Parallel Bets, Microsoft, and AI Strategies
The first IBM Personal Computer debuted in August 1981. It was also the first computer to use Microsoft’s MS-DOS operating system (technically, a nearly identical copy known as IBM PC DOS, also known as PC DOS or IBM DOS). At the time, Unix held a roughly 55% share of the market for operating systems running on personally owned devices; the rest was split between Xerox’s Alto (launched in 1973 for proto-PCs), CP/M (1974, for microcomputers), TRSDOS (1977, also microcomputers), Commodore KERNEL (1977, home computers), Atari DOS (1979, home computers), Apple’s DOS (1978, microcomputers), and SOS (1980, microcomputers). By 1985, MS-DOS had amassed more than 50% market share. By 1989, it held just under 90%.
Despite Microsoft’s success throughout the 1980s, the company’s founder and CEO, Bill Gates, suspected that DOS was approaching its end. Each year, there were more people who owned a PC, even more who used one as part of work, and leisure usage was growing rapidly, as was the number of PC developers. These changes were great for the market, and Microsoft was a primary beneficiary of this market, but these changes changed the market, too, which could jeopardize Microsoft’s position. There was also growing evidence of competitive and structural changes in the marketplace. Apple’s line of graphical user interfaces (GUI) computers, which debuted with the 1983 Lisa PC, were increasingly popular and served as an obvious contrast to the command-line interfaces of DOS. In 1984, MIT had kicked off a project to build the Window System for Unix and Unix-like OS. In 1985, IBM had begun development of OS/2, which Microsoft signed on to co-develop, but in contrast to IBM DOS, OS/2 was spearheaded by IBM, not Microsoft, and designed to sell IBM PCs and hardware, not foster the PC hardware ecosystem or Microsoft’s Windows. In 1986, a group that spanned Sun, AT&T, and Xerox (which had pioneered the GUI interface) began working on a GUI specification of Unix (OPEN LOOK, which later progressed into OpenWindows) that would make the OS more consumer-friendly. In 1988, a similar collective including IBM, HP, Compaq, and DEC formed to build Digital Unix (a.k.a. Tru64 UNIX).
Taken together, it was not hard to predict that platform-level changes might soon occur. If so, secondary and tertiary changes were inevitable, too. In other words, Microsoft faced not just the prospect of new competitors, some of which were current licensors, but also potential disruption in its OS licensing business model, all of which threatened its growth, investment, and product strategy and might alter industry profit pools as well.
To manage this uncertainty, Microsoft undertook a portfolio of bets throughout the 1980s and early 1990s that were often competitive with one another (or had competing premises) but collectively replicated the diversity, unpredictability, and dynamism of the market at large, thereby maximizing Microsoft’s odds of success in any future state. These bets were roughly as follows:
Continue development of MS-DOS;
Collaborate with the many companies working on UNIX, namely through the ongoing development of its Xenix version of Unix (1980–89);
Commence major investment(s) in Windows, a GUI OS (development started 1983, with Windows 1.0 shipping in 1985 and 3.0 in 1991)
Form partnership with IBM to develop OS/2 (1985–);
Purchase 20% stake in Santa Cruz Operation, the largest seller of Unix systems on PCs (1989); and
Develop suite of applications (namely Microsoft Office, 1990–) that could operate across operating systems which Microsoft might have no ownership (or influence) over
While Microsoft had many bets, it still had its preferred one: Winning the personal computing market, via a licensed OS. They wanted the OS that won out to be Windows. As Microsoft had been successful with this core endeavor, it also could have proceeded with that sole bet.
Yet a live-or-die bet wasn’t necessary, and indeed, some of those failed bets failed precisely because Windows 3.0, which launched in 1990, was so successful. One such example is OS/2. This jointly developed OS was always challenged by the dueling priorities of IBM and Microsoft, but following the breakout of Windows 3.0, the partnership between the two companies became impossible to sustain. IBM took sole ownership in 1992 (the final release was in 2001). By 1993, the Unix ecosystem had determined that a fully unified Unix was needed to combat Windows, prompting the Common Open Software Environment initiative, or COSE (founded by the Santa Cruz Operation, Univel, Unix Systems Laboratories, Sun, HP, and IBM). These hopes were dashed by the blockbuster Windows 95, which Santa Cruz, HP, and IBM had little choice but to support, especially as rivals such as Dell picked up market share. It’s likely that Microsoft also had picked up extensive knowledge from its various bets, such as the rationale behind various technical and interface-related decisions across O/2, Unix, and so forth, and used this knowledge to strengthen relationships with key industry partners, most notably those who manufactured and distributed PCs.
