When Robotaxis Will Displace Auto Rentals

Inevitably, people are going to wonder when, and under what circumstances, robotaxis are going to displace auto rentals, just as there was similar questioning when ride sharing first developed. 

And, in all likelihood, the decision matrix will be roughly similar. The most vulnerable segment will be short-duration rentals in dense urban areas, on short trips of one to perhaps three days, as often is the case for ride sharing as an alternative to car rental. 

Single travelers are probably more likely to use robotaxis than groups or families. 

But ride sharing still does not compete well with auto rentals in many instances, including trips that last more than several days; involve distance travel and travel to rural areas. 

For example, on an upcoming week-long trip with a distance from airport to destination of about 70 miles, one-way using Uber will probably be about $158 (fare plus tip), so assume double that for the roundtrip, or about $316. Renting a vehicle for the week (six days) will run about $328. 

Granted, that will include fuel charges and possibly some parking, but the $12 difference also includes local transportation for the whole six days. 

Actually, for that particular trip, which I take relatively frequently, even a short weekend visit works out better just renting a vehicle, rather than relying on ride sharing.

Travel Mode Cost Comparison

When Ride-sharing & Robotaxis Beat Auto Rentals

Cost-Effective Alternative

Marginal/Situational

Not Cost-Effective

Travel Scenario	Current Rideshare (2025)	Robotaxis (2027-2030)	Key Factors	Rental Car Advantages
LOCAL TRAVEL (Within Metro Area)
Single Person, 1-2 Days Business meetings, short visits	Cost-Effective $40-80/day total	Highly Cost-Effective $25-50/day total	No parking costs, insurance, or fuel. Short distances favor per-trip pricing.	Flexibility for spontaneous stops, privacy
Group (3-4 people), 1-2 Days Friends visiting, family events	Marginal $60-120/day total	Cost-Effective $35-70/day total	Per-person cost sharing makes robotaxis attractive	Group luggage space, split rental costs
Single Person, 3-7 Days Extended business, family visits	Marginal $120-280/week	Cost-Effective $75-175/week	Daily usage patterns matter. High-frequency days favor robotaxis.	Unlimited usage, storage for personal items
Group, 3-7 Days Family vacations, group trips	Not Cost-Effective $180-420/week	Marginal $105-245/week	Multiple daily trips with groups become expensive	Luggage capacity, convenience for families
OUT-OF-TOWN TRAVEL (Regional/Long-Distance)
Single Person, 1-2 Days Regional business, weekend getaways	Not Cost-Effective $200-400+ each way	Marginal $120-250+ each way	Distance is key factor. Under 200 miles may favor robotaxis by 2030.	Much cheaper for long distances, route flexibility
Group, 1-2 Days Weekend trips, events	Not Cost-Effective $300-600+ each way	Not Cost-Effective $180-375+ each way	Long-distance robotaxis remain expensive even with cost sharing	Significant cost advantage, luggage space
Single Person, 3-7 Days Extended regional travel	Not Cost-Effective $400-800+ total	Not Cost-Effective $240-500+ total	Rental becomes dramatically cheaper for extended regional stays	Unlimited local driving, much lower per-day cost
Group, 3-7 Days Family vacations, multi-day trips	Not Cost-Effective $600-1200+ total	Not Cost-Effective $360-750+ total	Rental cars dominate for extended out-of-town group travel	Massive cost advantage, convenience, storage
SPECIAL CONSIDERATIONS
Airport-Only Business Travel Fly in, meetings, fly out	Cost-Effective $30-60 total	Highly Cost-Effective $20-40 total	Airport parking costs ($15-30/day) make alternatives attractive	None - rideshare/robotaxis clearly superior
Dense Urban Areas NYC, SF, Chicago downtown	Cost-Effective Variable by usage	Highly Cost-Effective Variable by usage	Parking costs ($25-50/day) and congestion favor alternatives	Very limited - mainly privacy and storage
Rural/Remote Areas National parks, small towns	Not Available Limited service	Limited Availability May remain limited	Service availability is primary constraint	Often the only viable option

