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A New Study from Harvard and Perplexity Finds AI Agents Perform 26 Minutes of Autonomous Work per Session vs 33 Seconds for Search

A new working analysis from Perplexity and Harvard gives subject proof on what AI brokers do to data work. It attracts on manufacturing knowledge from two Perplexity merchandise: Search and Computer.

The setup is a pure comparability. Search is a conversational reply engine. Computer is an agent that plans and executes duties finish to finish. The identical customers contact each merchandise, so the workforce can maintain the duty roughly fixed.

What the Study Actually Measures

The analysis examine covers a 90-day window, February 27 by May 27, 2026. Computer launched two days earlier than that window opened.

The core technique matches near-identical question pairs throughout the 2 merchandise. The analysis workforce discovered 10,000 session pairs with cosine similarity above 0.99. Each pair is successfully the identical activity tried each methods.

Computer pairs are gated to periods that invoke an execution device. These ‘do’ instruments embrace code execution, browser actions, file writes, and connector calls. That gate ensures each Computer session does actual autonomous work.

Adoption rose over the window. Cumulative Computer queries reached 84× their first-week whole. A matched evaluation discovered Computer adoption additionally raised customers’ each day Search queries by 1.05. The optimistic impact factors to complementarity, not substitution.

https://analysis.perplexity.ai/articles/how-ai-agents-reshape-knowledge-work

The Cost-Structure Framework

The analysis grounds its knowledge in a easy task-based mannequin. Each activity has a step depend, and longer duties carry weakly larger worth.

Agents change the fee construction. They cost the next mounted value per activity, for delegation and evaluate. But they cost a decrease marginal value per step, because the system executes.

This produces a breakeven step depend. Below it, the conversational mode is cheaper. Above it, the agent mode wins. Short lookups keep handbook; lengthy workflows transfer to the agent.

Autonomy: 26 Minutes vs 33 Seconds

The first autonomy measure is execution time. Computer runs 26 minutes of machine work per session. Search runs 33 seconds. That is a 48× hole.

Medians present the identical sample: 9 minutes versus 14 seconds. The hole varies by area. Local duties present 75×; Science exhibits 26×, since plain solutions typically suffice.

Higher autonomy didn’t decrease high quality right here. The analysis workforce scored next-turn dissatisfaction from what customers do subsequent. Computer’s significant dissatisfaction charge was 1.3%, in opposition to 2.9% for Search (55% discount).

Follow-up turns additionally shift towards evaluate and extension on Computer, although the adjustments are small. Connector utilization rose extra clearly. Computer invoked a minimum of one connector in 7.9% of periods, versus 1.8% for Search. Computer chains exterior instruments that Search customers would in any other case run by hand.

Efficiency: Where the Savings Come From

The effectivity part estimates a Search + Human counterfactual. A human with Search alone takes 269 minutes per matched activity. Computer + Human takes 36 minutes.

That is 87% much less time and 94% much less value total. Cost financial savings exceed time financial savings as a result of area wages amplify the impact. Computer’s mannequin value runs $4–10 per activity; Search runs about $0.05.

The marginal numbers help the framework. Computer + Human prices $0.16 per step, versus $2.05 for Search + Human. Matched Computer periods additionally ran longer prompts, 652 versus 448 characters on the median. That helps the upper fixed-cost assumption for brokers.

Breakeven evaluation says knowledgeable should end all handbook steps in beneath 20 minutes to match Computer. The analysis workforce cross-checked with an unbiased LLM estimate and consumer interviews. The LLM technique discovered 84% time and 93% value financial savings. Interviewees reported speedups from 5× to 300×.

Horizontal and Vertical Expansion

Scope is the place this analysis extends previous prior work. Autonomy doesn’t simply velocity up duties. It adjustments which duties customers try.

Horizontally, Computer queries cross occupational traces extra typically. Cross-occupation share averaged 59% on Computer, versus 50% on Search. Management and Entrepreneurship confirmed the biggest hole, at 19 factors.

Vertically, Computer queries are extra demanding. On Bloom’s Revised Taxonomy, 76% required higher-order cognition, versus 55% for Search. Create-level work was 50% of Computer queries, in opposition to 26%.

Computer duties additionally span extra data domains. Each question touched 2.40 O*NET Knowledge domains on common, versus 1.74. It was almost 3 times as more likely to want three or extra domains.

