Google's True Moonshot – Stratechery by Ben Thompson

Google’s Horizontal Webs

My first point brings me to the second reason I’m reminded of that Google talk: my presentation was entitled “The Opportunity — and the Enemy.” The opportunity was mobile, the best market the tech industry had ever seen; the enemy was Google itself, which even then was still under-investing in its iOS apps.

In the presentation I highlighted the fact that Google’s apps still didn’t support Force Touch , which Apple had introduced to iOS over a year earlier; to me this reflected the strategic mistake the company made in prioritizing Google Maps on Android, which culminated in Apple making its own mapping service. My point was one I had been making on Stratechery from the beginning : Google was a services company, which meant their optimal strategy was to serve all devices; by favoring Android they were letting the tail wag the dog.

Eight years on, and it’s clear I wasn’t the only one who saw the Maps fiasco as a disaster to be learned from: one of the most interesting revelations from the ongoing DOJ antitrust case against Google was reported by Bloomberg :

It’s a powerful data point, and I think the key to understanding what you might call the Google Aggregator Paradox: if Google wins by being better, then why does it fight so hard for defaults, both for search and, in the case of Android, the Play Store? The answer, I think, is that it is best to not even take the chance of alternative defaults being good enough. This is made easier given the structure of these deals, which are revenue shares, not payments; this does show up on Google’s income statement as Traffic Acquisition Costs (TAC), but from a cash flow perspective it is foregone zero marginal cost revenue. There is no pain of payment, just somewhat lower profitability on zero marginal cost searches.

The bigger cost is increasingly legal: the decision in the DOJ case won’t come down until next year, and Google may very well win; it’s hard to argue that the company ought not be able to bid on Apple’s default search placement if its competitors can (if anything the case demonstrates Apple’s power ).

That’s not Google’s only legal challenge, though: last week the company lost another antitrust case, this time to Epic. I explained why the company lost — while Apple won — in last Tuesday’s Update :

This goes back to the point I made above: Google’s fundamental legal challenge with Android is that it sought to have its cake and eat it too: it wanted all of the shine of open source and all of the reach and network effects of being a horizontal operating system provider and all of the control and profits of Apple, but the only way to do that was to pretty clearly (in my opinion) violate antitrust law.

Google’s Android strategy was, without question, brilliant, particularly when you realize that the ultimate goal was to protect search . By making it “open source”, Google got all of the phone carriers desperate for an iOS alternative on board, ensuring that hated rival Microsoft was not the alternative to Apple as it had been on PCs; a modular approach, though, is inherently more fragmented — and Google didn’t just want an alternative to Apple, they wanted to beat them, particularly in the early days of the smartphone wars — so the company spun a web of contracts and incentives to ensure that Android was only really usable with Google’s services. For this the company was rightly found guilty of antitrust violations in the EU , and now, for similar reasons, in the U.S.

The challenge for Google is that the smartphone market has a lot more friction than search: the company needs to coordinate both OEMs and developers; when it came to search the company could simply take advantage of the openness of the web. This resulted in tension between Google’s nature — being the one-stop shop for information — and the business model of being a horizontal app platform and operating system provider. It’s not dissimilar to the tension the company faces with its Assistant, and in the future with Generative AI: the company wants to simply give you the answer, but how to do that while still making money?

Infrastructure, Data, and Ecosystems

The third reason I remember that weekend in 2015 is it was the same month that Google open-sourced TensorFlow, its machine-learning framework. I thought it was a great move, and wrote in TensorFlow and Monetizing Intellectual Property :

it is excellent

Indeed, as good as TensorFlow might be, I bet it’s the weakest of these three pieces Google needs to truly apply machine learning to all its various business, both those of today and those of the future. Why not, then, leverage the collective knowledge of machine learning experts all over the world to make TensorFlow better? Why not make a move to ensure the machine learning experts of the future grow up with TensorFlow as the default? And why not ensure that the industry’s default machine learning system utilizes standards set in place by Google itself, with a design already suited for Google’s infrastructure?

