Jevons Effect Applies to DeepSeek, Not Nvidia

Many financial analysts and investors are concerned that lower-cost language models such as DeepSeek will reduce demand for high-end graphics processor units, servers and high-performance computing services. The drop in Nvidia market value by about $600 billion after the DeepSeek unveiling of its latest model, which, it is claimed, was developed at a fraction of the cost of other leading generative AI models, attests to that fear. 

Others do not believe a dip in need for high-end Nvidia servers will happen. The Jevons Paradox or Jevons Effect has been cited in this regard. The Jevons Effect, also known as the rebound effect, is the proposition that increases in efficiency in resource use can lead to an increase in the consumption of that resource, rather than a decrease.

So the reasoning is that cheaper AI models and cheaper inferences will lead to much more AI use,and therefore continued demand for Nvidia servers, even high-end machines. Some might point out that the claimed DeepSeek cost advantages should properly mean the Jevons Effect happens to DeepSeek and other models, not the servers running those models. 

So the reduction in demand for Nvidia servers would be indirect. That has not deterred many observers, who believe significantly-lower-cost model training and inference operations are possible (with or without DeepSeek contributions). We already have seen such effects.

source: a16z

 

In principle, some argue, such trends might mean model operators could reduce the need for high-end graphics processing units and other acceleration chips. That remains to be seen. 

Broadly speaking, the history of computing suggests the “lower costs lead to wider usage; more usage; more demand for servers” argument has legs. Computing resource demand always has increased as the cost drops. 

But there also is an argument that lower-cost models and inferences will increase demand for all those products. Paradoxically, lower training and inference costs will stimulate more demand, possibly lead to new use cases 

Also, keep in mind that generative AI is only one form of AI. Perhaps SeepSeek points the way to lower processing costs for GenAI. That does not necessarily mean improvements for general AI or even machine learning. 

One might argue it remains necessary to use high-end processors and accelerators for use cases moving towards artificial general intelligence. 

The Jevons Effect, or Jevons Paradox suggests that an increase in efficiency in resource use can lead to an increase in the consumption of that resource, rather than a decrease, as some might expect, over the long term. 

Industry/Resource	Efficiency Improvement	Observed Increase in Consumption
Coal (Historical Example)	More efficient steam engines in the 19th century	Increased coal consumption as steam engines became more widely used
Oil & Gas	Fuel-efficient car engines	More people drive longer distances, leading to sustained or increased fuel demand
Electricity	Energy-efficient lighting (LEDs, CFLs)	More lighting is used due to lower energy costs, offsetting savings
Computing Power	More efficient processors (Moore’s Law)	Increased use of computing applications and data-intensive services
Water Usage	Efficient irrigation systems	More land is cultivated, increasing total water use
Internet Bandwidth	Faster and cheaper data transmission	More streaming, gaming, and cloud computing, increasing total bandwidth consumption
Paper Consumption	Digitalization and efficient printing	More documents are printed due to lower per-page costs
Ride-Sharing & Public Transit	Efficient, affordable transport services (Uber, electric buses)	More trips taken, sometimes replacing walking or biking
Air Travel	Fuel-efficient aircraft	Lower costs lead to increased air travel demand
Food Production	High-yield farming techniques	More food is produced and consumed, increasing overall agricultural resource use

As applied to more-affordable generative artificial intelligence solutions, a drastic decrease in cost might-also lead to a decrease in buying of high-end AI servers, for example, even if model training can be done on lower-capability servers. (at least in some cases). 

The argument is that efficiency doesn’t reduce demand—it increases it. As AI becomes cheaper and more accessible, more businesses, startups, and individuals will adopt it and new higher-end use cases will emerge, driving more higher-end GPU sales. 

Perhaps the bigger immediate concern is that many contestants have essentially overpaid for their infrastructure platforms. 

That becomes a business issue to the extent that, in competitive markets, the lower-cost producers tend to win. 

On the other hand, the Jevons Effect works when the price of the inputs does not change. If the price of high-end Nvidia servers does drop, then supply and demand principles--and not the Jevons Effect--will operate. And lower prices for high-end Nvidia GPUs then sustains demand. 

The Jevons Effect suggests that improved product efficiency leads to greater overall resource consumption rather than reductions. That might apply to use of AI models, AI as a service or power consumption. 

But  many have speculated that AI models such as DeepSeerk would lessen the need for high-end graphics processor units, for example. That might hold only if the prices for such servers remains the same. 

And the general rules of computing economics suggest lower prices with scale and time. So the ultimate impact of DeepSeek and other possible model contenders on demand for high-end Nvidia servers might be more neutral than some fear.  

And, in any cases, one might argue any effect on high-end server demand might affect GenAI models more than broader and more complicated artificial general intelligence operations.

How Much "Abundance" Could AI Bring to Physical Goods?

Some observers do not like the idea that wages and productivity are related in a fairly important negative way. Which is to say, lower labor costs are one measure of higher productivity. So, crudely, lower prices for most consumer goods hinge on lower production costs, which in turn are dependent on lower costs of all inputs required to produce those goods. 

The promise of artificial intelligence is that it will help reduce production cost, and therefore lead to retail price advantages. “Abundance,” in other words, is the hoped-for end result. We already have seen this trend for all manner of digital goods. 

The issue is whether we might see it in the realm of physical goods. To put matters another way, if product prices fall to near zero, it also will be true that the costs of creating those goods must be correspondingly near zero. 

Of course, most observers would say that raw materials, manufacturing, transportation, warehousing and marketing costs are far from zero. As a rule, the more value in any product is driven by virtual or non-material sources, the easier it will be to push out costs using AI. 

