Showing posts sorted by date for query platform business model. Sort by relevance Show all posts
Showing posts sorted by date for query platform business model. Sort by relevance Show all posts

Sunday, November 24, 2024

DIY and Licensed GenAI Patterns Will Continue

As always with software, firms are going to opt for a mix of "do it yourself" owned technology and licensed third party offerings. That might be especially be true for generative artificial intelligence apps that often work best when customized for particular firms, industries or functions.


Apple’s use of ChatGPT for iOS 18 was always expected, according to Apple executives, to be temporary. That would fit with Apple’s history, as the firm prefers to create and own its whole software stack. 


The agreement with OpenAI allowed Apple to integrate ChatGPT intoiPhones, iPads, and Macs. This partnership allows Apple to offer ChatGPT-powered capabilities through Siri and other iOS features. 


The obvious end user experience will happen when Siri cannot answer a question and will pass the user along to ChatGPT. 


But Apple also has been developing its own generative AI chatbot, and Apple Intelligence will ultimately use the homegrown technology, most would assume. The switch to Apple’s own chatbot is expected to appear with iOS 19 in 2026.  


That bifurcated approach might be used by other firms as well. Microsoft, for example, has a right to about 49 percent of the profits from OpenAI’s for-profit business.


But Microsoft also is developing its own generative AI systems, including Azure AI Studio, a platform for building, evaluating, and deploying generative AI solutions and custom copilots.


Azure Machine Learning and custom generative AI models also are in development, including a generative AI chatbot.


The point is that both “do it yourself” and “license the technology” approaches will be used by many entities. 


Company

Own Generative AI Development

Licensed Third-Party AI Solutions

Microsoft

Azure AI Studio, Azure Machine Learning

OpenAI's GPT models

Adobe

Adobe Firefly, Generative AI tools in Creative Cloud

Unknown third-party partnerships

Salesforce

Einstein GPT

OpenAI's language models

Goldman Sachs

Internal coding tool, documentation automation platform

Unknown third-party partnerships

BMW

Enterprise data analysis AI, customer service AI

Unknown third-party partnerships

Google

Google AI (including PaLM 2)

Anthropic (for Claude)

Meta

LLaMA (large language model)

Hugging Face

IBM

Watsonx (AI platform)

Various third-party models


Saturday, November 16, 2024

FTC Opens New Inquiry Into Microsoft Cloud Computng Practices

The U.S. Federal Trade Commission plans an investigation into Microsoft cloud computing practices, apparently licensing practices that tend to restrict customer ability to move data to other platforms and suppliers. 


The move probably illustrates for many the difficulties of regulating “competition” in the computing industry, when it  is characterized by complex and rapidly changing technologies. 


The fast pace of innovation can quickly make today’s possible problems vanish, only to be replaced by new issues. 


Some might argue that the Telecommunications Act of 1996, the first major revision of telecom  policy since 1934, focused on voice services competition, nearly completely missed the looming impact of the internet on the whole business. The Act assumed the key issue was competition for voice services, which rapidly ceased to be a relevant issue. 


Also, it often is difficult to define a market, as contestants often compete in multiple industry segments arguably related to each other. 


Perhaps more difficult is the growing importance of network effects. Many product markets now have a strong winner-take-all (or “winner take most”) character, based largely on natural economies of scale created by network effects (a product or service becomes more valuable as more people use it). 


For older voice networks, the value grew as the ability to call anybody (not just people in your town) grew. If all your friends and business associates are on one social network, it has the most value for you. 


If nearly all the things you buy are available on one e-commerce platform, it has the greatest value for you. If one payment method is accepted by virtually all the merchants you buy from, it has a strong network effect. 


The point is that in such markets, legitimate competition will tend to produce concentrated markets, without any anticompetitive behavior. 


The separate matter of how much such leadership helps propel leaders in one area to dominance in new or different markets often is the bigger issue for regulators. 


Also, assessing the existence of consumer harm is much harder when products are given away for free. The whole notion of “consumer harm” is hard to assess when there is no “price” paid by any user, and when size itself might be key to providing products “for free.”


Traditional antitrust analysis often focuses on price effects. The absence of monetary prices makes it difficult to measure direct consumer harm. As a result, all sorts of “non-price” effects have to be looked at, and that is rather more subjective.


Those effects might include product quality, innovation, privacy, and user experience or switching costs, all of which are necessarily subjective to a large extent. 


Of course, the move comes as a change of administration approaches, and many believe at least some regulatory action against hyperscalers could abate, though most assume oversight will remain elevated. 


