction made in law privileges one physical substrate (wetware) over another (hardware) based solely IMO on anthropocentric bias, not on any defensible principle of how knowledge works.
I'm not naïve about the dynamics at play in the law. Our institutions lag because entrenched interests make lots of money off of maintaining an obsolete status quo.
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Apple bet heavily on on-device task-specific neural networks that would add value to its current features. Don't take my word for it. Just look at the M-series architecture. The neural engine would not be there if it were not integral to Apple's strategy.
Unfortunately, Federighi saw the LLM inflection point too late. And if Federighi was late to the party, Joswiak wasn't even in the building. This was a professional failure. They simply failed to recognize a fundamental change in the industry.
Apple is still executing on their initial strategy, as they should because it isn't a bad strategy. It plays to Apple's existing market strengths. But now they doing so in a hurry, albeit revised, backfilled, and red faced.
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Look at Google, their Gemini doesn’t have to be directly profitable, it only needs to drive revenue for its other services/products.
Sure. Any evidence that’s actually happening? Any financial disclosures indicating that’s the case from any of the big players? The tens of billions in Capex spending and GPUS that can’t even be powered on because the electrical infrastructure simply doesn’t exist certainly stand out on financial disclosures.
Surely companies can seemingly do this forever in the claim that it’s helping other business ventures right?
What these companies are telling credulous investors, and what the actual business models will support are clearly not lined up when it comes to AI.
This logic is a stretch at best. Apple Intelligence sucks, it always has, and (a lot of us suspect) it always will.
"Slop" is all Apple have done for close to a decade now. They ship all of their software before it's ready. This is modern day Apple whether we like it or not. The Apple you've described is the one that existed 20 years ago.
It’s quite a thing to call anything Apple is doing around AI a “strategy”.
Yeah. It’s call ignoring ‘FOMO” and the Wall Street / CNBC/ Bloomberg / ‘Analyst’….TOUTS!
Apple's restrained artificial intelligence strategy may pay off in 2026 amid the arrival of a revamped Siri and concerns around the AI market "bubble" bursting, The Information argues.
The speculative report notes that Apple has taken a restrained approach with AI innovations compared with peers such as OpenAI, Google, and Meta, which are investing hundreds of billions of dollars in data centers, chips, and large language model training. This has fueled criticism that Apple is falling behind in the AI space, particularly as Siri has significantly lagged behind more advanced, capable, and reliable conversational systems.
The report argues that market sentiment toward AI spending is beginning to show signs of skepticism, with questions emerging over whether such large investments can be justified by near-term revenue. Against that backdrop, Apple's decision to limit AI-specific capital expenditures has left it with more than $130 billion in cash and marketable securities, giving the company the option to pursue acquisitions or partnerships if valuations of AI startups fall.
Apple's biggest AI-related move in 2026 will be the long-anticipated overhaul of Siri, which is expected to arrive in the spring. The updated assistant is set to be more conversational and capable of completing multi-step tasks. To power it, Apple is believed to be adopting Google's Gemini, reflecting an internal view that large language models may become commoditized and not worth the cost of large-scale proprietary development.
The iPhone is said to be a key strategic advantage. Unlike AI companies that rely on standalone apps or web services, Apple can distribute AI features directly through software updates and system-level integrations across its devices. Efforts by AI companies to build competing hardware face major challenges in manufacturing, distribution, and ecosystem development, areas where Apple has very strong footholds.
The Information also points to recent leadership changes as part of Apple's effort to refocus its AI work. Siri has been placed under Mike Rockwell, who was responsible for launching the Vision Pro headset, following significant delays to the assistant's overhaul. In addition, Apple's AI chief John Giannandrea announced his retirement earlier in December, with parts of his organization redistributed into product-focused teams amid internal concerns about a lack of clear product direction.
While Apple has a history of early but uneven AI efforts, including the original launch of Siri in 2011, The Information argues that these shortcomings have not materially harmed its core businesses. 2026 may be an inflection point in which Apple's cautious strategy could appear prescient if enthusiasm for large-scale AI spending continues to cool and the company finally delivers a more capable version of Siri.