After all, contra Gates’ 2005 claim, it turns out the value of pure intellectual property is not derived from government-enforced exclusivity, but rather from the complementary pieces that surround that intellectual property which are far more difficult to replicate. Google is betting that its lead in both data and infrastructure are significant and growing, and that’s a far better bet in my mind than an all-too-often futile attempt to derive value from an asset that by its very nature can be replicated endlessly.

In fact, it turned out that TensorFlow was not so excellent — that link I used to support my position in the above excerpt now 404s — and it has been surpassed by Meta’s PyTorch in particular; at Google Cloud Next the company announced a partnership with Nvidia to build out OpenXLA as a compiler of sorts to ensure that output from TensorFlow, Jax, and PyTorch can run on any hardware. This matters for Google because those infrastructure advantages very much exist ; the more important “Tensor” product for Google is its Tensor Processing Unit series of chips, the existence of which make Google uniquely able to scale beyond whatever allocation it can get of Nvidia GPUs.

The importance of TPUs was demonstrated with the announcement of Gemini, Google’s latest AI model; the company claims the “Ultra” variant, which it hasn’t yet released, is better than GPT-4. What is notable is that Gemini was trained and will run inference on TPUs. While there are some questions about the ultimate scalability of TPUs , for now Google is the best positioned to both train and, more importantly, serve generative AI in a cost efficient way.

Then there is data: a recent report in The Information claims that Gemini relies heavily on data from YouTube, and that is not the only proprietary data Google has access to: free Gmail and Google Docs are another massive resource, although it is unclear to what extent Google is using that data, or if it is, for what . At a minimum there is little question that Google has the most accessible repository of Internet data going back a quarter of a century to when Larry Page and Sergey Brin first started crawling the open web from their dorm room.

And so we are back where we started: Google has incredible amounts of data and the best infrastructure, but once again, an unsteady relationship with the broader development community.

Gemini and Seamless AI

The part of the Gemini announcement that drew the most attention did not have anything to do with infrastructure or data: what everyone ended up talking about was the company’s Gemini demo, and the fact it wasn’t representative of Gemini’s actual capabilities. Here’s the demo:

Parmy Olson for Bloomberg Opinion was the first to highlight the problem:

This was obviously a misstep, and a bizarre one at that: as I noted in an Update Google, given its long-term advantages in this space, would have been much better served in being transparent, particularly since it suddenly finds itself with a trustworthiness advantage relative to Microsoft and OpenAI . The goal for the company should be demonstrating competitiveness and competence; a fake demo did the opposite.

And yet, I can understand how the demo came to be; it is getting close to the holy grail of Assistants: an entity with which you can conduct a free-flowing conversation, without the friction of needing to invoke the right incantations or type and read big blocks of text. If Gemini Ultra really is better than GPT-4, or even roughly competitive, than I believe this capability is close. After all, I got a taste of it with GPT-4 and its voice capabilities; from AI, Hardware, and Virtual Reality :

You have obviously been able to chat with ChatGPT via text for many months now; what I only truly appreciated after talking with ChatGPT, though, was just how much work it was to type out questions and read answers. There was, in other words, a human constraint in our conversations that made it feel like I was using a tool; small wonder that the vast majority of my interaction with ChatGPT has been to do some sort of research, or try to remember something on the edge of my memory, too fuzzy to type a clear search term into Google.

Simply talking, though, removed that barrier: I quickly found myself having philosophical discussions including, for example, the nature of virtual reality. It was the discussion itself that provided a clue: virtual reality feels real, but something can only feel real if human constraints are no longer apparent. In the case of conversation, there is no effort required to talk to another human in person, or on the phone; to talk to them via chat is certainly convenient, but there is a much more tangible separation. So it is with ChatGPT.

