Will Robotaxi Business Models Beat Rideshare Models? The "Butterfly Effect" Might Matter

On the surface, given the role that worker compensation plays for just about any business, it might seem obvious that a robotaxi business would have higher profit margins than ride sharing, at scale. It probably is closer to a “tie” than most of us likely believe, for the medium term. 

Driver compensation for rideshare firms seems to be in the 70 percent to 75 percent range, so the attraction of a robotaxi for a service provider is obvious. 

But there are other issues, such as the possibility that, for a time, robotaxi fares could be as much as 50 percent lower than for a traditional rideshare. That should equalize over time, assuming the market comes to value both services equally. 

The comparison obviously is different if robotaxi fares are higher than ride sharing. 

Element	Traditional Ride-Hailing (Uber/Lyft)	Robotaxi (Autonomous)
Revenue per Mile	$2.50–$3.50 (average US market) 11	$1.00–$1.50 (targeted, but can be higher) 2 1 10
Main Revenue Source	Passenger fare (per ride/mile)	Passenger fare (per ride/mile), plus potential for in-car ads, data sales, B2B deals, and subscriptions 9
Key Costs
Driver Compensation	70–75% of fare (largest cost) 9	$0 (no driver) 9,2
Vehicle Depreciation	10–15% 9	20–25% (higher utilization, rapid wear) 1, 9
Fuel/Energy	5–10% (gas or EV charging) 9	10–15% (electricity, higher utilization) 1, 9
Platform/Tech Fees	20–30% taken by platform 3	15–20% (tech stack, remote ops, platform)9, 3
Insurance & Regulatory	5–10% 9	5–10% (may be higher due to AV liability) 9,3
Maintenance	5–10% 9	5–10% (higher miles/year, but fewer accidents )1,9
Profit Margin	5–7% per ride 9	10–15% per ride 9,2
Example Ride (5 miles)
Fare Charged	$15	$7.50
Total Costs	~$14 (driver: $10.50, other: $3.50)	~$6.60 (vehicle: $1.25, energy: $0.45, ops: $1.13, other: $3.77) 1, 9
Profit per Ride	~$1 (5–7%)	~$0.90–$1.13 (12–15%)

For example, there is some evidence that robotaxi fares have been higher than charged by ride sharing services, in some markets, at some times. So in some cases, ride sharing might actually then have a negative profit margin for some rides in off-peak hours. 

That data is probably skewed by the willingness of early adopters to pay more. It remains to be seen whether most customers will do so, over the longer term. 

Element	Traditional Ride-Hailing (Uber/Lyft)	Robotaxi (Autonomous) with 35% Higher Fare
Fare per Mile	$3.00 (average)	$4.05 (35% higher than $3.00)
Fare for 5-mile Ride	$15.00	$20.25
Main Revenue Source	Passenger fare	Passenger fare, plus potential ancillary revenues
Key Costs
Driver Compensation	~$10.50 (70% of fare)	$0 (no driver)
Vehicle Depreciation	~$2.25 (15% of fare)	~$5.06 (higher utilization and AV tech costs)
Fuel/Energy	~$1.05 (7% of fare)	~$1.35 (higher utilization, mostly electric)
Platform/Tech Fees	~$3.00 (20% of fare)	~$4.05 (20% of fare, includes AV tech & ops)
Insurance & Regulatory	~$1.05 (7% of fare)	~$1.35 (higher liability and regulatory costs)
Maintenance	~$1.05 (7% of fare)	~$1.35 (higher miles but fewer accidents)
Total Costs	~$18.90	~$13.16
Profit per Ride	~$15 - $18.90 = -$3.90 (loss or break-even*)	~$20.25 - $13.16 = $7.09
Profit Margin	Negative or very low (approx. -26%)	~35% profit margin

Whenever forecasts are made in complex systems, the results are highly dependent, in later years, on the initial assumptions.That often is known as the "butterfly effect," a concept in chaos theory that describes how a small change in initial conditions can lead to vastly different outcomes in a complex system. 