Article Link: Report: Apple's AI Strategy Could Finally Pay Off in 2026
This used to be what Apple did best - adding new features only when they were ready, and no sooner. Hopefully the Apple Intelligence fiasco was lesson enough…
Apple isn't perfect, no one is bug free but looking at the landscape with Google and Microsoft though, which have disjointed experiences (examples off the top of my head: Google Home / Nest device mess, Windows 11 is a sham of an OS) Apple is polished. Nothing is 20 years ago, from hardware to software everything is much more complex.
Who else has anything close to AirPlay, or how easy iCloud sync works, or even something like using Freeform & Notes? Sure no one likes UI change but what other OS runs as well on phones / tablets / desktop computers as iOS / iPadOS & MacOS?
None. If you're concerned about early release cycle bugs just wait to upgrade, the .2s for iOS 26 / MacOS 26 seem pretty stable.
Not to mention the hardware, Apple Silicon is crushing it, the phones / tablets / Macs are the best they've ever been. Sure it's easy to point out flagship features on competitors but Apple isn't about being on the cutting edge, they're about layering new tech into their product line in a way that fits.
They didn't want to get left behind on LLMs and made a mistake with Apple Intelligence being too early, its not a big deal, when they deliver, they'll do it their way and it'll be a more seamless Apple like experience.
Again, not what I said.
In my real world example, we had the design concepts. What we tried to use generative AI for was to quickly make some images to convey the concepts. It failed miserably to do so. While it generated impressive looking images, for the most part they were either flat wrong or very poor at depicting the key concepts in question. Eventually we got something sort of usable for rough draft purposes, but after much more time and effort than just creating some simple images without AI.
I don’t regret trying - good exercise to see if this new technology can be useful to us. Verdict - at current state of the art, definitely not worth it for our purposes and in fact creates negative work. And that is with taking advantage of the fact that Google offers it for free. If we’d had to pay the actual costs, I’m sure my boss would have fired me.
I see people asserting hypothetical claims like yours, but in my personal experience, I’m just not seeing generative AI adding enough value to justify the cost.
On the other hand, there are AI tools that are very useful. For instance, I was able to use the image extraction feature of the photos app on my iPhone to help generate some images that I needed to convey another concept with just a few seconds of simple minded effort instead of the hour or more of mentally tedious work it would have taken me to do a worse job otherwise. That was AI that was extremely useful to me and well worth whatever cost it added to my iPhone ownership cost.
the image playground, generative emojis, writing tools, etc. make nothing but slop. i think Apple already showed a lack of the care and restraint you're ascribing to them, given AI was the main topic of the last two WWDCs and much of the iPhone 16/17 ads.
not that i think they need a big in-house LLM and all the associated costs and risks, but maybe their mistake was focusing on intrinsically worse on-device models vs. just integrating Gemini, ChatGPT, etc. earlier?
AI will be good for identifying patterns (think correlations) and replicating patterns (think images/video) in the short run, I'm not convinced we can expect more. But I could be wrong. As for AI hype and stock market booms, similar patterns repeat throughout history - fomo (free of missing out) rules (think of dutch tulip boom/bust)
And once again you failed to read what I wrote.
I was not the one using the generative AI. Members of my team who know perfectly well how to use it were the ones using it. The problem was not that they didn’t know how to use it. The problem was that it is miserably useless for the task.
So, let’s try this again. Show me something useful that LLM based generative AI can do right now that is useful enough to be worth the cost.
I’ve been asking and so far there has been no answer.
Conclusion - for AI tasks that can actually do something useful, Apple is providing products and services that do well enough. As for the LLM tech that the cool kids here assert that Apple is lagging behind on, so far it looks like there’s no actually useful purpose to it other than bragging rights. Which means this article was pretty much on target.
If you disagree, then don’t waste time with another pedantically smug reply that misses the point. Just provide a real world example of a generative AI model doing something actually useful and doing it better enough than the alternatives to justify the cost.