And the point, for physical goods, is not necessarily any actual approach to near-zero costs. All that has to happen is that costs change so dramatically that the ability to use the product as much as desired is enhanced. In other words, cost is low enough that a user or customer will not be deterred by such cost. 

Think search, social media, e-commerce, content streaming or home broadband. The cost to use these products is low enough that there is no barrier to widespread use. 

One might argue that all past general-purpose technologies have achieved some measure of greater abundance in large part because they are broadly horizontal in their impact: they enable virtually all industries.

AI should have that same horizontal effect, increasing productivity in virtually all industries.

General Purpose Technology	Impact Across Industries	Examples
Electricity	Revolutionized manufacturing, transportation, communication, and daily life.	Factories with electric power, electric lighting, household appliances, electric vehicles
The Internet	Transformed communication, information access, commerce, and social interaction.	E-commerce, online education, social media, cloud computing
Artificial Intelligence	Automates tasks, provides insights from data, and enables new forms of interaction.	Self-driving cars, personalized medicine, chatbots, fraud detection
Biotechnology	Advances in medicine, agriculture, and materials science.	Gene editing, disease-resistant crops, bioplastics
Nanotechnology	Creates materials and devices at the atomic and molecular level, with applications in various fields.	Stronger and lighter materials, targeted drug delivery, advanced electronics

How Much Substitution Does ChatGPT Affect Google Search?

Most observers could agree that the rise of generative artificial intelligence chatbots poses a threat to traditional search engines. 

Indeed, Google search volume seems to have dropped recently, as some might have suspected could be the case as more “search” activity moves to generative artificial intelligence chatbots. 

On the other hand, Google is not sitting idly by, and is aggressively moving to incorporate GenAI into its core search process. And there is some evidence the strategy is working. In December 2024, for example, Techloy research suggests Google held a commanding lead among GenAI searches, compared to ChatGPT, Bing and Alphabet’s own Gemini model. 

source" Techloy  

Evercore analysts say there has been a notable increase in users adopting ChatGPT as their primary search tool, with usage rising from one percent to eight percent over a few months. Concurrently, Google's search engine usage declined from 80 percent to 74 percent.

Adobe reported a tenfold increase in U.K. traffic from AI sources to internet retailers between July and September 2024. Approximately 25 percent of Britons utilized AI for shopping, a figure projected to reach one-third by the end of the year.

Perplexity AI, an AI-driven search startup, experienced a sevenfold increase in monthly revenues and usage since early 2024, achieving $35 million in annualized revenues. 

Will DeepSeek Prove Neutral for Nvidia and AI-as-a-Service Providers?

Many financial analysts and investors are concerned that lower-cost language models such as DeepSeek will reduce demand for high-end graphics processor units, servers and high-performance computing services. 

But there also is an argument that lower-cost models and inferences will increase demand for all those products. Paradoxically, lower training and inference costs will stimulate more demand, possibly lead to new use cases 

Also, keep in mind that generative AI is only one form of AI. Perhaps SeepSeek points the way to lower processing costs for GenAI. That does not necessarily mean improvements for general AI or even machine learning. 

One might argue it remains necessary to use high-end processors and accelerators for use cases moving towards artificial general intelligence. 

The Jevons Effect, or Jevons Paradox suggests that an increase in efficiency in resource use can lead to an increase in the consumption of that resource, rather than a decrease, as some might expect, over the long term. 

Industry/Resource	Efficiency Improvement	Observed Increase in Consumption
Coal (Historical Example)	More efficient steam engines in the 19th century	Increased coal consumption as steam engines became more widely used
Oil & Gas	Fuel-efficient car engines	More people drive longer distances, leading to sustained or increased fuel demand
Electricity	Energy-efficient lighting (LEDs, CFLs)	More lighting is used due to lower energy costs, offsetting savings
Computing Power	More efficient processors (Moore’s Law)	Increased use of computing applications and data-intensive services
Water Usage	Efficient irrigation systems	More land is cultivated, increasing total water use
Internet Bandwidth	Faster and cheaper data transmission	More streaming, gaming, and cloud computing, increasing total bandwidth consumption
Paper Consumption	Digitalization and efficient printing	More documents are printed due to lower per-page costs
Ride-Sharing & Public Transit	Efficient, affordable transport services (Uber, electric buses)	More trips taken, sometimes replacing walking or biking
Air Travel	Fuel-efficient aircraft	Lower costs lead to increased air travel demand
Food Production	High-yield farming techniques	More food is produced and consumed, increasing overall agricultural resource use

As applied to more-affordable generative artificial intelligence solutions, a drastic decrease in cost might-also lead to a decrease in buying of high-end AI servers, for example, even if model training can be done on lower-capability servers. (at least in some cases). 

The argument is that efficiency doesn’t reduce demand—it increases it. As AI becomes cheaper and more accessible, more businesses, startups, and individuals will adopt it and new higher-end use cases will emerge, driving more higher-end GPU sales. 

Perhaps the bigger immediate concern is that many contestants have essentially overpaid for their infrastructure platforms. 

That becomes a business issue to the extent that, in competitive markets, the lower-cost producers tend to win. 

On the other hand, the Jevons Effect works when the price of the inputs does not change. If the price of high-end Nvidia servers does drop, then supply and demand principles--and not the Jevons Effect--will operate. And lower prices for high-end Nvidia GPUs then sustains demand. 

The Jevons Effect suggests that improved product efficiency leads to greater overall resource consumption rather than reductions. That might apply to use of AI models, AI as a service or power consumption. 

But  many have speculated that AI models such as DeepSeerk would lessen the need for high-end graphics processor units, for example. That might hold only if the prices for such servers remains the same. 

And the general rules of computing economics suggest lower prices with scale and time. So the ultimate impact of DeepSeek and other possible contenders might be more neutral than some fear.