Anthropic Survey Finds 81% of Respondents Think AI is Creating Value

After surveying 80,508 people across 159 countries and 70 languages, here is how Anthropic assesses the hopes people have for artificial intelligence. The survey asked:

• what people want from AI

• whether they’re getting what they want

• what they fear

• what they do for a living

• sentiment about AI overall.

Roughly a third of visions are about making room for life (more time, money, mental bandwidth). About a quarter revolve around using AI to help people do better, more fulfilling work.

About a fifth of responses are about becoming someone better (learning, healing, growing). 

A smaller share want to make something (“creative expression”) or fix the world (“societal transformation”). Those that wanted societal transformation from AI often cited a vision for healthcare. People wanted AI to detect cancer earlier, accelerate drug discovery, or enable broad access.

Respondents in low and middle income countries were quick to cite the possibility that AI might break the association between educational quality and wealth.

Some of you might be encouraged: these are normal, human hopes for new technology. 

source: Anthropic 

And even if user beliefs about impact are subjective (people might think an innovation has one impact; it might not), the survey suggests people believe AI is having an impact.

source: Anthropic 

About 81 percent of respondents said AI already has helped them fulfill their own stated visions for benefit. 

Granted, those are subjective responses, and far from the quantifiable outcomes investors ultimately will expect. But in the near term, we will have to keep looking for value proxies.

"Not Seeing AI Productivity" Storyline is Inevitable

It’s inevitable that we will keep seeing headlines, and seeing, hearing and reading stories about how many businesses are not seeing financial returns from their investments in artificial intelligence. 

Important new technologies rarely show up in the bottom line immediately, and the issues are structural.

First of all, business processes have to be recreated to harness the innovations. 

When electricity first entered factories, managers simply replaced their massive central steam engine with one massive electric motor. Productivity didn't move. Only after firms discovered they could put a small motor on every individual machine (the "unit drive") were they able to redesign the factory floor. 

In 2026 companies are using AI to "chat with docs" or "summarize emails" (overlaying tech on old habits) rather than redesigning their entire supply chains. That will take some time. 

Also, firms must retrain workers and staffs. That imposes real costs (time and money) while possibly lowering productivity in the short run as time and effort is diverted to such training. So a "J-curve" of productivity will happen: lower productivity in the near term, with the benefits in the future. .

Then there are measurement issues, such as how to quantify the impact of quality, variety and speed. If an AI helps a legal team finish a contract in two hours instead of 10, but the firm still charges a flat fee, the "productivity" is invisible to the GDP, even though the human cost has plummeted.

Study	Technology Period	Key Finding	Duration of Lag
Paul David (1990)	Electricity (1890–1920)	Factories had to be physically demolished and rebuilt to utilize "unit drive" motors before TFP spiked.	~30–40 Years
Robert Solow (1987)	Computing (1970–1990)	The "Solow Paradox": You could see the computer age everywhere except in the productivity statistics.	~20 Years
Brynjolfsson et al. (2021)	AI & Software (2010–2021)	Formulated the "Productivity J-Curve"; firms must invest in unmeasured "intangible assets" that initially depress earnings.	Ongoing
NBER / Juhász et al. (2020)	19th Century France	Productivity in mechanized spinning was initially lower and more dispersed than hand-spinning due to the need for factory reorganization.	~15–20 Years
Man Group / Bara (2026)	Generative AI (2023–2026)	80% of firms report no macro productivity impact yet, despite task-level gains of 15-55%, due to "workflow friction."	Projected 5-10 Years

In the meantime, leaders will have to try and come up with some quantifiable metrics (directly related or not) to justify the investments. It won’t take too much imagination to realize that headcount reductions are one such way to “show” outcomes, even if AI and headcount are indirectly, loosely or even unrelated in the short term. 

In 1900 the "electricity bubble" looked real to everyone still using steam. By 1920, the steam users were bankrupt. 

So “productivity proxies” must be developed.

The most immediate impact of AI is the compression of time. 

Firms can measure the "distance" between an idea and its execution.Time-to-prototype can show how many days it takes to move from a natural language prompt or requirement to a functional, testable version.

Draft-to-final ratio might be used by marketing and legal firms to measure the time spent on the "first 80 percent" of a task versus the "final 20 percent" of human polishing.

For engineering teams, the metric isn't just "lines of code," but the number of successful pushes to production per developer per week. 

Larger firms might try to assess the reduction in total "human hours" spent in meetings.

Query-to-find latency is a measurement of how long it takes an employee to retrieve a specific piece of internal tribal knowledge. AI should reduce that latency. 

Admin-to-maker ratio tracks whether the percentage of an employee's day spent on "coordination" is shrinking in favor of "creation." 

“Agents” also will need new metrics that quantify AI outcomes as though it were a digital employee rather than a software tool.

Autonomous completion rate is the percentage of workflows that an AI agent initiates and completes without a human "click" or intervention.

Human-in-the-loop friction measures how often an agent has to "hand back" a task to a human because it hit a reasoning wall. A falling HITL rate is a leading indicator of future productivity.

Token efficiency per outcome calculates the cost of AI "thinking" (API/Compute costs) relative to a successful business outcome. 

