Apple's AI platform pivot potential was published on Monday, March 10th, 2025. It was the best of times. It was the worst of times. It was the age of wisdom. It was the age of foolishness. It was the epoch of belief. It was the epoch of incredulity. It was the season of light. It was the season of darkness. It was the spring of hope. It was the winter of despair. We had everything before us. We had nothing before us. We were all going direct to heaven. We were all going direct the other way. In short, the period was so far like the present period that some of its noisiest authorities insisted on it being received for good or for evil in the superlative degree of comparison only. Charles Dickens, A Tale of Two Cities.
Apple's bad week. Apple has had the worst of weeks when it comes to AI. Consider this commercial which the company was running incessantly last fall.
In case you missed the fine print in the commercial, it reads, 'Apple Intelligence coming fall 2024 with Siri and device language set to US English. Some features and languages will be coming over the next year.' Next year is doing a lot of work now that the specific feature detailed in the commercial, Siri's ability to glean information from sources like your calendar, are officially delayed. Here is the statement Apple gave to John Gruber during Daring Fireball: 'Siri helps our users find what they need and get things done quickly. And in just the past 6 months, we made Siri more conversational, introduced new features like type to Siri and product knowledge, and added an integration with ChatGPT. We've also been working on a more personalized Siri, giving it more awareness of your personal context, as well as the ability to take action for you within and across your apps. It's going to take us longer than we thought to deliver on those features and we anticipate rolling them out in the coming year.'
It was a pretty big surprise even at the time that Apple, a company renowned for its secrecy, was so heavily advertising features that did not yet exist. I also, in full disclosure, thought it was all an excellent idea from my post-WWDC update. Quote, 'The key part here is the understanding personal context bit. Apple Intelligence will know more about you than any other AI because your phone knows more about you than any other device and knows what you are looking at whenever you invoke Apple Intelligence. This by extension explains why the infrastructure and privacy parts are so important. What this means is that Apple Intelligence is by and large focused on specific use cases where that knowledge is useful. That means the problem space that Apple Intelligence is trying to solve is constrained and grounded both figuratively and literally in areas where it is much less likely that the AI screws up. In other words, Apple is addressing a space that is very useful that only they can address and which also happens to be safe in terms of reputation risk. Honestly, it almost seems unfair. Or to put it another way, it speaks to what a massive advantage there is for a trusted platform. Apple gets to solve real problems in meaningful ways with low risk and that's exactly what they are doing. Contrast this to what OpenAI is trying to accomplish with its GPT models or Google with Gemini or Anthropic with Claude. Those large language models are trying to incorporate all the available public knowledge to know everything. It's a dramatically larger and more difficult problem space which is why they get stuff wrong. There is also a lot of stuff that they don't know because that information is locked away like all of the information on an iPhone. That's not to say these models aren't useful. They are far more capable and knowledgeable than what Apple is trying to build for anything that does not rely on personal context. They are also all trying to achieve the same things.' End quote.
So, is Apple more incompetent than these companies or was my evaluation of the problem space incorrect? Much of the commentary this week assumes 1, but as Simon Willis notes, you shouldn't discount point 2. I have a hunch that this delay might relate to security. These new Apple Intelligence features involve Siri responding to requests to access information in applications and then performing actions on the user's behalf. This is the worst possible combination for prompt injection attacks. Anytime an LLM-based system has access to private data, tools it can call, and exposure to potentially malicious instructions like emails and text messages from untrusted strangers, there's a significant risk that an attacker might subvert those tools and use them to damage or exfiltrate a user's data. Wilson links to a previous piece of his on the risk of prompt injections. To summarize the problem, if your on-device LLM is parsing your emails, what happens if one of those emails contains malicious text perfectly tuned to make your on-device AI do something you don't want it to? We intuitively get why code injections are bad news. LLMs expand the attack surface to text generally. Apple Intelligence, by being deeply interwoven into the system, expands the attack surface to your entire device and all that precious content it has unique access to. Needless to say, I regret not raising this point last June, but I'm sure my regret pales in comparison to Apple executives and whoever had to go on YouTube to pull that commercial over the weekend.
