What Will DoJ Require of Google?

Google is now in the “remedies” phase of an antitrust lawsuit filed by the U.S. Department of Justice, as Google has been found to be a monopolist in the search market. 

So what matters now are the remedies. DoJ has been floating remedies including

• Prohibiting Google's exclusive default search agreements, such as the multibillion-dollar deal with Apple to be the default search engine on Safari

• Forcing Google to divest its Chrome browser

• Requiring Google to share certain search data with competitors to level the playing field. ​

A final ruling on the remedies is expected by August 2025. ​An important issue is the relevance of past remedies imposed on Microsoft in the past. Basically, the case challenged Microsoft's bundling of its Internet Explorer web browser with the Windows operating system. The case culminated in a 2002 settlement that focused on behavioral remedies.

Key behavioral remedies included:

• API disclosure to third-party developers

• OEM flexibility, allowing original equipment manufacturers to install non-Microsoft middleware (Netscape Navigator)

• Set non-Microsoft middleware as defaults (a different default browser)

• Remove or hide access to Microsoft's middleware

Also, Microsoft was required to license Windows on uniform terms, meaning it could not provide preferential treatment to partners that agreed to exclude competitors.

If Google tries to follow Microsoft's playbook by arguing for behavioral remedies only, it is likely to  face a more skeptical reception than Microsoft did in the early 2000s. Regulators today are more likely to argue that behavioral remedies alone will not work.

In particular, the divesting of the Chrome browser has been proposed. That could have implications for artificial intelligence apps for several reasons, including the role played by the Chrome browser in driving traffic to Google’s important search engine. 

Losing the Chrome browser would likely require Google to do more cross-platform deals. 

For AI assistants, search provides real-time information beyond training data cutoffs, allowing answers including current information. Search also allows models to retrieve specific information on demand. Integrated search also helps models overcome hallucination issues by verifying facts. 

Search might also be a platform for creating new “super apps” that offer many functions within a single application. 

So the actual remedies will matter. 

Alphabet Suggests AI is Being Monetized, Already

Most observers want to know how  AI contributes to revenue growth at Alphabet and other firms, and the most-recent earnings report issued by Alphabet on April 25, 2025. So what might we conclude, based on that call?

Clearly, Alphabet management positioned AI features as central to Alphabet’s revenue and profit growth.  In the key search business(AI Overviews and Circle to Search), management pointed to significant ad revenue driven by increased engagement, with “stable” monetization. That is presumably meant to reassure investors that monetization, even if indirect, is not cannibalizing traditional search. 

Likewise, the Gemini language model, which powers both consumer and enterprise products, boosts Cloud and Workspace product adoption.

Obviously Waymo leverages AI for operational efficiency but is not a material revenue source, yet. 

The takeaway might be that AI monetization, especially for AI Overviews and Circle-to-Search, already is happening, even if indirectly, and does not cannibalize the core advertising business, which has been a concern analysts and investors have noted. 

Gemini’s enterprise applications likewise are said to be driving Google Cloud revenues. 

AI Overviews, powered by Gemini might have contributed to  double-digit revenue growth in search, with first quarter 2025 Search revenue reaching $50.7 billion, up 9.8 percent year-over-year. 

Ads integrated into AI Overviews are monetizing at roughly the same rate as traditional Search ads, Google indicated, suggesting no cannibalization of revenue.

In Google Cloud, Gemini powers the Vertex AI platform, which saw a five-fold  year-over-year increase in customers in the fourth quarter of  2024 and a 20-fold growth over the full year. Google Cloud’s $12.26 billion revenue in the first quarter of 2025 shows revenues up 28 percent  year-over-year). 

As often seems to be the case, AI monetization is largely indirect, contributing to the growth or profitability of other products using AI. 

Will AI Bring New Business Models?

One of the surprising internet developments has been the creation of new business models for technology firms, ranging from Alphabet and Meta to Apple. The most surprising development is ad-supported technology driven by content operations. 

Ad revenues are a major driver of revenues for Google, YouTube, Meta and even Amazon, for example. 

source: EMarketer, Seeking Alpha 

And one might dare to expect that at least one or two new models could develop with artificial intelligence as well, beyond subscriptions, pay-per-use, application programming interface licensing or insight-based monetization.

Indirect models likely also will be prevalent, as AI is expected to be used by most applications and processes, eventually. So indirect forms of revenue (customer acquisition, retention, profit margins, market share) might be the upside in many cases.

Should a major new business model emerge, we are likely to be taken largely by surprise.

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.