Key Assumptions & Variables

• Robotaxi Pricing: Assumes 30-40% cost reduction vs. current rideshare due to elimination of driver costs
• Rental Car Costs: $40-70/day base rate plus gas ($30-50/day), insurance ($15-25/day), parking ($0-50/day)
• Trip Distance: Local = within 50 miles, Regional = 50-300 miles, Long-distance = 300+ miles
• Usage Patterns: Assumes 2-4 trips per day for local travel, varies by scenario
• Market Maturity: Robotaxi projections assume mature service with high vehicle availability
• Parking Costs: Urban areas ($20-50/day), Suburbs ($0-15/day), highly variable by location
• Group Size: Assumes single occupancy for business, 2-4 people for leisure travel

Agentic AI Should Change Computing Infrastructure: Issue is How Much

Agentic artificial intelligence, eventually featuring teams of autonomous agents working in concert, should have some obvious impact on computing infrastructure. 

Chips will shift further in the direction of custom silicon. There will be more need for low-latency networking; more local or edge processing in addition to remote processing; more parallel and dynamic context to processing; distributed and fault-tolerant processing; more access to distributed databases. 

Specialized hardware (graphics processing units and field programmable gate arrays); more orchestration and more security also will be needed. Think perhaps of swarms of autonomous drones that have to work together, for example. 

In general, we will need “more:” more energy; more chips; more networking; more processing; more interworking and collaboration between autonomous systems. 

So how does that look for a firm such as Lumen Technologies, as a supplier of networking? Perhaps nobody doubts that “more” capacity will be needed, and might be needed in some different locations. 

The issue might be “how much” AI networking requirements actually change market demand, aside from the obvious “more capacity” that is continually needed. 

For starters, Lumen is doubling its intercity fiber mileage; upgrading bandwidth to 100 Gbps and 400 Gbps, using self-provisioning for enterprise customers, with plans to upgrade to 1.2 Tbps to 1.6 Tbps. 

Lumen also is building private networks that connect data centers owned by hyperscalers. But it might be the change in where capacity is needed that will change most. For some time, networking capacity has been driven both by the need to interconnect data centers and the need to make more bandwidth available in the access network so end users are connected with sufficient bandwidth and low-latency services. 

Agentic AI does not necessarily change that situation. Data center interconnection will drive developments in the network backbone. And AI used by edge devices will continue to rely on “on the device” local processing. But requirements for more edge processing in addition to “on the device” will likely mean more regional data center computing and therefore more bandwidth of a regional nature. 

Whether peer-to-peer requirements lead to more meshy architectures remains to be seen. But to some extent agentic AI simply continues other trends such as needs for more symmetrical bandwidth in the access network. As upstream bandwidth became more important as users started routinely uploading images and video, so agentic AI will additionally create more need for bidirectional capacity as local processors and actions combine with web services, software as a service platforms and application programming interfaces.

Barring a big change, such as Lumen somehow divesting its entire local telecom business, to become a latter-day Level 3 Communications capacity supplier, AI-driven requirements might be more incremental than disruptive.

As a financial matter, a Lumen that is a pure-play capacity provider might have 70 percent of present revenue, but a higher valuation. Some believe that could result in a Lumen valuation that is up to double what the firm presently commands, assuming "flawless execution" and probably also hinging on how the debt burden gets distributed.

Will AI Increase "Span of Control" and What Does That Mean for Middle Managers?

Most observers likely believe artificial intelligence will be used to increase span of control, at least to the extent that many supervisory functions can be handled by AI. That "should" create some opportunities to increase "span of control," which should also allow some moves to reduce middle management jobs.

“Span of control” refers to the maximum number of “direct report” humans that can be supervised by a single manager. That number tends to hover between five and eight, but can be wider or narrower depending on the industry and tasks to be supervised. 

But that assumption might not be universally applicable. If and where AI means job functions get more complex, or require even more exercise of "human" skills such as empathy, ethical judgment or complex coordination, then AI might actually decrease spans.

