AAPL: Looking for upside to iPhones and a peek into AI
Apple goes into its earnings call today with the weakest investor sentiment in recent years. Four consecutive quarters of negative growth in FY23 and the 1st quarter in FY24 guided flat have taken their toll on investor sentiment. We were in the minority calling for negative revenue growth in Fy23 as early as Oct’22 (link). Going into the 4Q23 earnings call, the stock hit our previous PT of $170. After calling for downside to investor expectations all through Fy23, we turned the corner in mid-Dec’23 into what we felt was excessive investor pessimism (link), and raised our PT to $220.
While an overall slowdown in iPhone sales weighs on sentiment, investor expectation of a challenge from Huawei’s Mate60 tipped investors into outright depression. We took a contrarian view. We felt investors were being overly pessimistic in their China assessment. Ex-China too, we felt iPhone had the opportunity to gain share from Samsung’s aging Galaxy S23. In recent weeks, reports from market research firms have largely confirmed our assessment of the Dec quarter. Rather than lose share, iPhones seems to have gained share in China and also worldwide as Huawei and Samsung failed to live up to expectations.
As for the March quarter and for the full year, investor sentiment seems just as low on continued China fears and unconfirmed reports of component cuts. However, our checks show that TSM raised 3nm node utilization to full capacity. As there are no major 3nm customers outside of Apple, we think Apple may have raised 3nm wafer starts at TSM, potentially a positive for iPhones and Macs.
Fears related to iPhone weakness need to be addressed by Apple management. We believe they will. But that alone will not do it. Apple needs to demonstrate itself as a full participant in the AI revolution, not just in applications as many Apple bulls are hoping for, but in Apple silicon for running full-fledged LLM workloads on smartphone and PCs. The M2 and M3 chips designed for Macs show tantalizing promise. It is unclear to us as to what is management’s strategy for AI. But the potential for a significant new growth vector exists.
We model FQ1 at $120bn/$2.16 vs. consensus at $118bn/$2.11. We model FY24 at $391bn/$6.4, revenue up 2% vs. consensus at $395bn/$6.62, revenue up 3%. We are long into print.
iPhones in FQ1: In our Dec note, we raised our iPhone expectations to up 7% y/y vs. consensus up 4% on higher-than-expected strength in China and worldwide.
Going by reports from market research firms, iPhone shipments in CQ4 may have worked out largely inline with our thinking – no share loss in China and worldwide. According to market research firms, IPhones gained share in China, despite the Huawei challenge. Other China brands seem to have lot share to Huawei. This implies that Huawei, while still an important player in China, is largely limited to mid-range smartphone models. Worldwide too, a similar story seems to have played out, for the quarter and for the full CY. iPhones gained share from Samsung to become #1 worldwide by unit volume.
How did iPhones manage to get on top of the competition? 1) On the strength of its balance sheet, we believe Apple has been able to out-finance its competition by offering easy terms to telcos and distributors. Samsung’s loss of share we believe has been due to constrained finances and their inability to help telcos stock up shelves. 2) As for Huawei, we believe poor yields at SMIC 7nm process constrained the supply of the Mate60 and prevented Huawei from following through with the initial success of its launch in early Oct’23. The challenge from Mate60 to iPhones may have been transitory
Apple may have a silicon solution for AI: Whereas Apple bulls have been sending up trial balloons in the form of hopes for Gen AI applications in next gen iPhones, we think Apple’s position in AI could be far more fundamental.
After a decade of integrating its operating system with its cpu, gpu, neural-network and memory, we think Apple may well have the single best silicon capable of performing inference workloads at the lowest power envelop. If not all inference workloads, at the very least transformer functions.
The AI investor base mostly avoids considering the huge cost of running today’s mainstream AI silicon on liquid-cooled GPUs. We believe there is a role for today’s large-cluster liquid-cooled GPU servers in specialized functions - LLM training and large-scale inference workloads. However, for mass market inference workloads and for training SLMs, we think the AI marketplace needs low-cost, air-cooled traditional CPUs. We think the yet-to-launch M3 Ultra at 3nm or the M-series follow-on at 2nm may be that long sought-after chip. And if so, Apple stock has a long way to go.
We called out this possibility going into the M3 launch (link) back in Oct’23. While Apple has avoided referring to its AI plans at the launch event, company management has mentioned in passing, the silicon’s potential for transformer workloads. The M3 Pro supports 126GB of ‘unified memory’ (the gpu shares address space with the CPU) vs. NVDA H100’s 80GB of ‘discrete’ memory (which is more power hungry than unified architecture). The M3 Ultra, yet to be launched, likely features 2x126GB (vs. AMD’s MI300X at 192GB), spread across two chips, thus providing massive memory for loading LLMs.
And it is not just in the hardware specifications. Apple silicon is fully integrated with Apple’s software stack. Also, the open-source community ported PyTorch onto
MacOS. Whereas Intel and AMD talk about ‘AI PC’, Apple’s M2 Mac Pro has already been in use as an AI PC for over a year. Meanwhile, Microsoft Windows OS is yet to provide hooks into x86 PC solutions due to lack of Windows’ integration with Intel’s OpenVino or AMD’s ROCM.
Net/Net:
- For the stock to shed investor pessimism, Apple management needs to demonstrate strength in its core iPhone business. We think it can.
- While very much out of character for Apple’s management style, Apple needs to give investors an advance peek into its AI plans, especially as it has under its belt, Apple silicon which has the potential to beat the AI incumbents in mass market AI workloads.
- We are long into print.