The problem is that this experience requires a pretty significant suspension of disbelief, because there is too much friction. You have to open the OpenAI app, then you have to set it to voice mode, then you have to wait for it to connect, then every question and answer contains a bit too much lag, and the answers start sounding like blocks of text instead of a conversation. Notice, though, that Google is much better placed than OpenAI to solve all of these challenges:

Google sells its own phones which could be configured to have a conversation UI by default (or with Google’s Pixel Buds). This removes the friction of opening an app and setting a mode. Google also has a fleet of home devices already designed for voice interaction.
Google has massive amounts of infrastructure all over the globe, with the lowest latency and fastest response. This undergirds search today, but it could undergird a new generative AI assistant tomorrow.
Google has access to gobs of data specifically tied to human vocal communication, thanks to YouTube in particular.

In short, the Gemini demo may have been faked, but Google is by far the company best positioned to make it real.

Pixie

There was one other interesting tidbit in The Information article ( emphasis mine ):

an AI assistant called Pixie that will be exclusive to its Pixel devices

The details of Pixie, such as they were, came at the very end:

That Google is readying a super-charged version of the Google Assistant is hardly a surprise; what is notable is the reporting that it will be exclusive to Pixel devices. This is counter to Gemini itself: the Gemini Nano model, which is designed to run on smartphones, will be available to all Android devices with neural processing units like Google’s Tensor G3. That is very much in-line with the post-Maps Google: services are the most valuable when they are available everywhere, and Pixel has a tiny amount of marketshare.

That, by extension, makes me think that the “Pixie exclusive to Pixel” report is mistaken, particularly since I’ve been taken in by this sort of thing before. That Google Assistant piece I quote above — Google and the Limits of Strategy — interpreted the launch of Google Assistant on Pixel devices as evidence that Google was trying to differentiate its own hardware:

What is even more interesting is that Google has also introduced a new business model: the Pixel phone starts at $649, the same as an iPhone, and while it will take time for Google to achieve the level of scale and expertise to match Apple’s profit margins, the fact there is unquestionably a big margin built-in is a profound new direction for the company.

The most fascinating point of all, though, is how Google intends to sell the Pixel: the Google Assistant is, at least for now, exclusive to the first true Google phone, delivering a differentiated experience that, at least theoretically, justifies that margin. It is a strategy that certainly sounds familiar, raising the question of whether this is a replay of the turn-by-turn navigation disaster. Is Google forgetting that they are a horizontal company, one whose business model is designed to maximize reach, not limit it?

My argument was that Google was in fact being logical, for the business model reasons I articulated both in that Article and at the beginning of this year in AI and the Big Five : simply giving the user the right answer threatened the company’s core business model, which meant it made sense to start diversifying into new ones. And then, just a few months later, Google Assistant was available to other Android device makers . It was probably the right decision, for the same reason that the company should have never diminished its iOS maps product in favor of Android.

And yet, all of the reasoning I laid out for making the Google Assistant a differentiator still hold: AI is a threat to Search for all of the same reasons I laid out in 2016, and Google is uniquely positioned to create the best Assistant. The big potential difference with Pixie is that it might actually be good, and a far better differentiator than the Google Assistant. The reason, remember, is not just about Gemini versus GPT-4: it’s because Google actually sells hardware, and has the infrastructure and data to back it up.

Google’s True Moonshot

Google’s collection of moonshots — from Waymo to Google Fiber to Nest to Project Wing to Verily to Project Loon (and the list goes on) — have mostly been science projects that have, for the most part, served to divert profits from Google Search away from shareholders. Waymo is probably the most interesting, but even if it succeeds, it is ultimately a car service rather far afield from Google’s mission statement “to organize the world’s information and make it universally accessible and useful.”

What, though, if the mission statement were the moonshot all along? What if “I’m Feeling Lucky” were not a whimsical button on a spartan home page, but the default way of interacting with all of the world’s information? What if an AI Assistant were so good, and so natural, that anyone with seamless access to it simply used it all the time, without thought?