Still, that is likely the exception to the rule. Most observers likely believe that AI will make possible some increases in spans of control.

AI systems might  monitor performance metrics continuously, provide real-time feedback, coordinate schedules and resources and handle routine administrative tasks that previously required human managers. It remains to be seen. 

AI should be able to analyze patterns across larger groups of employees than any human manager could effectively track. So to that extent, middle manager value could decrease somewhat. 

Still, middle managers likely cannot be completely replaced. They translate strategic vision into operational tasks, provide coaching and development, facilitate cross-functional coordination, handle complex interpersonal dynamics and act as information conduits between levels. They also make nuanced decisions that require contextual understanding and emotional intelligence.

The issue is how AI might ultimately be able to become a substitute for some of those functions. 

Industry	Typical Span of Control (Manager:Direct Reports)	Characteristics & Rationale
Call Centers	Wide (1:10 - 1:20+, potentially even 1:100+)	High volume of standardized, repetitive tasks. Employees often follow scripts and procedures. Technology (CRM, call monitoring) assists with oversight, allowing managers to oversee many agents.
Manufacturing, Production	Medium to Wide (1:8 - 1:15+)	Tasks can be repetitive and process-driven, especially on assembly lines. Clear procedures and visual management tools facilitate supervision. Safety protocols often require close monitoring, but experienced workers may need less direct oversight.
Retail (Store Level)	Medium to Wide (1:5 - 1:15+)	Store managers often oversee a team of sales associates, cashiers, and stockers. Tasks are somewhat standardized, but customer interaction and merchandising can add complexity. Varies by store size and type.
Technology, Software Development	Narrow to Medium (1:4 - 1:7)	Work is often complex, requiring high levels of collaboration, problem-solving, and creativity. Teams are typically self-managing to a degree, but managers provide technical guidance, mentorship, and project oversight for highly specialized tasks.
Healthcare (Clinical/Nursing)	Narrow to Medium (1:3 - 1:10)	High complexity, critical decisions, and direct patient care. Requires close supervision to ensure patient safety, quality of care, and adherence to protocols. Nurse managers often have fewer direct reports due to the intensity and criticality of the work.
Executive Management, Leadership	Narrow (1:3 - 1:7)	Deals with strategic decision-making, complex problem-solving, and high-level collaboration. Requires significant individualized attention, coaching, and strategic alignment among direct reports (e.g., department heads, VPs).
Consulting, Professional Services	Narrow to Medium (1:3 - 1:8)	Projects are often unique and complex, requiring specialized expertise and close client interaction. Managers (project leaders) need to provide detailed guidance, quality control, and client relationship management for their team members.
Education (School Administration)	Narrow to Medium (1:5 - 1:10)	Principals oversee teachers, who in turn manage students. The complexity of managing various curricula, student needs, and administrative tasks often leads to a narrower span for administrators compared to highly standardized environments.
Financial Services ( Banking Branch)	Medium (1:6 - 1:10)	Branch managers oversee tellers, customer service representatives, and loan officers. While some tasks are standardized, customer interactions and regulatory compliance require a moderate level of oversight.

And though AI should tend to increase spans of control in most industries, AI could result in a decrease of spans in education and healthcare, two fields where productivity increases are hard to produce. AI might in many cases increase the span of control by reducing the managerial burden. 

But in some contexts it could decrease spans if roles become more complex or require closer oversight of human-AI collaboration. That might also tend to happen when empathy, ethical judgment or complex coordination is required, and that would tend to be true whether we are considering AI or simply span of control issues in general. 