Business Function	Traditional Metric (Lagging)	AI Proxy Metric (Leading)	Why it Matters
Software Engineering	Lines of Code, Story Points	PR Cycle Time	Measures how fast code is reviewed and merged, not just typed.
Legal, Compliance	Billable Hours	Review Velocity per Page	Shows the acceleration of document ingestion and risk flagging.
Customer Support	First Response Time	Resolution via Zero-Touch	Measures the percentage of issues solved entirely by agents.
R&D	Patents Filed, Products Launched	Iteration Cycles per Quarter	Shows how many "failed fast" experiments the firm can run.
Human Resources	Headcount Growth	Talent Density (Revenue/FTE)	Measures if the firm is scaling output without scaling people.

The “productivity lag” is entirely predictable. So are the storylines about it. Sure, it is a significant practical problem for those firms making the investments. But the “lag” storyline is entirely predictable.

Outcomes Matter, Not Virtue Signaling

Adam Garfinkle's book Telltale Hearts argues that the U.S. antiwar movement of the 1960s (yes, Baby Boomers) did not meaningfully shorten the Vietnam War and may actually have prolonged it. 

That matters if you think it is more important to “do good” than to “feel good;” better to accomplish a change than simply to “virtue signal.” 

The attack is upon the  narrative, arguably central to Boomer self-understanding, that their activism decisively “ended the war.” He argues that story is emotionally satisfying but incorrect. 

For a generation that prides itself on being “transformational,” that puncturing of a myth might be uncomfortable, but a useful antidote to ingrained arrogance. 

Oddly enough, Garfinkle argues, both opponents of the war and those who believe it might actually have been won by the United States seem to agree on the movement’s impact. But both sides might be wrong. 

Garfinkle challenges the widespread belief that protests forced U.S. withdrawal and instead argues the movement had “marginal impact” (and maybe almost none) on ending the war. 

In fact, he says the movement actually was counterproductive:

• provoked backlash

• strengthened hardline positions

• disrupted conventional political processes that might otherwise have constrained the war.

His most startling argument is that the protests might actually have extended the conflict and increased casualties. 

And other authors have made similar claims about a generation that might have created as many problems as it believes it solved:

• Boomers: The Men and Women Who Promised Freedom and Delivered Disaster – Helen Andrews
  Argues Boomer elites reshaped institutions (media, politics, religion) in ways that produced long-term dysfunction

• A Generation of Sociopaths – Bruce Cannon Gibney
  A blunt critique claiming Boomers extracted economic and social value while leaving debt and institutional decay

• The Narcissism Epidemic – Jean Twenge
  Connects Boomer-era cultural shifts to rising individualism and narcissism (though broader than just Boomers).

• The Sixties – Todd Gitlin
  Written by a former activist; acknowledges idealism but also fragmentation, excess, and unintended consequences

• The Unraveling of America – Allen J. Matusow
  Examines how 1960s movements reshaped institutions, often with ambiguous or destabilizing results

• One Nation, After All – Alan Wolfe
  Suggests Boomer-driven cultural change led to a more tolerant but also more morally fragmented society.

Garfinkle’s work is narrower (focused on Vietnam), but:

• Challenges moral self-congratulation

• Highlights unintended consequences

• Separates cultural impact from policy impact (huge in one, limited in the other)
  
  

Many will argue boomers were enormously influential. But influence is not the same as positive outcomes.

I may be a boomer, but I do not buy the self-congratulatory plaudits. Perhaps we meant well. But what matters are outcomes, not feelings.

Boomer economic impact likely is mixed, at best.

Author	Positive Effects	Negative Effects	Net View
Bruce Cannon Gibney (A Generation of Sociopaths)	—	Asset inflation, entitlement expansion, public debt burden shifted to younger generations	Strongly negative
Helen Andrews (Boomers)	Some institutional dynamism	Mismanagement of institutions, short-termism	Mostly negative
William Strauss & Neil Howe (Generations, The Fourth Turning)	Innovation, growth cycles	Fiscal imbalances, intergenerational strain	Cyclical / mixed

Boomer political or institutional impact might be a mix of positive and negative. 

Author	Positive Effects	Negative Effects	Net View
Adam Garfinkle (Telltale Hearts)	Raised awareness of war	Undermined political cohesion; limited policy effectiveness; possible prolongation of Vietnam War	Negative
Todd Gitlin (The Sixties)	Expanded democratic participation	Fragmentation, radicalization weakened movements	Mixed
Alan Wolfe (One Nation, After All)	Greater tolerance, pluralism	Decline in shared moral frameworks	Tradeoff

Cultural or social impact might be the most-questionable area of influence. 

Author	Positive Effects	Negative Effects	Net View
Jean Twenge (The Narcissism Epidemic)	Self-expression, individual empowerment	Rising narcissism, fragility, decline in social cohesion	Negative
Todd Gitlin	Liberation movements, civil rights gains	Excess, identity fragmentation	Mixed
Daniel Bell	Cultural creativity	Breakdown of norms supporting institutions	Tradeoff
Alan Wolfe	Tolerance, reduced prejudice	Moral relativism, weaker shared norms	Tradeoff