Even as Windows secured the market in the early 1990s, Microsoft remained paranoid about the right product offering. Prior to the launch of Windows 95, the company released Microsoft Bob, which was intended to be an even more consumer-friendly GUI for novice computer users, but it failed miserably and was quickly killed the following year.
In 1995, Gates wrote his famous “Internet Tidal Wave memo,” in which he argued that the Internet was not just a critical new frontier for Microsoft, but one that might empower the company’s OS competitors or even displace the role of the OS altogether:
I assign the Internet the highest level of importance… I want to make clear that our focus on the Internet is crucial to every part of our business… The Internet is the most important single development to come along since the IBM PC was introduced in 1981. It is even more important than the arrival of the graphical user interface (GUI). The PC analogy is apt for many reasons. The PC wasn’t perfect. Aspects of the PC were arbitrary or even poor. However a phenomena grew up around the IBM PC that made it a key element of everything that would happen for the next 15 years. Companies that tried to fight the PC standard often had good reasons for doing so but they failed because the phenomena overcame any weaknesses that resisters identified… IBM [also] includes Internet connection through its network in OS/2 and promotes that as a key feature. Some competitors have a much deeper involvement in the Internet than Microsoft. All UNIX vendors are benefiting from the Internet since the default server is still a UNIX box and not Windows… Sun has exploited this quite effectively… and [is] very involved in evolving the Internet to stay away from Microsoft… [Moreover], a new competitor “born” on the Internet is Netscape.
Gates’s memo led to a flood of investment in the company’s digital efforts, from Internet Explorer (1995) to the MSN portal and search engine (1995), the $400 million acquisition of Hotmail (1997; $760MM in 2023 dollars), Messenger (1999); the list goes on. The company also remained diversified in its traditional OS bets, purchasing a 5% stake in Apple in 1997 for $150MM; the deal also involved Apple making Internet Explorer the Mac’s default browser).
The Waves Roll On
As we review Microsoft’s parallel bets in 2023, a few takeaways are clear. Eventually, Microsoft lost most of the market in consumer internet services, such as search and web portals, email, messaging, and identity. Several of these markets are more lucrative than operating system licensing. While Microsoft successfully boxed out Unix, Linux, an open-source OS that launched in 1991, has thrived (though differently than its creator once imagined). Today, more than 95% of the 150–200 million operating servers run Linux, and Android, the most used operating system in history, is also based on the OS. Microsoft’s modern-day strength in productivity software and other horizontal/cross-platform technology, such as Azure, has also enabled the company to thrive even after its OS was displaced—although this displacement was not at the hands of PC-related competitors in the ’80s or ’90s but rather two mobile competitors of the 2000s and 2010s, iOS and Android. It also turned out that mobile computers would become than two-thirds of the market, meaning that Microsoft could no longer amass more than a one-third share of the market.
Crucially, Microsoft’s displacement in the mobile market resulted from undiversified thesis errors. In his infamous January 2007 CNBC interview, Microsoft CEO Steve Ballmer laughs at the prospects of the just-announced iPhone, citing its high price and lack of a keyboard. Popular recollections of this interview typically ends here, but Ballmer’s full report is more generous, saying “it may sell very well,” after which he explains why Microsoft’s strategy is superior. It was the foundational theses of Microsoft’s mobile strategy, which rested on concepts that were shared by most of the proto-smartphone, most notably BlackBerry and Palm, that doomed the company’s efforts. Smartphones should be $100–$200, not $500 or more; they should focus on business users not consumers; they should feature a keyboard; data usage should be minimized so as to protect scarce network bandwidth; batteries should last for days, not hours; fall damage should be minimal, rather than device-wrecking. Though these bets were likely “right” in the late 1990s and early 2000s, they proved wrong over time.
