Surge Pricing Rankles, But is Not "Gouging"

Surge pricing (dynamic pricing) often rankles consumers, as prices shift suddenly and noticeably higher during peak times. But as with so much in life, dynamic pricing “automatically” adjusts the use of scarce resources and encourages greater supply of in-demand goods, as much as it seems somehow unfair to consumers facing the price changes. 

Studies on Dynamic Pricing's Impact on Supply-Demand Balance
Study	Researchers	Year	Industry	Key Findings	Effect on Supply	Effect on Demand
The Economics of Surge Pricing	Cohen, Hahn, Hall, Levitt & Metcalfe	2016	Ridesharing	For every   10% increase in price during surge periods, driver supply increased by 8.4% while demand fell by about 6.2%	Positive: 8.4% increase in driver supply for every 10% price increase	Moderate reduction: 6.2% decrease for every 10% price increase
Surge Pricing Solves the Wild Goose Chase	Castillo, Knoepfle & Weyl	2017	Ridesharing	Without surge pricing, driver utilization rates fell from 70% to 50% during peak demand	Significant improvement in resource allocation and driver availability	Shifted demand from peak to off-peak periods
Dynamic Pricing in Major League Baseball Tickets	Shapiro & Drayer	2014	Sports Events	Dynamic pricing increased revenue by 5-15% while maintaining attendance levels	N/A (fixed supply)	More efficient distribution of attendance across games
Peak-Load Pricing in the Electric Utility Industry	Borenstein & Holland	2005	Electricity	Time-of-use pricing reduced peak demand by 3-6% and increased off-peak usage	Reduced need for building excess capacity for peak periods	Shifted 5-10% of consumption to off-peak hours
The Impact of Dynamic Pricing on Hotel Revenue	Abrate, Fraquelli & Viglia	2012	Hospitality	Hotels with dynamic pricing had 4-9% higher revenue and 7% higher occupancy rates	Incentivized better capacity management	More efficient room allocation across different demand periods
Dynamic Pricing of Inventory/Capacity with Infrequent Price Changes	Netessine & Shumsky	2005	Airline Industry	Airlines using dynamic pricing saw load factors increase from 72% to 82%	Better capacity utilization	Shifted price-sensitive customers to off-peak flights
Dynamic Pricing for Public Transportation	Li, Brunskill & van der Schaar	2019	Public Transit	Time-variable fares reduced peak congestion by 11% while increasing overall ridership by 3%	Reduced need for additional vehicles during peak hours	Shifted 8-15% of non-time-sensitive trips to off-peak hours
The Value of Flexible Pricing in Mass Transit	Currie	2018	Public Transit	Peak/off-peak fare differentials of 25% reduced peak crowding by 7.5%	More efficient allocation of existing capacity	5-7% of riders shifted travel times to off-peak periods
Dynamic Pricing in Online Marketplaces	Chen & Gallego	2019	E-commerce	Retailers using dynamic pricing algorithms increased inventory turnover by 14%	Better inventory management with 22% less overstocking	More efficient matching of price-sensitive buyers with products
Real-Time Pricing and Demand Response in Electricity Markets	Faruqui & Sergici	2010	Electricity	Critical peak pricing reduced peak demand by 13-20%	Reduced need for backup generation capacity	Significant reduction in usage during peak events
Dynamic Pricing in Professional Services	Lee, Choi & Shen	2015	Service Industry	Law firms with dynamic billing increased utilization rates by 9%	More efficient allocation of attorney time	Shifted non-urgent work to off-peak periods
Dynamic Pricing for Parking Spaces	Pierce & Shoup	2013	Urban Parking	SFpark program in San Francisco reduced cruising for parking by 30%	More efficient allocation of existing parking spaces	Reduced congestion from drivers searching for parking
Dynamic Pricing and Learning	den Boer	2015	Various Retail	Retailers using machine learning for dynamic pricing saw 3-7% higher profits	Better inventory management with 15% less waste	More efficient matching of price-sensitive customers with products
Resort Revenue Management	Pinchuk	2006	Tourism	Ski resorts using dynamic pricing saw 6% higher utilization rates	Better distribution of visitors across facilities	Shifted 10-15% of visitors to non-peak days
Congestion Pricing for Road Networks	Small & Verhoef	2007	Transportation	Congestion charges reduced peak traffic by 15-20%	Improved traffic flow and reduced travel times	Shifted 10-18% of non-essential travel to off-peak hours

