How AI Is Changing Technical Interviews in 2026

AI has not replaced the technical interview with one new format. It has created a messy transition. Employers are adding automated interviews, changing take-homes that models can solve, testing AI-assisted work, and retaining live coding or system design. Candidates should expect a mixed pipeline and demand clear rules for each round.

How common are AI interviews?

Greenhouse's 2026 Candidate AI Interview Report surveyed 2,950 active job seekers across the United States, United Kingdom, Ireland, Germany, and Australia. Sixty-three percent said they had experienced an AI interview.

The same survey found:

• 70 percent of candidates who experienced AI evaluation said they were not clearly told in advance;
• 38 percent said they had left a hiring process because it included an AI interview;
• 33 percent identified pre-recorded video scored by AI with no human present as a reason to withdraw.

This is a candidate survey produced by a hiring-platform vendor. It measures reported experience and attitudes across five countries, not verified employer adoption across the entire labour market.

The strong conclusion is not that every interview is now automated. It is that enough candidates encounter AI evaluation for disclosure and human oversight to matter.

What parts of the pipeline are changing?

Screening and recorded interviews

Some employers use chat, voice, or video systems before a human conversation. Candidates may be scored, summarized, or routed by software.

Ask:

• Is AI evaluating or only transcribing?
• What data is recorded and retained?
• Which traits or qualifications are assessed?
• Does a human review the result?
• How can I request an accommodation or alternative?

In the United States, the EEOC and Department of Justice warn that algorithmic hiring tools can screen out qualified people with disabilities and that employers need accommodation processes. In New York City, certain automated employment decision tools require a recent bias audit, public information, and candidate notice under Local Law 144 guidance.

Those rules have specific legal scopes. They are not a universal global standard or a guarantee that an individual tool is fair.

Take-home assessments

Take-homes are easier to delegate to coding models, so employers must choose whether to ban assistance, allow and inspect it, or redesign the work.

Anthropic documented three iterations of one performance-engineering take-home as newer Claude models matched increasingly strong human performance under the assessment's time limit. More than 1,000 candidates had completed the original test. Anthropic ultimately moved toward more novel problems.

That case is unusually transparent and comes from one team. It demonstrates why assessment design is changing, not that all take-homes are invalid.

Live technical interviews

Official company guidance still emphasizes live skill.

OpenAI's interview guide says engineering interviews evaluate solution design, code quality, performance, testing, communication, and collaboration. Anthropic's careers page says candidates use shared coding environments and are expected to explain tradeoffs and write, run, and debug solutions.

The tools may change, but observable reasoning and engineering ownership remain.

AI-assisted work

Some assessments explicitly allow an assistant and evaluate how the candidate frames, delegates, checks, and improves the result. Permission must be explicit. See the round-by-round AI-use guide before assuming a tool is allowed.

Is the LeetCode era over?

No evidence supports that universal claim.

Classic algorithm questions remain in many interview loops. At the same time, technical-hiring respondents increasingly value work closer to production.

CoderPad's 2026 State of Tech Hiring includes more than 650 global participants from its developer and hiring community. Respondents expected debugging, system design, fine-tuning, and collaboration to gain importance relative to writing new code. Because the sample comes through a technical-assessment vendor, treat it as directional.

Prepare for both fundamentals and production judgment until the recruiter provides a role-specific process.

What should candidates do differently?

Get the rules in writing

Ask which rounds permit AI, web search, autocomplete, notes, or external documentation. Ask whether AI use must be disclosed.

Prepare evidence, not slogans

For each project, explain:

• the outcome and constraint;
• your design decision;
• the alternative you rejected;
• how you evaluated the system;
• one failure and what changed afterward.

Practice visible debugging

Interviewers cannot evaluate reasoning they cannot see. State assumptions, run tests, inspect failures, and explain tradeoffs without narrating every keystroke.

Protect your rights and boundaries

If a recorded or automated assessment creates an accessibility barrier, request an accommodation. If the employer cannot explain what is being evaluated or how humans remain involved, that is information about the employer too.

What does the likely 2026 pipeline look like?

There is no single pipeline, but a candidate may encounter:

1. application and automated screening;
2. recruiter or hiring-manager conversation;
3. role-specific coding, take-home, or skills assessment;
4. system design and project deep dive;
5. collaboration, values, or stakeholder interviews;
6. an AI-assisted exercise for roles where tool judgment matters.

Any step may be absent, combined, or reordered. Use the employer's written process as the source of truth.

Sources and scope

• Greenhouse 2026 Candidate AI Interview Report. Survey of 2,950 active job seekers in five countries, produced by a hiring-platform vendor.
• Anthropic: Designing AI-resistant technical evaluations. First-person account from one performance-engineering team and assessment.
• CoderPad State of Tech Hiring 2026. More than 650 participants from a technical-hiring community; directional.
• EEOC and DOJ technical-assistance announcement. US disability-discrimination scope.
• NYC Department of Consumer and Worker Protection: Automated Employment Decision Tools. New York City-specific legal guidance.