Industry	Current Avg. Span of Control	AI Impact Direction	Projected Change	Reasoning
Manufacturing	5–10	Increase	10–15	AI enables predictive maintenance, process monitoring, reducing oversight needs.
Retail	8–12	Increase	12–18	AI-driven scheduling, inventory, and customer insights reduce manual supervision.
Healthcare	4–6	Mixed	3–7	Clinical AI reduces admin load, but patient safety and human empathy limit scalability.
Finance	6–10	Increase	10–15	AI automates reporting, fraud detection, and customer service functions.
Education	10–15 (academic), 5–8 (admin)	Mixed	8–20 (academic), 4–10 (admin)	AI grading and tutoring help faculty scale, but student engagement needs persist.
IT & Software	6–12	Increase	10–18	AI tools for code review, project tracking, and testing reduce managerial bottlenecks.
Construction	4–8	Slight Increase	6–10	AI in site planning and safety monitoring helps, but physical oversight still critical.
Legal Services	3–6	Increase	5–10	AI in discovery, contract analysis boosts support staff efficiency.
Public Sector, Government	5–9	Slow Increase	6–10	Bureaucracy limits rapid gains, but AI may help in records and citizen services.
Logistics, Transport	6–12	Increase	10–18	AI route optimization and fleet tracking allow for greater managerial reach.

Content Does Not Monetize Itself: Others in the Value Chain are Necessary

Businesses virtually never take positions that undermine or threaten their own interests. So how does Cloudflare benefit from its new emphasis on blocking language model scraping of web content?

By blocking unauthorized AI crawlers, Cloudflare helps its customers safeguard their original content from being used to train language models without consent or compensation. This is especially valuable for the publishers, media companies, and content-driven businesses that buy Cloudflare services. 

There also are new revenue opportunities. Cloudflare’s introduction of an AI scraping marketplace allows website owners to set terms and charge AI companies for access to their content. Cloudflare potentially earns fees or commissions, turning content protection into a business opportunity.

Cloudflare’s policies also provide more negotiating leverage for its content customers when negotiating licensing deals.

AI scraping can resemble denial-of-service attacks, overloading servers and degrading website performance. Cloudflare’s controls help prevent such problems.

The point is that the new policies are beneficial for Cloudflare, increasing its perceived value for its core clients; providing differentiation from competing alternatives and creating possible new revenue opportunities as well.

Lawsuits over copyright infringement and also technology solutions such as undertaken by Cloudflare to prevent web scraping are some content owner and content-supporting supplier responses to AI language model impact on their revenue models.

Still, content business models are going to have to change, as monetizable web traffic already is declining. Blocking of AI crawlers might slow down indexing activities, and raise the cost of doing so.

But content creators have no “right” to make money in the value chain. It always takes partners to monetize content or art. Language models might arguably pose an issue, just as controls on content scraping might be viewed as a help.

But content monetization always is dependent on others in the value chain.

Content Creators	Historical Value Chain Partners	Role in Monetization	Modern Value Chain Partners	Role in Monetization
Painters	Patrons (e.g., nobility, churches), art dealers, auction houses	Commissioned works, purchased paintings, or sold them to collectors; provided materials and exhibition spaces.	Galleries, online marketplaces (e.g., Saatchi Art), NFT platforms (e.g., OpenSea)	Display and sell artworks, authenticate pieces, or enable digital sales (e.g., NFTs); provide global exposure via online platforms.
Writers	Publishers, printers, booksellers	Printed and distributed books, serialized novels in magazines, paid advances or royalties.	Self-publishing platforms (e.g., Amazon KDP), literary agents, crowdfunding (e.g., Kickstarter)	Enable direct publishing and sales, connect writers to publishers, or fund projects via fan support.
Musicians	Record labels, concert promoters, sheet music publishers	Produced and distributed recordings, organized live performances, sold sheet music for home use.	Streaming platforms (e.g., Spotify, Apple Music), social media, Bandcamp	Distribute music globally, generate ad/subscription revenue, enable direct sales or fan-funded projects.
Sculptors	Patrons, city governments, workshops	Commissioned public or private sculptures, provided materials and studio space.	Art collectives, online galleries, 3D printing services	Facilitate sales through exhibitions, provide digital tools for creation, or connect with buyers online.
Actors	Theater companies, producers, patrons	Staged performances, paid actors for roles, attracted paying audiences.	Film/TV studios, streaming platforms (e.g., Netflix), talent agencies	Produce and distribute content, pay for performances, or monetize via subscriptions/ads; connect actors to opportunities.