Perhaps inevitably, surge pricing can seem like “price gouging” (when sellers dramatically increase prices for essential goods or services during an emergency or crisis situation). 

Though the two (price gouging and dynamic pricing) can appear to be the same, they are not. Price gouging occurs specifically during emergencies while dynamic pricing operates continuously under normal market conditions.

Price gouging involves extreme markups (often several hundred percent), while dynamic pricing typically involves more moderate adjustments.

Price gouging exploits desperation during crises, whereas dynamic pricing aims to allocate resources efficiently by incentivizing more supply when demand increases.

Good dynamic pricing systems communicate changes clearly and predictably, while price gouging often happens with little warning or justification.

All that said, dynamic pricing often seems “wrong” because people develop mental "reference prices" for products and services. Surge pricing violates these established expectations.

It often also seems unfair and unreasonable, seemingly a case of firms exploiting shortages to maximize profit, rather than responding to demand and supply imbalances. 

It also seems “unequal,” as potential customers with more resources get access while those with less resources have to wait. 

Still, the economic principle is arguably clear enough: it creates more supply under conditions of excess demand and also reduces demand. 

Surge pricing incentivizes more drivers (or robotaxis) to enter areas with high demand, such as during peak hours, events, or bad weather. This increases the supply of rides, reducing wait times and ensuring riders can access transportation when they need it most.

Without surge pricing, shortages (excess demand) would lead to longer wait times or unavailability, as seen in fixed-price systems like traditional taxis during peak periods.

Surge pricing acts as a market-clearing mechanism, prioritizing rides for those who value them most (those willing to pay higher fares). The surge prices also encourage riders to delay trips or use alternative transport (public transit, taxis), reducing congestion on the platform and preventing system overload, which preserves experience quality for riders who do use the platform. 

Dynamic pricing also encourages more supply, motivating human drivers to work during high-demand periods or in underserved areas. For robotaxi operators, it justifies deploying more vehicles or reallocating fleets to high-demand zones.

Higher revenues from surge pricing enable providers to invest in fleet expansion, technology upgrades, or driver recruitment, improving service quality and capacity over time, benefiting both providers and riders.

But none of that alleviates the shock of price surges under peak demand.

Amazon has at Least 160 AI Initiatives Underway, Sources Say

Amazon's retail business supports more than 160 AI initiatives, including the Rufus shopping assistant and Theia product-image generator, according to a report by Business Insider. 

Other AI projects in the works include:

• A vision-assisted package retrieval service that uses computer-vision technology to help drivers quickly identify and pick the correct packages from vans at delivery stops.

• A service that automatically pulls in data from external websites to create consistent product information.

• A new AI model that optimizes driver routing and package handling to reduce delivery times and improve efficiency.

• An improved customer service agent that uses natural language to address customer return inquiries.

• A service that automates seller fraud investigations and verifies document compliance.

Last year, Amazon estimated that AI investments by its retail business indirectly contributed $2.5 billion in operating profits. Those investments also resulted in about $670 million in variable cost savings, Amazon sources said.

Turns Out, Voice is a Product Like Any Other

For many of us who were familiar with the voice business, it seemed inconceivable that fixed network voice calling would be a product like any other, with a life cycle of growth, maturation and then decline. But that has happened. 