Composability climbs because the O*NET hierarchy will get finer. At the Task Statement stage, Computer engaged 60% extra actions. About 23% of Computer queries hit a Task Statement that the identical customers by no means despatched to Search.

https://analysis.perplexity.ai/articles/how-ai-agents-reshape-knowledge-work

Comparison Table: Search vs Computer

Dimension Perplexity Search Perplexity Computer
Mode within the framework Conversational reply engine Agent orchestrator
Machine time per session 33 seconds (median 14s) 26 minutes (median 9m)
Queries per session 2.8 5.3
Meaningful (mid+excessive) dissatisfaction 2.9% 1.3%
Sessions with a connector name 1.8% 7.9%
Counterfactual activity time 269 min (Search + Human) 36 min (Computer + Human)
Cost per step $2.05 $0.16
Model value per activity ~$0.05 $4–10
Cross-occupation question share 50% 59%
Higher-order Bloom cognition 55% 76%
O*NET Knowledge domains per question 1.74 2.40

(*33*)Key Takeaways

  • Computer runs 26 minutes of autonomous work per session versus 33 seconds for Search, a 48× hole.
  • On matched duties, Computer + Human cuts estimated time 87% and value 94% versus Search + Human.
  • Computer’s significant dissatisfaction charge is 1.3% versus 2.9% for Search, a 55% discount.
  • Computer queries cross occupations extra (59% vs 50%) and demand extra higher-order cognition (76% vs 55%).
  • About 23% of Computer queries hit a Task Statement the identical customers by no means despatched to Search.

Marktechpost’s Visual Explainer

Research Guide
Harvard × Perplexity

01 / 10

How AI Agents Reshape Knowledge Work

Autonomy, Efficiency, and Scope — subject proof from manufacturing knowledge.
  • A new examine compares an autonomous agent with a conversational search assistant.
  • It makes use of actual utilization knowledge from Perplexity Search and Perplexity Computer.
Jeremy Yang (Harvard) · Kate Zyskowski, Noah Yonack, Jerry Ma (Perplexity) · arXiv:2606.07489v1

02 / 10

What the Study Measures

A matched-pair design holds the duty roughly fixed throughout merchandise.
  • 90-day window: February 27 to May 27, 2026.
  • 10,000 matched session pairs with cosine similarity above 0.99.
  • Computer periods are gated to “do” instruments: code execution, browser actions, file writes, connector calls.
  • The identical dual-product customers seem on either side.
03 / 10

The Cost-Structure Framework

A easy task-based mannequin explains when delegation pays off.
  • The agent mode prices a larger mounted value per activity, for delegation and evaluate.
  • It prices a decrease marginal value per step, because it executes.
  • A breakeven step depend kinds work: brief under it, agent above it.
  • Task choice is modeled as a 0-1 knapsack drawback.
04 / 10

Autonomy: Machine Work per Session

Higher autonomy didn’t come at a high quality value right here.
26 min vs 33 s
Autonomous work per session (48× hole)
9 min vs 14 s
Median session time (40× hole)
1.3% vs 2.9%
Meaningful dissatisfaction (55% decrease)
7.9% vs 1.8%
Sessions invoking a connector name

05 / 10

Efficiency: Time and Cost

Estimated in opposition to a Search + Human counterfactual on matched duties.
269 → 36 min
Average activity completion time
87% / 94%
Time saved / value saved total
$0.16 vs $2.05
Cost per step (Computer vs Search + Human)
< 20 min
Manual-step breakeven to match Computer

06 / 10

Scope: Broader and Harder Work

Autonomy adjustments which duties customers try, not simply their velocity.
  • Horizontal: cross-occupation share 59% vs 50% (Management & Entrepreneurship +19 pp).
  • Vertical: higher-order Bloom cognition 76% vs 55%.
  • Create-level work: 50% of Computer queries vs 26% for Search.
  • Knowledge breadth: 2.40 vs 1.74 O*NET domains per question (+38%).
07 / 10

What Computer Unlocks

Distinctiveness lies in fine-grained executional work, not topical vary.
  • +60% extra O*NET Task Statements engaged per question than Search.
  • 23% of Computer queries hit a Task Statement the identical customers by no means despatched to Search.
  • Gains focus in software program and net growth, documentation, and knowledge visualization.
08 / 10

Search vs Computer

Side-by-side throughout the examine’s major measures.
Dimension Search Computer
Machine time per session 33 s (med 14 s) 26 min (med 9 m)
Queries per session 2.8 5.3
Meaningful dissatisfaction 2.9% 1.3%
Cost per step $2.05 $0.16
Cross-occupation share 50% 59%
Higher-order cognition 55% 76%
O*NET domains per question 1.74 2.40

09 / 10

Use Cases for Engineers

How the findings map to day-to-day technical work.
  • Data scientists: single duties span Design, Mathematics, and Economics and Accounting.
  • Software engineers: the agent writes recordsdata, runs code, and deploys; you supervise.
  • AI engineers: route brief lookups to a conversational path, lengthy workflows to an agent.
10 / 10

The Takeaway

From velocity to scope.
  • Time and value financial savings are massive however anticipated.
  • The sharper discovering is broader, extra complicated work tried.
  • The sensible lesson is task-tool match: match the device to the step depend.
Source: How AI Agents Reshape Knowledge Work: Autonomy, Efficiency, and Scope (arXiv:2606.07489v1).

(*26*)‹

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