That, needless to say, is probably the only thing that truly scares Apple. Yes, Android has its advantages to iOS, but they aren’t particularly meaningful to most people, and even for those that care — like me — they are not large enough to give up on iOS’s overall superior user experience. The only thing that drives meaningful shifts in platform marketshare are paradigm shifts, and while I doubt the v1 version of Pixie would be good enough to drive switching from iPhone users, there is at least a path to where it does exactly that.

Of course Pixel would need to win in the Android space first, and that would mean massively more investment by Google in go-to-market activities in particular, from opening stores to subsidizing carriers to ramping up production capacity. It would not be cheap, which is why it’s no surprise that Google hasn’t truly invested to make Pixel a meaningful player in the smartphone space.

The potential payoff, though, is astronomical: a world with Pixie everywhere means a world where Google makes real money from selling hardware, in addition to services for enterprises and schools, and cloud services that leverage Google’s infrastructure to provide the same capabilities to businesses. Moreover, it’s a world where Google is truly integrated: the company already makes the chips, in both its phones and its data centers, it makes the models, and it does it all with the largest collection of data in the world.

This path does away with the messiness of complicated relationships with OEMs and developers and the like, which I think suits the company: Google, at its core, has always been much more like Apple than Microsoft. It wants to control everything, it just needs to do it legally; that the best manifestation of AI is almost certainly dependent on a fully integrated (and thus fully seamless) experience means that the company can both control everything and, if it pulls this gambit off, serve everyone.

The problem is that the risks are massive: Google would not only be risking search revenue, it would also estrange its OEM partners, all while spending astronomical amounts of money. The attempt to be the one AI Assistant that everyone uses — and pays for — is the polar opposite of the conservative approach the company has taken to the Google Aggregator Paradox. Paying for defaults and buying off competitors is the strategy of a company seeking to protect what it has; spending on a bold assault on the most dominant company in tech is to risk it all.

And yet, to simply continue on the current path, folding AI into its current products and selling it via Google Cloud, is a risk of another sort. Google is not going anywhere anytime soon, and Search has a powerful moat in terms of usefulness, defaults, and most critically, user habits; Google Cloud, no matter the scenario, remains an attractive way to monetize Google AI and leverage its infrastructure, and perhaps that will be seen as enough. Where will such a path lead in ten or twenty years, though?

Ultimately, this is a question for leadership, and I thought Daniel Gross’s observation on this point in the recent Stratechery Interview with him and Nat Friedman was insightful:

We definitively know that if the founders weren’t in the story — we could not definitively, but forecast with pretty good odds — that it would just run its course and it would gradually lose market share over time and we’d all sail into a world of agents. However, we saw Sergey Brin as an individual contributor on the Gemini paper and we have friends that work on Gemini and they say that’s not a joke, he is involved day-to-day. He has a tremendous amount of influence, power, and control over Google so if he’s staring at that, together with his co-founder, I do think they could overnight kill a lot of startups, really damage ChatGPT, and just build a great product, but that requires a moment of [founder initiative].

It’s possible, it’s just hard to forecast if they will do it or not. In my head, that is the main question that matters in terms of whether Google adds or loses a zero. I think they’ll build the capability, there’s no doubt about it.

I agree. Google could build the AI to win it all. It’s not guaranteed they would succeed, but the opportunity is there if they want to go for it. That is the path that would be in the nature of the Google that conquered the web twenty years ago, the Google that saw advertising as the easiest way to monetize what was an unbridled pursuit of self-contained technological capability.

The question is if that nature been superceded by one focused on limiting losses and extracting profits; yes, there is still tremendous technological invention, but as Horace Dediu explained on Asymco , that is different than innovation, which means actually making products that move markets. Can Google still do that? Do they want to? Whither Google?

I do still speak at conferences, but last spoke for pay in January 2017 ↩