Microsoft also continued to apply its PC-era business model, rather than diversify or really even test other hypotheses. When MS-DOS debuted, there were already a handful of computer manufacturers, most of which supported multiple operating systems (and some consumers chose to install their own at a later time, anyway). Microsoft grew its share of the market not by competing with these OEMs, by producing its own PC, or by partnering exclusively with IBM but by licensing Windows for $50–$100 to any or all manufacturers. For the most part, Apple limited its OS to its own hardware, the first iteration of which debuted in 1976 (five years before the IBM PC or MS-DOS). Some in the tech community believed the company produced better “PCs,” but its vertically integrated approach led to higher prices and constrained distribution, and the company struggled mightily to overcome the large and incredibly developed PC and Windows ecosystem. Microsoft, of course, knew that there were potential benefits from producing its own first-party hardware, but doing so would mean competing with its many competitors, most of which could (and would) renew their own OS investments or adopt a rival such as Unix.
When it came to the early “smartphones” of the late 1990s and early 2000s, Microsoft’s approach (Windows Compact and Windows Mobile) was similar to its PC strategy, though it charged a more modest $10–$25 per device. The challenge here was that mobile computers were comparatively harder to build than PCs due to the vastly different constraints on size, power usage, heat generation, and the like. But in the mobile form factor, which was so nascent that fewer than 60MM devices had been sold over fifteen years, Apple’s vertically integrated approach led to a substantially better device. Unlike Microsoft, Apple built a truly mobile-native OS, rather than porting over most of the design principles of its Mac OS (Windows Mobile literally had a taskbar and Start menu button). The result was a device that totally outclassed Windows-based smartphones and effectively downgraded the other would-be smartphones to proto-smartphones.
Independent smartphone manufacturers rushed to catch-up to Apple’s hardware designs, and were somewhat successful, while also differentiating on other designed-related features (e.g., offering better cameras or larger screens). However, these OEMs fell well short on the OS and ecosystem side, thereby preserving the PC-era opportunity for an independent OS provider. Here however, Microsoft’s Windows Mobile lost to Google’s Android, which was not just a more modern OS (mobile-native, touch- and consumer-focused, and so on) but free to device makers. In fact, Google offered OEMs (and wireless carriers) a share of Android-related search and Google Play app store revenues. In effect, the OS business model had inverted from direct monetization (Microsoft’s bread and butter) to the sale of profitable hardware (Apple’s main business line and one in which Microsoft lacked any business at all) and software/services bundles (where Google thrived and Apple was rapidly growing). By the time Microsoft pivoted, launching its free-to-license Windows Phone OS in 2010 and an exclusive partnership with Nokia a year later (Microsoft ended up buying it in 2014), it was too late. Had Microsoft been earlier in its shift, or just partly wrong about smartphones when the iPhone launched, its mobile OS might have survived. (Incidentally, MacOS, iOS, and Android are all UNIX-based).
Parallel Lessons
I think a lot about parallel bets when it comes to AI—especially given that Microsoft has undertaken a wide array of strategies, several which compete with one another or otherwise seem conflicting.
Over the past half century, Microsoft has spent tens of billions in various areas of AI, in addition to billions more acquiring leaders in natural language processing (including $19.7 billion on Nuance Communications in April 2021). By the end of 2022, Microsoft employed more than 1,500 researchers focused on internally developed AI. Beginning in 2019, however, Microsoft also invested roughly $3 billion in OpenAI, which had been founded in December 2015. Microsoft also granted the startup with free access to its Bing search database, which OpenAI then used to train its generative pre-trained transformer (GPT) models.
By December of 2022, the models developed by OpenAI’s 250-person team had surpassed those of Microsoft Research such that Microsoft CEO Satya Nadell was demanding to know “Why do we have Microsoft Research at all?” Shortly thereafter, Nadella spent $10 billion to buy 49% of OpenAI, which provided Microsoft with broad rights to incorporate its technologies into Microsoft’s own offerings, as well as 75% of OpenAI’s profits until that $10 billion is repaid, and 49% thereafter (to an unknown cap).
Despite its equity, data, commercial, and royalties-related deals with OpenAI, Microsoft continues to invest in its own fully proprietary GPT and LLM (large language model) approaches—and directly compete with OpenAI, too. According to The Information, Microsoft is working to build its own models that can be substituted for those of OpenAI and “may not perform as well . . . [but] costs far less to operate.” (The Information has since reported that OpenAI’s efforts has abandoned, at least for now, efforts to build its own good-enough-but-much-cheaper model, as early results fell short of the company’s targets).