The butterfly effect highlights the limitations of predictability in complex systems and suggests that it may be impossible to accurately predict long-term outcomes, as small changes in initial conditions will be magnified over time. 

Autonomous vehicles used to support robotaxi service might not qualify, in a strict sense, as such highly-complex systems. Still, the variances in outcomes based on different assumptions about costs and revenues illustrate the principle. 

Stablecoin Use Cases Seem Tailor-Made for Cross-Border Transactions

With the caveat that new technologies sometimes enable use cases we have not thought about, and despite the interest a few hyperscale consumer retailers have in stablecoins for consumer retail payments, it still seems to me that the volume use cases for stablecoins revolve around cross-border payments. 

Stablecoins are cryptocurrencies pegged to stable assets like the U.S. dollar, and are seen as potential  disruptors of traditional consumer retail payments. Of course, there always are issues when such disruption is proposed. 

Retailers like the lower interchange fees (often three percent of charged value when a credit card is used) stablecoins promise. But lots of consumers probably like the “cash back” feature of their credit cards, and stablecoins might not allow that feature so easily. So some consumer resistance is to be expected.

And scale will be an issue, as has been the case for any payment network (Visa, Mastercard, Discover, PayPal or any other platform). There will be some friction unless consumers are assured their stablecoins can be used virtually anywhere. 

Cross-border payments seem the more compelling use case. 

Stablecoins offer the value of near-instant, low-fee transfers across borders, bypassing slow and expensive traditional systems like SWIFT or wire transfers. That might be particularly attractive for individuals sending money home (remittances) or businesses handling international transactions.

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For cross-border business-to-business transactions, stablecoins could mean settlement times reduced from days to seconds, cutting intermediary fees (often three to six percent).

Some of those same values might also apply to consumer cross-border payments or payments to employees or contractors across borders as well, avoiding currency conversion fees and delays. 

In principle, stablecoins might also support microtransactions. 

There is a Union of Different Kinds

There's way too little of this, these days: what unites us, not what divides us; the acceptance, tolerance of real diversity; not the fake kind that castigates "groups" of people one disagrees with. 

Computing has Shifted from Work to Life and Now Begins to Augment Life

I think we generally miss something important when pondering how artificial intelligence will shift job functions from repetitive, lower-order tasks to higher-order cognitive tasks, even displacing many cognitive tasks, with consequent impact on jobs. 

Across three major computing eras: the personal computer era (roughly 1970s–1990s); the internet era (1990s–2010s) and the coming AI era (2010s–present), computing's pervasiveness has increased steadily.

Where we first used PCs to accelerate routine work tasks ("doing things faster"), we later used the internet to accelerate knowledge acquisition ("learning things faster") and then playing, shopping and traveling, while demolishing many geographic barriers.  

The shift was from “computing for work” to “computing for life.”

But AI should be even more pervasive, allowing us to optimize outcomes ("doing things better"), and shifting computing from intentional interactions to anticipatory (autonomous) action. So computing shifts from tool to “collaborator.” PCs and software were tools we used. In the AI era computing will augment and amplify human capabilities. 

To be sure, we might argue that all general-purpose technologies have augmented human senses or capabilities in some way (muscles, sight, hearing, cognitive tasks, speech, transport, staying warm or cool). 

So the movement is something like “work to life to existence.” Sure, we can still ponder what AI means for work, or life. But that likely underplays the impact on normally esoteric thinking about what humans do that is uniquely human. 

AI arguably can automate intermediate cognitive tasks such as basic data analysis, customer service responses and routine decision-making. So yes, AI will reshape work. 

