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Monday, January 26, 2026

Clear AI Productivity? Remember History: It Will Take Time

History is quite useful for many things. For example, when some argue that AI adoption still lags, that observation, even when accurate, ignores the general history of computing technology adoption, which is that it takes longer than most expect. 


Consider a widely-discussed MIT study that was also widely misinterpreted. Press reports said the study showed AI was not producing productivity gains at enterprises.


So all we really know is that pilot projects have not yet shown productivity gains at the whole-enterprise level. And how could they? 


Much has been made of a study suggesting 95 percent of enterprises deploying artificial intelligence are not seeing a return on investment.


There’s just one glaring problem: the report points out that just five percent of those entities have AI in a “production” stage. The rest are pilots or limited early deployments. 


That significant gap between AI experimentation and successful, large-scale deployment arguably explains most of the sensationalized claim that “only five percent of enterprises” are seeing return on AI investment. 


It would be much more accurate to say that most enterprises have not yet deployed AI at scale, and therefore we cannot yet ascertain potential impact. 


But that is not unusual for any important new computing technology. Adoption at scale takes time. 


Consider the adoption of personal computers, ignoring the early hobbyist phases prior to 1981, which would lengthen the adoption period. At best, 10-percent adoption happened in four years, but 50-percent adoption took 19 years. 


It took at least five years for the visual web to reach 10-percent adoption, and about a decade to reach 50-percent usage. 


For home broadband, using a very-conservative definition of “broadband,” (perhaps 1.5 Mbps up to perhaps 100 Mbps), it took seven years to reach half of U.S. homes.  


Technology

Commercial Start (Year)

Time to 10% Adoption

Time to 50% Adoption

The "Lag" Context

Personal Computer

1981 (IBM PC launch)

~4 Years (1985)

~19 Years (2000)

High Lag. Slowed by high cost ($1,500+), lack of connectivity (pre-internet), and steep learning curve (DOS/early Windows).

Internet

1991 (WWW available)

~5 Years (1996)

~10 Years (2001)

Medium Lag. Required physical infrastructure (cables/modems) and ISP subscription growth. "Network effects" accelerated it rapidly in the late 90s.

Broadband

~2000 (Cable/DSL)

~2 Years (2002)

~7 Years (2007)

Medium Lag. Replaced dial-up. Dependent on telecom providers upgrading last-mile infrastructure to homes.

Smartphone

2007 (iPhone launch)

~2 Years (2009)

~5-6 Years (2012-13)

Low Lag. Piggybacked on existing cellular networks. High replacement rate of mobile phones accelerated hardware turnover.

Tablet

2010 (iPad launch)

~2 Years (2012)

~5 Years (2015)

Low Lag. Benefited from the "post-PC" era ecosystem. Familiar interface (iOS/Android) meant zero learning curve for smartphone users.

Generative AI

2022 (ChatGPT launch)

< 1 Year (2023)

~2-3 Years (Proj. 2025)*

Near-Zero Lag. Instant global distribution via browser/app. "Freemium" models removed cost barriers. Adoption is currently outpacing the smartphone and internet.


The point is that widespread adoption of any popular and important consumer computing technology does take longer than we generally imagine. 


AI adoption is only at the very early stages. It will take some time for workflows to be redesigned; apps to be created and redesigned and user behavior to start to match the new capabilities. 


It is unreasonable to expect widespread evidence of productivity benefits so soon after introduction, even if new technologies now seemingly are adopted at a faster rate than prior innovations.


Mission Creep: When a Problem is Solved, Go Do Something Else

One issue in public life is mission creep, the gradual and often unintended expansion of an entity’s goals, operations, scope or activities beyond the original core mission. It is a big issue for non-profits, but sometimes for logical reasons. 


When an issue is essentially “solved,” a non-profit can disband, as it has succeeded, or institutional vested interests can find a new problem to solve. In other cases, growth itself seems to become its own driver, as the logic seems to be, “if we are doing good things now, how much more impact could we have if we were bigger?”


The new goals may serve the interest of the employees or the management of the organization, who would otherwise be “out of their jobs.”


In cases where the organization’s original goals are already achieved or when the original goals are no longer necessary, goal displacement means organizations direct their energies elsewhere. 


For example an organization which was initially intended to fight polio would find a new disease to fight once the vaccine for polio is invented.


There are other implications for donors and citizens who are not inside an organization, though. There are lots of problems we need, or should work to fix. So spending resources on problems that are already largely “fixed” is wasteful. 


Spending “too much” on problems with less social or economic impact likewise is wasteful; as would be the case of directing excessive effort and resources on one problem, no matter how important, to the exclusion of many other equally-pressing issues. 


For example, some might argue we need to devote serious resources and effort to global warming, even if that means other things (eradicating malaria, improving general health, sanitation or water quality, building more housing, eliminating infant mortality) take a back seat. 


It might seem obvious this is not a good idea. 


We might see some relevance, in that regard, around the “problem” of quality internet access


There was a time when “quality” internet access (based on downstream speed) was a bigger problem in the U.S. market. That is not to say there are no issues, but useful internet access does not seem to be much of a problem for most potential users. 


And though we often focus on “supply” issues, “demand” also matters, as some customers choose not to buy fixed network internet access, while others use substitutes. And since price and speed are correlated, customers make decisions all the time about the tradeoff between price paid and typical speed received. 


Making quality internet access available is one problem. Choices consumers make is a separate issue. 


To the extent there are problems, those tend to deal with supply: is quality access available? What consumers choose to buy is not the same “problem,” indeed is simply consumer choice. 


We sometimes see data that suggests customers who buy very-low-speed services are a “problem.” Maybe, maybe not. It’s a problem if the option of buying higher speed services is unavailable. It is not a problem if a consumer makes a rational buying decision. 