Apple's great week. Apple has had the best of weeks when it comes to AI. Consider their new hardware announcements, particularly the Mac Studio and its available M3 Ultra. From the company's press release: 'Apple today announced M3 Ultra, the highest performing chip it has ever created, offering the most powerful CPU and GPU in a Mac, double the neural engine cores, and the most unified memory ever in a personal computer. M3 Ultra also features Thunderbolt 5 with more than 2x the bandwidth per port for faster connectivity and robust expansion. M3 Ultra is built using Apple's innovative Ultra Fusion packaging architecture which links two M3 Max dies over 10,000 high-speed connections that offer low latency and high bandwidth. This allows the system to treat the combined dies as a single unified chip for massive performance while maintaining Apple's industry-leading power efficiency. Ultra Fusion brings together a total of 184 billion transistors to take the industry-leading capabilities of the new Mac Studio to new heights.' Quote, 'M3 Ultra is the pinnacle of our scalable system on a chip architecture aimed specifically at users who run the most heavily threaded and bandwidth intensive applications.' End quote. Said Johny Srouji, Apple's senior vice president of hardware technologies. Quote, 'Thanks to its 32-core CPU, massive GPU, support for the most unified memory ever in a personal computer, Thunderbolt 5 connectivity, and industry-leading power efficiency, there's no other chip like M3 Ultra.'
That Apple released a new Ultra chip wasn't a shock given there was an M1 Ultra and M2 Ultra. Almost everything about this specific announcement, however, was a surprise. Start with the naming. Apple names have two components. M blank refers to the core type and the suffix to the configuration of those cores. Therefore, to use the M1 series of chips as an example, the M1 cores in question were the Firestorm high-performance core, Icestorm energy efficient core, and a not publicly named GPU core. All three of these cores debuted first on the A14 Bionic chip, which shipped in the iPhone 12. The suffix meanwhile referred to some combination of increased core count, both CPU and GPU, as well as an increased number of memory controllers and associated bandwidth and in the case of the M1 series faster RAM. The Ultra notably was simply two Max chips fused together. That's why all the numbers simply double. The M2 was broadly similar to the M1, at least in terms of the relative performance of the different suffixes. The M2 Ultra, for example, simply doubled up the M2 Max. The M3 Ultra, however, is unique when it comes to max RAM. I can't completely vouch for every number on this table, which was sourced from Wikipedia, as Apple hasn't yet released the full technical details of the M3 Ultra, and it's not yet available for testing. What seems likely, however, is that instead of simply doubling up the M3 Max, Apple also reworked the memory controllers to address double the memory. That also explains why the M3 Ultra came out so much later than the rest of the family. Indeed, the Mac Studio base chip is actually the M4 Max.
The wait was worth it. However, what makes Apple's chip architecture unique is that the RAM is shared by the CPU and GPU and not in the carve-out way like integrated graphics of old. Rather, every part of the chip, including the neural processing units, which I didn't include on these tables, has full access to all of the memory all the time. What that means in practical terms is that Apple just shipped the best consumer-grade AI computer ever. A Mac Studio with an M3 Ultra chip and 512 GB of RAM can run a 4-bit quantized version of DeepSeek R1, a state-of-the-art open source reasoning model, right on your desktop. It's not perfect. Quantization reduces precision, and the memory bandwidth is a bottleneck that limits performance. But this is something you simply can't do with a standalone NVIDIA chip, pro or consumer. The former can, of course, be interconnected, giving you superior performance, but that costs hundreds of thousands of dollars all-in. The only real alternative for home use would be a server, CPU, and gobs of RAM, but that's even slower, and you have to put it together yourself. Apple didn't, of course, explicitly design the M3 Ultra for R1. The architectural decisions undergirding this chip were surely made years ago. In fact, if you want to include the critical decision to pursue a unified memory architecture, then your timeline has to extend back to the late 2000s whenever the key architectural decisions were made for Apple's first A4 chip, which debuted in the original iPad in 2010. Regardless, the fact of the matter is that you can make a strong case that Apple is the best consumer hardware company in AI. And this week affirmed that reality.