Surveys of U.S. consumer use of landline telephone networks  (2022 to 2024) suggest the percentage of U.S. households or adults with any type of landline phone service ranges between 24 percent and 30 percent, down from 90-percent or higher levels  in the early 2000s.

One reason is that about 70 percent or more of U.S. consumers rely on mobile phones for voice communications. 

U.S. carrier of last resort laws impose specific obligations on incumbent phone companies to ensure basic telephone service is available to all residents. The core principle is the obligation to provide service to any customer, irrespective of how difficult or costly it might be to reach them.

Such laws have cost implications for telcos trying to modernize their networks, as a carrier of last resort  cannot simply abandon service or cease operations in its designated territory without obtaining permission from state public utility commissions. 

As a practical matter, that includes the copper access networks few customers use anymore. The obligation is understood to mean that "basic service" (voice connections over copper networks) must be maintained. 

The key issue is that service providers would vastly prefer to retire the lightly-used copper networks and replace them with modern fiber optic networks, for example.

"NeoCloud" Emerges

The term "neocloud" is used to describe a new generation of cloud infrastructure companies such as CoreWeave, Lambda Labs, Crusoe and others that are distinct from the traditional hyperscalers like Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP)."Neocloud" (literally 

The neocloud providers specialize in AI infrastructure, especially compute power specifically tailored for artificial intelligence and machine learning workloads, especially using graphics processing units. 

Some would argue that neocloud providers offer computing that is more flexible and customized than tends to be offered by hyperscalers, offering custom infrastructure setups, shorter contract commitments, or usage-based pricing models.

Some providers, including CoreWeave and Nebius Group,  design their entire stack around AI, with support for AI model training, fine-tuning, and inference workloads. 

The Tomb is Empty

 

Happy Easter, brothers and sisters.

How LLMs Work, by Andrej Karpathy

Andrej Karpathy, Eureka Labs founder and computer scientist (Tesla, OpenAI), explains how language models work, and are built. You'll need about 3.5 hours to view the whole video, but it covers transformer networks, training (human and algorithmic; labeling) and reinforcement learning. 

Google Loses Antitrust Lawsuit, Not a Good Precedent for Meta

Google has been found guilty under the Sherman Act United States v. Google LLC 

Of illegally monopolizing advertising markets for publisher ad servers and ad exchanges used in open-web display advertising.   

The ruling is that Google engaged in a series of anti-competitive acts to willfully acquire and maintain monopoly power in the ad technology market, using acquisitions of ad software companies (such as DoubleClick and AdMeld) to unlawfully shore up market share and stifle competition.

The ruling probably does not bode well for Meta, which faces similar antitrust action brought by the Federal Trade Commission. Principally, the FTC alleges that Meta illegally maintained a monopoly in the "personal social networking services" market through anticompetitive conduct. 

The core of the complaint focuses on the acquisition of competitors, as was the finding in the Google ad tech antitrust decision. 

The FTC argues Meta employed a "buy-or-bury" strategy, specifically targeting its acquisitions of Instagram in 2012 and WhatsApp in 2014 to neutralize potential competitive threats and maintain its dominance.

What matters are the remedies the government entities might propose, assuming Meta likewise is found guilty of monopolistic behavior. 

For Amazon, AI is "Existential"

Though observers remain concerned about the huge amounts of capital being invested in artificial intelligence by hyperscalers, they arguably are doing so because AI poses disruptive threats comparable to past general-purpose technologies. It is, in other words, quite literally "existential," the very existence of a thing.

In other words, as happens with GPTs, AI could  fundamentally change how value is created and captured across industries such as search and e-commerce. Indeed, that happens frequently when a GPT arises. 

Electricity enabled entirely new industries built around electric motors (factories, appliances) and communications (radio, eventually TV). 

The internet revolutionized communication (email, social media), information access (web search), commerce (e-commerce), and entertainment (streaming). It decimated industries like print media and physical retail while creating whole new replacements led by different firms than led the legacy industries. 

