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What Lütke’s memo actually required
The key wording reported by TechCrunch was: “Before asking for more headcount and resources, teams must demonstrate why they cannot get what they want done using AI.” (TechCrunch, April 7, 2025)
The memo treated AI use as a baseline expectation rather than an optional experiment. Lütke also asked managers to imagine how their functions would operate if autonomous AI agents were already members of the team. Reports said AI use could become relevant to performance reviews, peer reviews, business reviews and product development, while the stated aim was to multiply the output of employees who learn to use the tools effectively. (The Information)
Is Shopify under a hiring freeze?
| Question | What the public evidence supports |
|---|---|
| Must teams consider AI before requesting staff? | Yes. New headcount and resource requests are expected to explain why AI cannot accomplish the desired work. |
| Did Shopify ban every external hire? | Not established by the publicly reported memo. |
| Must every departing employee be replaced? | Not established. Some coverage presented this as a possible interpretation, but it is not unambiguous memo language. (Windows Central) |
| Can Shopify still hire for work AI cannot safely own? | The wording leaves room for hires justified by capability, accountability, growth or other business needs. |
“No new hires” is therefore a headline shorthand that can overstate the rule. Fortune and TechCrunch described a requirement to justify why AI could not perform the work, not an unconditional prohibition on hiring. (Fortune; TechCrunch)
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Shopify’s broader AI-first strategy
Shopify later described the operating principle as “default to AI.” In an October 2025 company account, it reported universal adoption of AI code editors, thousands of Cursor licenses, frequent use of internal AI tools and broad employee access to leading models. (Shopify)
That follow-up suggests the April memo was intended to change workflow and resource allocation, not simply generate publicity. AI fluency was being positioned as a professional and managerial competency across the company, although Shopify did not publish evidence that every occupation could be performed autonomously.
What “AI can’t do the job” should mean in practice
A credible headcount request should compare the complete business outcome—not just whether a model can produce an impressive sample. Managers should document:
- Task coverage: Which parts of the proposed role can the tools perform, and which remain human-owned?
- Quality and failure rates: Are results accurate and reliable enough for the customer, product or internal decision?
- Review burden: How much expert checking, correction and rework is required?
- Speed: Does the workflow reduce elapsed time after prompting, review and integration?
- Security and privacy: Can company or customer data be used under approved controls?
- Compliance: Are legal, contractual, regulatory or audit obligations satisfied?
- Accountability: Who owns the outcome when an AI-generated decision causes harm?
- Context and relationships: Does success depend on trust, negotiation, empathy, leadership or undocumented institutional knowledge?
- Total cost: Include licenses, implementation, training, monitoring, integration and error correction—not only the subscription.
- Alternatives: Compare automation with a hire, contractor, internal transfer or process redesign.
“AI can produce an output” is not the same as “AI can replace a role.” A position may contain automatable tasks while still requiring a human owner.
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| More amenable to assistance | Less suitable for autonomous substitution |
|---|---|
| Routine coding and code transformation | Executive accountability and people management |
| Drafting, editing and summarization | Sensitive employee relations |
| Internal knowledge search and research synthesis | Complex sales, negotiation and legal advice |
| Customer-support triage | Security incident response and high-stakes financial decisions |
| Data cleaning, simple analysis, testing and documentation | Physical work or decisions requiring trust, empathy or changing context |
These are task-based examples, not Shopify’s classification of particular jobs. Even an apparently automatable workflow can fail when information is confidential, poorly structured or constantly changing.
What the policy means for Shopify employees
- Employees are expected to learn how AI applies to their craft, rather than wait for a separate automation team.
- Repetitive work may disappear, creating capacity for higher-value product, customer or judgment work.
- Performance and peer reviews may increasingly consider effective AI use. (The Information)
- Workers may be expected to deliver materially more without additional staffing.
- People can be unfairly judged if approved tools are unreliable, unavailable, poorly trained or unsuited to their data.
- Responsibility for productivity gains can shift from management investment to individual employees unless training, access and workload are addressed.
The strongest case for the policy—and the strongest criticism
Why a company might adopt it
AI can let an existing team handle more work without proportional headcount growth. Requiring an AI review also forces managers to examine inefficient processes before assuming that adding people is the answer. It creates an incentive to test agents, redesign workflows and control operating costs while continuing to invest in products.
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Why employees and investors should be cautious
Leaner staffing can reduce resilience when people leave, systems fail or demand spikes. Generated work can create a checking bottleneck, and a policy framed as “use AI” can become a demand for more output rather than a reduction in low-value work. If managers must prove that AI cannot help, they may understate security, quality or accountability risks to win approval for staff.
Common edge cases and failure modes
- Partial automation: One AI-assisted hire may be better than either zero hires or a fully manual team.
- Unavailable approval: A capable tool is irrelevant if privacy or security rules prohibit its use on the required data.
- Review bottlenecks: AI can increase output faster than qualified staff can verify it.
- Rare, high-stakes work: Low frequency does not make an incorrect legal, safety or financial result acceptable.
- Growth work: AI may reduce the cost of current operations while people are still needed to open markets or build new products.
- Bad measurement: Counting generated text or code instead of correct, completed outcomes disguises added rework.
- Weak governance: Employees need approved tools, training, clear standards and a way to challenge an unsafe AI-first decision.
What companies could test before adding headcount
The relevant commercial question is not which assistant has the lowest per-seat price, but whether a controlled pilot improves a defined workflow without unacceptable risk.
| Tool | Useful test | Published price signal and constraints |
|---|---|---|
| Microsoft 365 Copilot | Drafting, meeting follow-up, analysis and search across Microsoft 365 data. | The U.S. business page displayed $25.20 per user/month with a monthly commitment and an annual-price presentation beginning at $18 per user/month when checked; a qualifying Microsoft 365 plan is required. Prices and billing terms are date-sensitive. Copilot Chat and the paid add-on have different data access and agent capabilities. (Microsoft support) |
| GitHub Copilot | Coding, refactoring, tests, documentation and repository search. | Business was listed at $19 per user/month and Enterprise at $39 per user/month in the captured pricing; organization billing also includes AI-credit allocations and usage-based charges. Some completions are included while chat and agent interactions can consume credits. (Billing documentation; Usage billing) |
| Shopify’s AI ecosystem | Merchant support and Shopify-specific workflows, including Sidekick and internal tools. | Best suited to Shopify merchants or teams; it is not a general replacement for a cross-department knowledge platform or human accountability. |
Any pilot should record baseline cycle time, quality, review hours, error consequences, data exposure and total cost. A successful demonstration that AI accelerates tasks is evidence for redesigning a job—not automatic evidence that no human is needed.
Bottom line
Shopify established an AI-first presumption for staffing decisions on April 7, 2025: teams seeking headcount or resources must show why AI cannot deliver the required outcome. The public record does not establish that Shopify stopped all hiring, refused every vacancy replacement or proved that complete occupations can be run by AI. The durable lesson for other employers is to evaluate people, software, process and oversight together, using measured outcomes and explicit accountability.
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