Cognitive Task	Example Tasks	Current AI Capabilities	Extent of Automation
Data Processing and Analysis	Data entry, basic statistical analysis, report generation	AI excels at processing large datasets, generating insights, and creating reports (e.g., tools like Power BI, Tableau with AI plugins, or custom ML models).	High: Routine data tasks are fully or near-fully automated. Human oversight needed for validation and complex interpretation.
Pattern Recognition	Fraud detection, image classification, trend identification	AI uses machine learning (e.g., neural networks) to identify patterns in financial transactions, medical imaging, or market trends with high accuracy.	High: AI often outperforms humans in speed and scale, but human judgment is required for context or anomalies.
Basic Decision-Making	Customer service responses, inventory management, scheduling	AI-powered chatbots (e.g., Zendesk, Intercom) handle routine queries; algorithms optimize schedules or stock levels.	Moderate to High: Routine decisions are automated, but complex or ambiguous cases require human intervention.
Content Generation	Writing emails, creating marketing copy, summarizing texts	Generative AI (e.g., GPT models, Jasper) produces coherent text, summaries, or creative content based on prompts.	Moderate: AI generates drafts or suggestions, but human editing is needed for nuance, tone, or originality.
Diagnostic Tasks	Medical diagnostics, legal research, technical troubleshooting	AI assists in diagnosing diseases (e.g., IBM Watson, Google Health), analyzing legal documents, or identifying system errors.	Moderate: AI provides accurate recommendations, but final diagnoses or decisions require human expertise.
Predictive Modeling	Sales forecasting, risk assessment, customer behavior prediction	AI models (e.g., regression, deep learning) predict outcomes based on historical data with high precision.	High: Predictions are automated, but humans must interpret results and make strategic decisions.
Language Translation and Processing	Real-time translation, sentiment analysis, speech-to-text	AI tools (e.g., Google Translate, DeepL) provide near-human-quality translations and analyze sentiment in texts or speech.	High: Routine translations are nearly fully automated; human input needed for cultural nuances or specialized contexts.
Routine Problem-Solving	Technical support queries, basic coding, process optimization	AI resolves common IT issues, generates simple code (e.g., GitHub Copilot), or optimizes workflows.	Moderate: AI handles standard cases, but novel or complex problems require human creativity and reasoning.

But AI will affect not only work, but almost all other elements of human life. In the PC era computing automated and digitized work and personal projects.

In the Internet era computing enabled new forms of creativity, commerce, and community.

In the AI era we’ll see augmented human intelligence, senses, and capabilities.

Also, compared to the earlier impact of PCs and the internet, it is possible that AI will produce outcomes sooner than has been the case in the past. 

Where we might argue that PCs produced widespread change over a two-decade or three-decade period, where the internet arguably produced fundamental changes over a two-decade period,, some believe AI will achieve widespread change in as little as a decade. 

The IBM PC, for example, was released in 1981. It wasn’t until about 2000 that half of U.S. households owned a PC. 

In 1983, perhaps 10 percent of U.S. homes owned a PC and about 14 percent of those homes used a modem to connect using the internet, according to Pew Research. At that point, it was all-text bulletin boards and the visual browser and multimedia internet had not yet been invented. 

It was not until 2000 or so that half of U.S. consumers said they used the internet. 

Year	PC Adoption (%)	Internet Adoption (%)
1995	36	14
2000	51	41.5
2010	76.7	71
2016	89.3	87

Hard to Tell What a "Typical" Consumer Pays for Home Broadband

It remains as hard as ever to figure out what the “average” or “typical” U.S. customer actually pays for home broadband service. Analysis has to be done on posted retail prices for stand-alone service, without taking into account bundle discounts, promotional pricing and then all the “extras” such as modem rental that often are involved.

And in the U.S. market, bundling is quite significant, indeed.



That noted, if one just analyzes the posted, stand-alone prices for home broadband, one might say these are typical ranges. Anecdotally, one might guess that the bundle price for any home broadband service can easily be 40 percent to 50 percent lower than the stand-alone retail price.

The point is that up to half of households do not pay those “stand-alone” rates because they buy a bundle of some sort that lowers the actual cost. So, roughly speaking, blended prices might be about 75 percent of the stand-alone rates, including both bundle discounts and accounts buying stand-alone internet access.