Looking only at e-commerce, there already are glimmers of potential change. According to an analysis by Adobe Analytics, AI-driven traffic to retail websites surged 1,200 percent during the most-recent 2024 holiday shopping season.

source: Adobe 

Adobe’s survey of 5,000 U.S. consumers suggests 39 percent have used generative AI for online shopping, with 53 percent planning to do so in 2025.

Consumers use generative AI for research (55 percent of respondents), receiving product recommendations (47 percent), seeking deals (43 percent), getting present ideas (35 percent), finding unique products (35 percent) and creating shopping lists (33 percent).

According to a survey conducted by Adobe Analytics based on one trillion visits to U.S. retail sites, the trend extended beyond the holiday season as traffic to retail sites from AI-driven searches increased 1,200 percent in February compared to July 2024 and has doubled every two months since September 2024.

On Cyber Monday alone, traffic from generative AI sources soared by 1,950 percent from last year’s event, Adobe says. 

Compared to consumers coming from non-AI traffic sources (including paid search, affiliates and partners, email, organic search and social media), consumers coming from generative AI sources show eight percent higher engagement as they dwell sites for a longer period of time. 

These visitors also browse 12 percent more pages per visit, with a 23 percent lower bounce rate, Adobe notes.

source: Adobe 

At least for the moment, conversion (visits that become purchases) is the one area where AI lags other sources of traffic. 

Traffic from generative AI sources is nine percent less likely to convert compared to other sources of traffic. But conversion rates are improving. In July 2024, the AI conversion gap was 43 percent.

source: Adobe 

As you might therefore guess, this poses threats to Amazon. 

Generative AI-powered Search engines and chat assistants are becoming increasingly important sources of consumer traffic for online web stores. 

In January 2025 Amazon had about 455 million monthly unique visitors. But AI assistants and chatbots collectively are getting three-digit millions of daily interactions. 

source: Similarweb 

And ChatGPT already is moving in the direction of adding shopping and e-commerce features. 

The point is that GPTs tend to disrupt industries and economies. So it makes sense that Amazon would invest heavily in AI to protect its commerce business, as Google will invest to protect its search business. 

And sometimes that works. Manufacturing firms previously reliant on steam, water, or manual power redesigned their factories around electric motors.

Energy companies focused initially on coal gas for lighting and kerosene for lamps pivoted their refining processes to prioritize gasoline production as demand shifted from lighting oil to fuel for internal combustion engines.

Likewise, IBM, originally a leader in tabulating machines and typewriters,  transitioned into the mainframe computer era. Walmart and Target added significant online commerce to their place-based operations. 

Many legacy content firms and industry segments were battered by the internet’s alternatives, but a few have managed to stabilize and even grow their online businesses. 

And financial firms have moved rapidly to embrace online commerce as well. The point is that the legacy providers are not without weapons in the fight to retain their relevance and even dominance. 

Still, the hyperscalers are investing so heavily in AI for obvious reasons: AI poses a genuine threat to their core business models.

Language Model Progress Blows Away Moore's Law

Language models are improving at a blistering pace, far outstripping what we have come to expect from computing in general and Moore’s Law in particular. Where Moore’s Law has suggested chip density doubles about every 18 months or so, AI language models have been improving nearly 300 times faster. 

The cost of querying an artificial intelligence model that scores the equivalent of GPT-3.5 (64.8) on MMLU, a popular benchmark for assessing language model performance, dropped from $20.00 per million tokens in November 2022 to just $0.07 per million tokens by October 2024 (Gemini-1.5-Flash-8B), a more than 280-fold reduction in approximately 18 months, according to Stanford University’s Human Centered AI Institute. 

source: Stanford University HAI 

Depending on the task, LLM inference prices have fallen anywhere from nine to 900 times per year.

At the hardware level, costs have declined by 30 percent annually, while energy efficiency has improved by 40 percent each year, HAI’s 2025 AI Index Report says.