Alphabet Accelerates Its AI Investment

Alphabet. The Google parent raised the top end of its CapEx plan for this year, to $205,000,000,000. There's cloud growth. There's Gemini engagement. All there, but the discipline on spending is a bit of a concern. Joining us is Eric Sheridan, Goldman Sachs, co business unit leader of the technology, media, and telecommunications group in global investment research. He says Alphabet is well positioned to benefit from the growing demand for AI across both consumer and enterprise markets and reiterates a buy rating while lowering its 12 price twelve month price target to 200 to $435 from 440. Eric, welcome to the program. Not a surprise, really, that that they would raise the the the CapEx expectation for this year, but the the the reaction to that seems a bit severe. They did swing to negative free cash flow for the first time as a public company. Was that it? It they did swing to negative free cash flow, and I think there's a mixture of signals versus noise in this print. The long term signals are search is a stable business. YouTube continues to gain momentum across the broader media landscape, and Google Cloud revenue continues to reaccelerate and will likely reaccelerate in an outsized way for most of the next one to two years. They made some decisions short term to raise CapEx and strike deals for third party compute that are impacting OpEx that are all about closing some of the demand versus supply gap that exists around compute today because they didn't wanna slow growth and disappoint external clients. Now we certainly are cognizant that in this market environment, over indexing through investment and under indexing to short term return isn't being rewarded. But we think Alphabet is making the right long term decisions, when angling against the larger market opportunity for AI over the next couple of years. We got a lot of stats. Stats about Gemini. Stats about enterprise adoption. The cloud unit's growing 82% year on year. For me, the really simple question is, is Google doing well at AI? They are still an AI winner in our, view. The market took a step back from that view overnight. The delays around 3.5 pro and the fact that they no longer have a foundational model that sits right at the frontier of performance and benchmarking has definitely taken a little bit of the shine off the AI winner theme. What Sundar Pichai talked about last night is that they're likely gonna have to wait for Gemini four to be back at the frontier of performance with AI models. Two points. I think, generally, when you look at access to chips, data, the ability to train these models, we think Alphabet is as well positioned as anyone, but there can be short term gaps that open up between performance and training runs around these models. More importantly, we think the world is broadly shifting from token maxing, to token optimizing. And some of these other models that are around speed and efficiency, including some of the flash models that they've released, will allow them to remain very competitive for, incremental workloads. But investors wanna see companies spending this amount of money, then they want them at the frontier of model performance. They might have to wait a few months for that with Alphabet. That is a conversation I've been having with CEOs all across the stack recently, the difference between token maxing and token optimizing. If if Google nails that, where does it show up? Right? I think you write write at the top of your note the cloud revenue estimates now revised even higher. Is that is that still the metric to follow on how they are being used out in the real world? Yes. And we believe companies like Alphabet and next week will hear this from Amazon that are going into enterprise customers and saying, we're gonna help you optimize your spend. It's not gonna be about just buying tokens no matter what the cost from a single model, but buying a wider array of tokens from a wider array of models is generally where this landscape is going. We wrote a note a couple of months ago about where the AI economy would go over the longer term, and I think what got lost in that note, Ed, would be the fact that to drive utility and to drive token, growth, you need deflation. Every technology compute shift I've ever covered and analyzed has unit growth that comes with deflation because you have to incent adoption rates, and we don't think the AI economy is gonna be any different than that. We don't have time for this, but China's focused on lowering dollar per token. America's focused on the quality of the token. I just note very quickly that the other hyperscalers as by association markedly lower today.