Anthropic's fifth Economic Index report reveals a striking finding: people who have been using Claude for six months or longer succeed in their conversations at measurably higher rates than newer users, and this gap persists even after accounting for task type, model selection, and geographic origin. The analysis, based on 1 million conversations from Claude.ai and its first-party API (Application Programming Interface, the developer-facing interface for integrating Claude into products) collected between February 5 and February 12, 2026, suggests that expertise with the AI assistant compounds over time in ways that go beyond simply learning which model to choose .

What's Driving the Success Gap Between Experienced and New Claude Users?

The research team, led by Maxim Massenkoff, Eva Lyubich, and Peter McCrory, examined how tenure shapes what users get out of Claude's platform. The finding that experienced users outperform newcomers is particularly significant because the researchers controlled for variables that might otherwise explain the difference. They accounted for the type of task being performed, which Claude model was selected (Haiku, Sonnet, or Opus), the user's country of origin, and the specific use case. Even with these factors held constant, the tenure effect remained robust .

This suggests that long-term users have developed tacit knowledge about how to interact with Claude effectively. They may be better at crafting prompts, understanding the model's strengths and limitations, or knowing when to iterate on a response rather than accepting the first output. The 10% success rate advantage is not trivial in professional contexts where productivity gains compound across hundreds of interactions per month.

How Are Different User Groups Using Claude Differently?

Between November 2025 and February 2026, Claude.ai usage became noticeably less concentrated. The top 10 most common occupational tasks fell from 24% of all conversations to 19%, indicating that users are exploring a wider range of applications. This diversification reflects two distinct forces working simultaneously .

First, coding work continued shifting from Claude.ai to the first-party API, where Claude Code has grown substantially. Claude Code's agentic architecture, which breaks coding work into smaller API calls, distributes coding-related traffic across many categories rather than concentrating it in a few high-volume buckets. Computer and Mathematical tasks increased by 14% in the API while declining by 18% on Claude.ai since August 2025 .

Second, the user base itself changed. Coursework conversations fell from 19% to 12% of Claude.ai traffic, partly because the sampling window overlapped with winter academic breaks in several major countries. The report notes that the drop in coursework was 5 percentage points in countries where school terms were active and 12 percentage points in countries where students were on break. Meanwhile, personal use rose from 35% to 42%, driven by increasing sign-ups of more casual users. Anthropic's Super Bowl advertising campaign during the same period brought many first-time users to the platform .

Steps to Maximize Your Claude Usage Based on Task Complexity

• Match Model Selection to Task Difficulty: Use Claude Opus for complex, high-value work requiring premium reasoning; reserve Claude Sonnet for moderate-complexity tasks; and use Claude Haiku for simple queries and fact-checking to optimize both cost and performance.
• Develop Iterative Prompting Habits: Experienced users likely refine their prompts based on initial responses rather than accepting first outputs, suggesting that building a practice of follow-up questions and clarifications improves success rates over time.
• Explore API Integration for Specialized Workflows: If you perform coding, sales automation, or trading-related tasks regularly, migrating to the first-party API unlocks Claude Code and agentic capabilities that distribute work more efficiently than the web interface.

Where Is Claude Being Used for High-Value Work?

The economic value of tasks on Claude.ai, measured as the average hourly wage of the occupations involved, slipped from $49.3 per hour in January 2025 to $47.9 per hour in February 2026. That figure remains well above the US average hourly wage of $37.3. The report attributes the decline mainly to growth in simple factual queries, such as sports outcomes, weather, and product comparisons, rather than any structural retreat from high-value work. On the API side, the average task value has risen consistently, reaching $50.7 per hour in February 2026 .

Two API workflow categories saw their usage at least double between November 2025 and February 2026. The first is business sales and outreach automation, encompassing sales enablement generation, B2B lead qualification research, customer data enrichment, and cold-email drafting. The second is automated trading and market operations, including monitoring market positions, proposing specific investments, and informing traders of market conditions. Both represent categories in which a human is largely absent from individual interactions, a pattern the report associates with more imminent labor market exposure for the associated occupations .

Is Claude Adoption Becoming More Equal Globally?

A persistent theme across all five Economic Index reports has been geographic concentration, and the latest data shows this pattern is not closing. According to the February 2026 data, the top 20 countries by per-capita usage accounted for 48% of all usage adjusted for population, up from 45% in November 2025. The Gini coefficient for cross-country usage rose over the same period, indicating that the gap between high-adoption and low-adoption countries widened slightly .

Within the United States, the picture is different. States with lower per-capita usage continued converging toward the national mean, though at a slower pace than in earlier reports. The share of per-person usage accounted for by the top five states fell from 30% to 24% between August 2025 and February 2026. However, the researchers revised their earlier projections: at the current pace, US states would reach roughly equal per-capita Claude usage in five to nine years, rather than the two-to-five-year estimate published previously .

About 49% of jobs have now seen at least a quarter of their tasks performed using Claude, a figure that barely changed from the previous report. This suggests that the breadth of occupational exposure has stabilized even as depth varies considerably across different industries and regions .

How Do Users Choose Between Claude's Different Models?

One of the report's analytical chapters examines how users distribute their work across Claude's three model classes: Haiku, Sonnet, and Opus. The Opus class uses the most tokens and is priced at a premium on the API, but offers the strongest performance on complex tasks. If users are calibrating rationally, Opus should appear more often on harder, higher-wage work .

That is broadly what the data shows. According to the report, 51% of overall usage on paid Claude.ai accounts involves Opus. Among Computer and Mathematical tasks, which include software development, that figure rises to 55%, representing a 4.4 percentage point overrepresentation relative to the average. Educational tasks, by contrast, see Opus used at a rate 6.5 percentage points below average .

The relationship is roughly linear. On Claude.ai, for every additional $10 per hour in occupational wage associated with a task, the share of conversations using Opus increases by 1.5 percentage points. That slope is about twice as steep for first-party API users, at 2.8 percentage points, suggesting that developers integrating Claude into products are even more sensitive to task complexity when selecting models .

The widening success gap between experienced and new Claude users underscores a broader truth about AI tools: they reward expertise. As the platform matures and more users accumulate months of experience, those who have invested time in learning Claude's capabilities and limitations will continue to pull ahead. For organizations and individuals just beginning their Claude journey, this finding suggests that the learning curve is real, but the payoff is substantial.