Purchased plan tier (down/up)

Approx. share of U.S. broadband households

Rationale snapshot

≤25 Mbps (legacy DSL / low‑end cable)

~10–15%

FCC still tracks a small tail of low‑speed subscriptions; availability of ≥25/3 to ~98% of population suggests remaining low tiers are now a minority.itif+1

25–99 Mbps

~20–25%

Many cable and FWA “basic” plans fall here; must be below the 53% that are ≥100/20, leaving roughly a quarter of subs in sub‑100 tiers.benton+1

100–499 Mbps

~30–35%

Parks Associates reports that 100–999 Mbps is the most common tier; combining that with 53% at ≥100/20 implies a large cluster in low‑hundreds plans.benton+1

500–900 Mbps

~15–20%

Higher cable/fiber tiers in competitive markets; fills the gap between the big ≥100/20 cohort and the 26% at ~940/500.benton+1

≈940–1000 Mbps (gigabit class)

~20–30%

FCC data indicate ~26% of households subscribe at 940/500 Mbps when available, which is a reasonable center estimate for this tier’s share overall.benton


One sees this when looking at speed tiers purchased and other variables such as household income, age, educational status or geography. One might argue that supply is a bigger problem in rural areas, but not much of a problem in urban areas. 


Many observers note that incomes tend to be lower in rural areas, which can affect demand. The cost of networks in rural areas also is higher, which affects supply. 


Segment

Indicative dominant tiers (down)

Implied avg purchased Mbps per household

Implied per‑capita purchased Mbps (household Mbps ÷ 2.5)

Key drivers

Urban

Mix skewed to 300–1000 Mbps, with high gigabit availability (82% access to gigabit‑capable).opensignal

~400 Mbps

~160 Mbps/person

High availability of 100/20 and gigabit, higher income, more competition; urban speeds about 40–45% higher than rural on average.rcrwireless+2

Rural

Mix skewed to 50–300 Mbps, with less gigabit (46% access), more sub‑100 Mbps and FWA.tlp+1

~200 Mbps

~80 Mbps/person

Only 72% of rural Americans have access to 100/20; lower gigabit availability and lower observed speeds; fewer providers in many counties.tlp+2


And household income is a major driver of buyer behavior. Wealthier households tend to buy more-capable speed tiers, just as they tend to buy more and “better” goods in general. 


Approx. income band (U.S. 2024)

Rough income deciles

Likely dominant plan tier (down)

Approx. avg purchased Mbps per household

Estimated per‑capita purchased Mbps

<$35k

Bottom 2–3 deciles

25–100 Mbps (many at 50–100 Mbps; some with no fixed broadband, relying on mobile).tlp+1

~150 Mbps (conditional on having fixed broadband)

~60 Mbps/person

$35k–$55k

Deciles 3–4

100–300 Mbps

~250 Mbps

~100 Mbps/person

$55k–$85k

Deciles 5–6 (around median $84k)census

200–500 Mbps

~350 Mbps

~140 Mbps/person

$85k–$130k

Deciles 7–8

300–1000 Mbps (heavy 500–1000 Mbps share)

~500 Mbps

~200 Mbps/person

>$130k

Top 1–2 deciles

500–1000+ Mbps (gigabit default where available)

~600 Mbps

~240 Mbps/person


The other notable demand issue is that a growing percentage of customers seem to buy mobile services as a product substitute for fixed network broadband. That might be especially true of lower-income households and households of younger people. 


In other niche cases, such choices might also happen for single-user households or homes in rual areas where there are fixed network supply issues. 


Customer segment

Typical characteristics and use

Likelihood that mobile‑only is satisfactory

Why mobile‑only can work reasonably well

Low‑income, light‑use households

Limited budget; 1–2 smartphones; little or no PC; usage focused on messaging, social, short video.playablemaker+1

Medium–High

Mobile data plans and smartphones cover core communication and entertainment needs at lowest total cost.playablemaker+1

Young adults (18–29), renters

High smartphone dependence; heavy social and app use; often stream video primarily on phones; more mobile than fixed in living situations.playablemaker+2

High for individuals, Medium for shared households

5G speeds and phone‑centric lifestyles make mobile‑only viable for day‑to‑day use, especially when not sharing with many others.datareportal+1

Students in K‑12 or college

Need stable connectivity for assignments, research, video classes, uploads.weforum

Low–Medium

In limited cases, a robust unlimited plan plus hotspot can bridge a temporary gap.datareportal+1

Remote‑work professionals

Need sustained bandwidth for video calls, large file transfers, VPN, and multi‑device use.weforum

Low

5G can sometimes support remote work on the move, and as a backup, mobile‑only can be a useful failover.datareportal+1

Rural households with limited fixed options

May have only expensive satellite or slow DSL; good 4G/5G may be the best available option.weforum+1

Medium

Mobile broadband can outperform legacy DSL or expensive satellite, especially where modern macro sites or fixed‑wireless offerings exist.datareportal+1

High‑income, tech‑heavy households

Multiple devices, 4K streaming, gaming, smart‑home gear; often already subscribe to high‑speed fixed broadband.weforum

Very Low

Mobile is excellent as a secondary/backup or for travel; speeds can rival entry‑level fixed plans.datareportal+1


The broader point is that internet access, generally speaking, is a problem that is mostly “solved” for most potential users and buyers, though some issues remain. 


As with always, the amount of effort or priority we “should” be devoting to such issues should be proportional to “where” and “why” the problems may continue to exist, tailoring solutions in ways that solve the problems in an efficient way. 


Once supply issues are overcome, we might not want to exert prior levels of effort to take on new missions such as “encouraging” use of the internet, which might not be a substantial problem, or one that really needs much subsidy and effort.


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