The answers to questions about financial return from generative artificial intelligence might not have clear answers at the moment, which is worrisome for some observers, especially financial analysts, as well as a relative handful of cloud computing as a service providers who are making huge investments in computing infrastructure.
One way of illustrating the problem is to compare the costs and benefits. Some would argue that the internet succeeded because it was either a low-cost or moderate-cost platform used to solve multiple significant problems for its users.
It was, in other words, a tool that produced significant benefits (cost, value, revenue, ease of use, speed, user experience, comprehensiveness) at reasonable cost.
The internet allowed firms to operate globally at costs not much different than operating locally. It reduced overhead costs; some forms of capital investment; distribution and marketing costs; allowed personalization and customization of products at a higher level.
Used in conjunction with smartphones, it enabled all sorts of peer-to-peer business opportunities in transportation; lodging and so forth.
The issue with generative AI is that it appears to be a high-cost solution that so far addresses relatively lower-cost problems (customer service, content generation).
The best case would be its evolution to become a low-cost solution solving high-cost, high-value problems, or at least a solution for moderate-impact problems at moderate cost.
In many ways, today’s generative AI data infrastructure requirements for startups pose different cost issues for startups and the cloud computing services that undoubtedly will power most of those operations for clients.
As with the earlier development of cloud computing, the burden of GenAI infrastructure capex is shifted from individual firms to the cloud computing outfits.
In 1999, a typical internet startup might spend $500,000 to $2 million on initial infrastructure setup, with ongoing costs of $50,000 to $200,000 per month.
Today, a comparable startup using cloud computing as a service might spend $0 to $10,000 on initial setup, with ongoing costs starting as low as a few hundred dollars per month and scaling with usage.
For most startups, use of a cloud computing service is generally going to be the approach to using large language models.
At a high level, the strategic problem for a cloud services platform is simply earning a financial return on the huge capital investment to create AI compute capabilities requiring huge investments in graphics processor units and other acceleration hardware.
By some estimates, 70 percent of global corporate investment in AI is spent on infrastructure, representing a global spend of about $64.4 billion in computing in 2022 alone.
Dylan Patel and Afzal Ahmad of SemiAnalysis have argued that “deploying current ChatGPT into every search done by Google would require 512,820 A100 HGX servers with a total of 4,102,568 A100 GPUs.”
“The total cost of these servers and networking exceeds $100 billion of capex alone,” they estimate. They also estimate the costs of running ChatGPT inference operations at about $700,000 per day.
So the payback for a cloud computing services provider includes the number of customers buying GenAI as a service; spending by account and the capex and opex to support those operations.
The longer-term issue is development of attractive payback models for firms that use GenAI. If GenAI remains a relatively-costly type of infrastructure, it will be pushed to solve higher-value problems for firms using it.
if GenAi winds up being a relatively-costly platform that can only be used to solve lower-cost problems, success is imperiled. If GenAI does not eventually support solutions that materially increase revenue, success is endangered.
Of course, the payback from infra to support GenAI is not limited to that one use case. The compute platform can be used to support other AI use cases including machine learning as well, one might argue.
So, over time, the infra payback is going to be leveraged in other ways, not merely gains from using GenAI.
Assuming AI in general emerges as the next general-purpose technology, how does the payback model compare to the internet, for example, considering both long-term impact and near-term payback on investments?
Some believe the AI impact on gross domestic product will be hard to measure, for example.
A general-purpose technology (GPT) is a technological advancement that has the potential to significantly impact a society and its economy on a large scale. These technologies are often foundational; have widespread impact; produce long-lasting changes; are broadly applicable and support continued advances.
Past GPTs include the steam engine, electricity and many information technology platforms.
Today, GenAI solves problems of content creation. That might represent a relatively lower-value solution for customer care or marketing. But it might drive lots of value for research-driven endeavors such as pharmaceutical products, such as new drug therapies.
The internet reduced real estate requirements, some other forms of capex (multi-purpose networks instead of single-purpose); distribution and marketing costs; collaboration costs and many forms of latency (email or instant messaging delivery times compared to postal service).
In a similar way, GenAI will have disparate impact on firm functions, and on firms in different industries, depending on how much GenAI can reduce costs or create value for various value chain functions. For example, to the extent that GenAI can affect the new drug discovery process for pharmaceutical firms, which might spend as much as half their effort on research, the impact could be significant.
GenAI could have the greatest impact if it can help the manufacturing process for industrial firms, if possible. It might be logical to conclude that machine vision or machine learning will have more impact than GenAI in industrial processes.
For most industries, impact is likely to come in sales or fulfillment.
So identifiable financial returns from AI will be difficult, for many, in the near term.
No comments:
Post a Comment