Paysend CEO Ben Chisell is taking the opposite approach to companies that automatically issue an AI license to every employee. His policy is pro-AI but skeptical of indiscriminate access: new hires get roughly a month to test a tool, then must explain what it delivered before they keep using it. Chisell estimates the company can complete about 25% more engineering work with the same-sized team, but he says access should follow demonstrated value and responsible ownership.
“I know it sounds very unglamorous and unsexy as an approach given the world of tokenmaxxing and AI, but I think it’s really, really important,” Chisell said, according to a CNBC report carried by KhanList. The policy reflects a concern shared by many technology leaders: an AI tool can increase activity without increasing useful output, revenue or accountability.
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Why Paysend makes employees prove the value first
Paysend’s earned-access process gives a new employee approximately one month to experiment with an AI product. To continue using it, the employee must describe what the tool produced, how it changed the work and why the subscription or license is necessary.
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One search-engine-optimization lead tested a general-purpose Claude subscription. The tool helped produce briefs faster, but the resulting benefit was judged too limited to justify ongoing access. Rather than approve a license automatically, Paysend used the experiment to identify a better solution: a dedicated agent for its content-brief workflow.
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That distinction matters. Faster drafts are not automatically better briefs, and a faster process is not necessarily a more valuable process. The test asks an employee to connect AI use to a concrete business result instead of treating login activity as proof of productivity.
Chisell’s capacity, not head-count, argument
Chisell estimates that Paysend can deliver roughly 25% more engineering work with the same-sized team. The figure is his estimate, reported in 2026, not an independently audited benchmark. He says the preferred response to that capacity is to expand the team’s ambition rather than immediately reduce its size: “I personally believe you should use it to expand your ambition rather than make the team smaller.”
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How this differs from giving everyone a license
Other large employers have emphasized scale and adoption. Lloyds Banking Group reported 40,000 Microsoft 365 Copilot licenses and 97% active use among license holders in a 2026 report. Sage said its AI coding tools saved hundreds of thousands of work hours, while Grant Thornton UK reported a £500 million ($660 million) AI investment. Those figures show substantial deployment or time savings, but they do not by themselves establish a matching increase in profit or revenue.
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| Approach | Access rule | Primary control | Main risk | What success should show |
|---|---|---|---|---|
| Earned access (Paysend) | Continue after demonstrating value | User performance and proof of benefit | Slower experimentation or unequal access | More concrete output from the same team |
| Broad rollout | Licenses distributed widely | Training, licensing and adoption metrics | High spend with a weak link to revenue | Usage connected to measurable business results |
| Agent-heavy development (CoreView) | Broad developer access with coordinated agents | Permissions, workflows and technical oversight | False efficiency when work exceeds expertise | Shorter cycles without quality or reliability loss |
Why feeling faster can be misleading
CoreView CEO Simon Azzopardi describes a failure mode that broad access can hide: “You feel more efficient, but you actually aren’t.” He was referring to people using AI to build outside their expertise. Agents can coordinate tasks and compress a timeline, but a user may not recognize flawed architecture, weak security, incorrect assumptions or poor-quality output in an unfamiliar domain.
That is why the relevant question is not simply how many tasks an agent completed. A responsible review checks whether the work was correct, maintainable, secure and useful to the customer or internal team. A short cycle time paired with rework is not a productivity gain.
How to measure workplace AI productivity
Companies deciding between earned access and broad licensing should measure more than seats, prompts or active users.
- Define the baseline: record cycle time, defect rate, rework, throughput and customer or business outcomes before introducing the tool.
- Measure quality with speed: compare delivery time alongside review findings, incidents, failed releases and downstream corrections.
- Track outcomes by workflow: distinguish a faster content brief, code review or support response from an outcome that actually improves conversion, retention, reliability or cost.
- Check expertise and permissions: give users access appropriate to their role, and require technical or managerial review when agents operate beyond the user’s competence.
- Decide what happens to capacity: document whether saved time funds new projects, higher service levels, training or a reduction in staffing demand.
- Reassess licenses: remove tools that produce activity without durable value and replace general-purpose access with a focused workflow when that is more effective.
Human ownership remains the control point
Paysend’s policy does not remove humans from AI-assisted work; it makes responsibility explicit. “Having a human in the loop shouldn’t mean human CC’d. It should mean human ownership,” Chisell said. The employee or manager who approves the output remains accountable for its accuracy, consequences and fit for purpose, even when an agent performs most of the intermediate steps.
Best Value
For companies, the practical choice is therefore not “AI for everyone” versus “AI for top performers.” It is whether access, training, permissions and review are tied to evidence of value. Broad deployment can be sensible when a workflow is well understood and results are measurable. Earned access is more defensible when tools are expensive, risks vary by role or the organization cannot yet show that activity translates into business outcomes.
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