Microsoft also continues to advertise its ChatGPT-powered Bing as a rival to OpenAI’s ChatGPT and has reportedly made its Bing search database available to other AI startups and companies interested in building their own foundation models. When Meta open-sourced its Llama model—a move that threatened OpenAI’s business model and thus also Microsoft’s potential royalties from said business model—it did so in headline partnership with Microsoft, Meta’s “preferred partner.”
And OpenAI, for that matter, continues to compete back—and empower Microsoft’s own competitors. DuckDuckGo now uses ChatGPT to power its DuckAssist service, while Microsoft’s CRM rival, Salesforce, has used ChatGPT to build its EinsteinGPT, which competes with many of Microsoft Office’s ChatGPT-based features.
Parallel bet strategies have several core advantages, from increasing corporate optionality (especially when it comes to acquisitions) to covering strategic bases, maximizing learnings, and (potentially) neutralizing (or at least moderating the risk of) any potential competitors. But there are costs. More money tends to be spent, but each bet tends to receive less funding than they might have under a more focused approach, which can constrain the would-be “winners.” Overseeing many bets can also lead to mixed signals on what is (and is not) the “right bet” and harm internal morale. Few teams feel good about competing against their colleagues. Worse still, these teams typically hate when their corporate parent funds external competitors that, with a little more funding and support, they might been able to beat but now threaten to put them out of a job.
In the context of Microsoft, it’s not hard to “what if” its parallel bets strategy. What if Microsoft had bought 49% of OpenAI earlier, if not the entire company, rather than hedging on the startup and other internal investments? Or at least formed a (more exclusive) commercial deal around the time of GPT-2 or GPT-3, back when OpenAI’s lead was more modest and its leverage similarly smaller, too? Perhaps with more money and attention and a longer competitive runway, Microsoft’s internal efforts might have surpassed those of OpenAI, rather than helped to make it the market leader. (Incidentally, the Motorola ROKR, also known as the “iTunes Phone,” was a parallel bet of both Motorola and Apple but was a failure for the former and helped drive and accelerate the latter’s iPhone initiative, which then helped destroy Motorola’s market share.)
There are, however, a number of timing-related flaws with those “what ifs.” The sudden maturation of transformer models was not predictable, for example, nor was the fact that OpenAI, rather than Cohere or Anthropic or Hugging Face, would be the market leader. OpenAI might not remain the market leader, and transformer models may eventually be replaced, too. And though powerful, transformer models address only some AI use cases.
Microsoft’s approach also contrasts with that of Amazon. Beginning in 2012, the company bet that the era of the “smart assistant” had begun. Amazon was not alone in that bet; Apple had shipped Siri with iOS a year earlier, with Google deploying its Google Now eight months later. Unlike Apple and Google, Amazon lacked a smartphone, and so the company instead focused on building a suite of lightweight devices that could be placed throughout the home and office and called upon at any moment. By 2016, the Alexa device was shipping tens of millions of units per year, with Amazon founder and CEO Jeff Bezos speaking openly of his hope that it would become the fourth pillar of the company, sitting alongside Amazon Marketplace, Amazon Prime, and Amazon Web Services. By 2020, lifetime device sales were in the hundreds of millions, an order of magnitude higher than those of Google’s Home/Nest devices, with Apple’s competitor, the HomePod, flunking out of the market. Amazon also began to unlock the Alexa software from its first-party hardware, deploying it widely in vehicles, such as those of Ford and Toyota, and even quasi-rivalrous speaker systems like Sonos.
Though Alexa’s topline performance suggested it was or at least could be the winner in “consumer AI,” the platform struggled to overcome core engagement problems. For example, the average consumer didn’t use Alexa very much—and when they did, queries were rudimentary (“What time is it?,” “Set an alarm for 2:05PM,” “Will it rain today?,” “Play Dua Lipa,” etc.). Few developers had produced Alexa apps (“Skills”), and even fewer integrated the platform (or its device) into their products. The result was a device that sold well but generated only abstract value to Amazon—and, if the reports are to be believed, tens of billions in losses. When Amazon began its layoffs in 2022, the Alexa division, which had an estimated 10,000–15,000 employees, was disproportionately affected. As transformer models took off toward the end of 2022, the optics of Amazon’s investments worsened as it became clear its costly bet had been wrong—at least for now. The Information has since reported that Amazon had planned to launch its own transformer LLM (“Bedrock”) in November of 2022 but shelved it after seeing ChatGPT and also that the company had passed on various product partnership and equity proposals from OpenAI, Anthropic, and Cohere dating as far back to 2018.