In November 2023, the FTC began assessing cloud providers' practices in four key areas: competition, single points of failure, security, and artificial intelligence. 


The Microsoft inquiry is the latest of such moves. 


In January 2024, the FTC launched a formal inquiry into generative AI investments and partnerships, focusing on Alphabet, Amazon, Anthropic, Microsoft and OpenAI licensing terms and practices that might harm competition. 


Among other matters, the FTC is looking at the competitive impact of huge investments by hyperscalers into AI model firms, such as Microsoft's investment in OpenAI, and Google's and Amazon's ownership interests in Anthropic. 


At least part of the issue is hyperscaler ability to leverage their cloud computing leadership into new AI markets, the same sort of issue officials have targeted in the past. For the FTC, the issue often is preventing leading firms from leveraging existing market power to gain leadership of new markets as well. 


The Federal Trade Commission (FTC) and Department of Justice have histories of taking actions to protect competition in the computing industry, particularly focusing on preventing market leaders from leveraging their dominance in one area to gain unfair advantages in new or adjacent markets. 


The Microsoft Antitrust Case (1990s-2000s)by the Department of Justice focused on Microsoft's bundling of Internet Explorer with Windows, leveraging its operating system dominance to gain market share in web browsers. This resulted in a settlement in 2001, imposing restrictions on Microsoft's business practices.


The FTC’s Intel Antitrust Case (2009-2010) centered on the accusation that Intel used its dominant market position in central processing units s to stifle competition in the graphics processing unit  market. The case was settled in 2010, with Intel agreeing to modify its business practices.


The agency also opened an investigation into Google Search (2011-2013), asking whether Google was leveraging its search engine dominance to promote its own services unfairly.The FTC closed the investigation without major action.


The FTC also filed an antitrust lawsuit against Facebook (Meta) in 2020 alleging that Facebook's acquisitions of Instagram and WhatsApp were part of a strategy to maintain its social networking monopoly.


The Commission also investigated Amazon's MGM acquisition (2021-2022), focused on how Amazon might leverage the acquisition; its e-commerce and streaming dominance in the entertainment industry to reduce competition. The agency ultimately did not block the deal.  


Cloud computing practices also are under examination by the European Union and U.K. Competition and Markets Authority.


Friday, November 15, 2024

"Winner Takes All" or "Winner Takes Most" Market Structure for LLMs?

According to the Chatbot Arena leaderboard, a platform for evaluating AI determined by user votes, Gemini’s latest update--Gemini-Exp-1114--ranks best among large language models. 


It is worth noting that leaders change somewhat frequently, with the top-five models presently all versions of OpenAI or Google models. Perhaps notably, Grok-2-08-13 ranks sixth. 


source: Chatbot Arena 


It might also be worth noting that OpenAI's models (such as GPT-4) and Anthropic's Claude models have consistently ranked near the top of the leaderboard.


And leadership seems to have changed since the spring of 2023. Consider the leaderboard published by LMsys in the spring of 2023. ChatGPT 3.5 launched in late 2022 and seems to have been in the top five of the Arena leaderboard since its inception in the spring of 2023. 


source: lmsys.org 


Eventually, business history would suggest leadership of the market will condense, as have other technology markets. So. the LLM market is likely to evolve into a structure characterized by oligopolistic competition among a few major players, complemented by a range of specialized providers catering to specific industries or use cases.


In 2023 five LLMs had more than 88 percent market share. That leadership group might condense further, eventually. 


On the other hand, the room for specialized platforms might remain. How many of us would not see any way for OpenAI, Google, Microsoft, Meta, Amazon, Apple and IBM, for example, to continue as operators of domain-specific LLMs, no matter what happens ;with the broader market?


And who might doubt that specialized industry-specific platforms could number between 10 and 20 (catering to different sectors like healthcare, finance, legal)?


And of the leaders, might open-source initiatives include three to five significant contributors? 


Might AI-as-a-Service providers number 10 to 15 “significant” players, even if the top five or so positions include AWS, Google Cloud, Azure, Meta and Amazon? 


Also, if history is instructive, could there not exist five to 10 Integration and orchestration platforms as well?


The issue is what “winner takes all” will mean in the LLM ecosystem and platforms markets. Current examples include just one or perhaps two leaders in existing markets, which is more on the “operating system” model. On the other hand, most of us would have a hard time deciding on less than perhaps four leading LLMs for some time to come. 


And some structural differences between existing technology market structures and LLMs come to mind. Unlike the operating system market, LLMs don't require the same level of user lock-in or hardware integration. So the "two-leaders” pattern might not emerge. 


