Other ARM SoC's haven't been able to bridge the relative gap in raw single-core performance going on 5+ years, and haven't hit multi parity since iDevices went

Other ARM SoC's haven't been able to bridge the relative gap in raw single-core performance going on 5+ years, and haven't hit multi parity since iDevices went beyond 2 cores. I don't really see a gamble here at all - adding ML blocks is relatively cheap and icing on the cake. Take Geekbench (and any other benchmarks) with a grain of salt of course, but benchmarks & reviews all corroborate the same story. See these GB4 results for instance: Android [1] - 3323 single, 8894 multi iPhone XS [2] - 4794 single, 11151 multi Especially for single-core, the latest, fastest Android phone (Galaxy S9) is a bit slower than the iPhone 7 [3], and every other Android phone (running Qualcomm's latest Snapdragon 845) is equivalent to the iPhone 6s / SE. The delta is just huge. It's actually a structural / incentive problem that keeps Apple at an advantage in this case, so I wouldn't count on the gap being bridged or even reduced anytime soon. Apple has built an incredible flywheel that lets them earn much more $ per chip than Qualcomm or Samsung by bundling them into super high-margin, high-volume iPhones. Everything equal, my wild-ass guess is Apple can probably throw at least 33% more transistors at workloads than the competition, due to being on leading edge nodes, being comfortable with lower yields, running multiple chip teams in parallel for maximum efficiency & manual layout, etc. This means huge caches, fancy pipelines, lots and lots of specialized silicon, etc. They also have arguably the most talented chip team in the industry. And that's just the chip. Because Apple is so vertically integrated, they're better able to optimize the device as a whole. That means more expensive components like faster ram & disk, better integration between IP blocks like CPU/GPU/ML, and more optimized OS/drivers that all play into overall performance and differentiated use cases. It's a business structure that is nearly impossible to replicate and will continue to create a lasting advantage. With regard to how ML is used, it fits the approach Apple is taking to keep things on-device (vs in the cloud) as much as possible. So any use cases where ML is used in the cloud are potential use cases for ML on the device. [1] https://browser.geekbench.com/android-benchmarks https://browser.geekbench.com/android-benchmarks [2] https://browser.geekbench.com/ios_devices/56 https://browser.geekbench.com/ios_devices/56 [3] https://browser.geekbench.com/ios-benchmarks https://browser.geekbench.com/ios-benchmarks