Throughout 2023, Amazon has reset its AI strategy. Dave Limp, the head of Amazon’s Alexa division, announced he was “retiring.” Amazon repurposed its Bedrock name to launch an AWS marketplace for third-party AI solutions, with the goal that AWS customers would run these products on AWS. In October, Amazon announced a $1.25 billion investment in Anthropic, which had raised several hundred million dollars from Google across two fundraising rounds in January and May, and Amazon announced a framework to invest another $2.75 billion in the company. (A week later, Google responded with a $2 billion follow-on investment in Anthropic.) Amazon continues to develop its own foundational models, too.
Breakfast and Dinner
Parallel bets strategies are best suited to (1) cash-rich companies . . . that are (2) pursuing “must-win” categories” . . . in which (3) their assets and strategies are a good fit . . . but (4) may not be configured correctly . . . and (5) there is a high rate of change . . . and (6) many uncertainties . . . and (7) many players . . . with (8) progress often occurring out of sight. Deployed correctly, a company can cover all of the bases while also neutralizing the existential threat of a new competitor. Parallel bets are therefore likely the right strategy generally for “Big Tech” and during this phase of AI, during which there are many unresolved and interconnected hypotheses.
Will closed or open models be more capable? If closed models are technically superior, will open models nevertheless be considered “superior” on a cost-adjusted basis? What is the trade-off between the quality of a generative AI response and its cost? How does this vary by vertical?
How many of the potential uses of generative AI will result in new companies/applications, rather than new or improved functionality in the products of existing market leaders? Put another way, is the technology or distribution more important? Is there a hybrid model in which users access existing applications, such as PhotoShop or Microsoft Office, but while logging into a third-party AI service, such as OpenAI?
Which AI products or integrations will warrant additional revenue from the user, rather than just be baked into the core product as a new table-stakes feature?
To what extent are the answers to these questions path-dependent, that is, subject to specific decisions by specific companies and the quality of their specific products—as was the case with Meta open-sourcing its Llama 2 LLM). And how, again, do the answers differ by vertical?
Eventually, though, it will be necessary for parallel bets to be winnowed; all strategy is eventually about execution. Note how quickly Microsoft focused its OS strategy on Windows after the success of Windows 3.0 in 1990 (the company was later accused of following an “Embrace, Extend, Extinguish” model where one-time partners would be crushed once emerging markets stabilized). The questions here, of course, are “When,” “How Much,” and “How do you know?”
Microsoft never halted its investments in applications and productivity tools, nor Internet services, and is better off as a result. Sometimes parallel bets lead to growth in new adjacent markets, rather than displace a current one (to that end, Microsoft’s more direct OS-bets were eventually paired). It’s possible that Amazon’s Alexa device footprint will still yet enable the company to regain market leadership. Indeed, OpenAI’s CEO, Sam Altman has confirmed reports that it is considering its own foray into consumer hardware (led, according to rumours, by Apple’s long-time design chief, Jony Ive).
And sitting alongside all of the above considerations is the biggest question: how might the focus on current AI architectures and opportunities distract from the development of artificial general intelligence? John Carmack, who is considered the “father of 3D graphics” due his pioneering work at Id Software, which he co-founded in 1991, and joined Oculus VR as its first CTO in 2013, founded his own AI start-up in 2022, Keen Technologies, which is exclusively focused on developing artificial general intelligence. According to Carmack, the number of [contemporary] “billion-dollar off-ramps” for AI technologies has become a de facto obstacle to achieving true AGI. “There are extremely powerful things possible now in the narrow machine-learning stuff,” Carmack told Dallas Innovates in his first major interview after founding Keen, “[but] it’s not clear those are the necessary steps to get all the way to artificial general intelligence.”
Matthew Ball (@ballmatthew)