Roughly the same argument might be made about the e-commerce or search market structures, where one leader tends to emerge. The competitiveness of existing LLMs, with continual upgrades, tends to dispel the notion that any single provider will achieve technological superiority on a sustainable basis. 


LLMs also lack the network effects and user-generated content central to social media platforms. So it is possible the one leader model might not develop. Right now, differences between leading platforms are relatively subtle. 


So the likely direction is “winner take most” more than “winner take all.” Even if network effects are not so strong, high capital intensity, branding and trust issues and the ability to vertically integrate with existing ecosystems (Google, Apple, Microsoft, Meta) create enormous advantages for a few contenders. 


At least for the moment, “winner take all” is hard to see. A still-oligopolistic, but “winner take most” structure with a handful of leaders might be more plausible. 


Tuesday, October 29, 2024

Generative AI: Why It is All or Nothing

Suppliers of generative artificial intelligence frontier models (advanced artificial intelligence systems that push the boundaries of what generative AI can achieve) are under scrutiny for capital investments and pathways to revenue, given the explosion in investment that started in 2023. 


Risk is very high, but so is the potential reward. And even if most contenders eventually will lose their bets, the winner is epected to dominate the market.


Some idea of the ramp up of investment can be seen in venture capital investments alone, and excluding investments by leading firms such as Google, Microsoft, Meta, Apple and Amazon, to support generative and other forms of AI. 


Year

Estimated VC Investment (Billions USD)

2020

0.2

2021

1.2

2022

2.7

2023

22.4

2024 (projected)

30+


Those figures do not include any sums spent by enterprises, software or hardware firms to create AI features, apps or platforms. Nor doe those amounts include investment by hyperscale app providers or  device firms to add AI features to their existing products. 

source: Our World in Data 


Estimating all AI capex by the hyperscale app and device providers is difficult, as many types of capex as well as operating expense (personnel, for example) are involved. Often, all we know is aggregate capex. 


Company

Time Period

AI-Related Capital Expenditure

Combined (Apple, Amazon, Meta, Microsoft, Alphabet)

Q2 2023*

$59 billion

Combined (Apple, Amazon, Meta, Microsoft, Alphabet)

2024 (forecast)

$215 billion**

* total capex, not just AI capex

** source: New York Times 


And most of the revenue tied to AI investments is indirect, often contributing to cloud computing as a service revenues, for example. 


Company

AI Model/Product

Reported Financial Outcome

Microsoft

Azure OpenAI Service

$10 billion investment in OpenAI, contributing to 27% growth in Intelligent Cloud revenue in Q4 20232

Google (Alphabet)

Various AI products

28% year-over-year increase in Google Cloud revenue in Q2 2023, partly attributed to AI offerings3

IBM

Watson AI platform

$1 billion in annual revenue from Watson and AI-related services as of 20214

Palantir

AIP (Artificial Intelligence Platform)

31% year-over-year revenue growth in Q2 2023, with AI driving new customer acquisition5


We should expect to see continuing reporting of AI-attributed revenues by firms making big AI investments, even as we have to expect an awful lot of somewhat indirect approaches to identifying that revenue growth. 


Just as important, if generative AI winds up being a “winner take all” business, as most other computing segments have been, there will be no prize for third best. 


We have already seen that pattern in many other computing markets. The leader in search has 91 percent market share. The browser leader has 65 percent share. The mobile operating system leader has 72 percent share. The U.S. ride-hailing leader has 68 percent share. 


Market

Dominant Player

Market Share

Runner-up

Market Share

Search Engines

Google

91.9%

Bing

3.0%

Desktop Browsers

Chrome

65.72%

Safari

18.22%

Mobile Browsers

Chrome

66.17%

Safari

23.28%

E-commerce

Amazon

37.8% (US)

Walmart

6.3% (US)

Video Streaming

YouTube

2.5B users

Netflix

231M subscribers

Music Streaming

Spotify

31%

Apple Music

15%

Ride-hailing (US)

Uber

68%

Lyft

32%

Cloud Services

AWS

32%

Azure

22%

Mobile OS

Android

71.8%

iOS

27.6%


So if generative AI follows that pattern, the strategic choices are “don’t play” or “play to be number one.” That implies most of the investment, in most of the firms, will be stranded or lost, eventually. But a whole ecosystem might be built around the leader.


DIY and Licensed GenAI Patterns Will Continue

As always with software, firms are going to opt for a mix of "do it yourself" owned technology and licensed third party offerings....