3 ways AI has changed FAANG interviews in 2026

Experienced software engineers walking into FAANG interview loops this year are encountering a format that has shifted meaningfully over the past 18 months. New rounds have appeared. Existing rounds have grown or shrunk. Evaluation criteria have changed at every layer. 

The bar for what counts as senior-level performance has risen quickly enough that many strong engineers find their 2024 preparation no longer cuts it.

The shift is structural, driven largely by how AI has reshaped the practice of software engineering itself. The 2025 Stack Overflow Developer Survey found that 84 percent of developers now use or plan to use AI tools at work. At Google, CEO Sundar Pichai confirmed that more than 30 percent of all new code is AI-generated, reviewed, and accepted by engineers. Anthropic's internal study of its own engineering team found that Claude is now used in roughly 60 percent of daily tasks.

Interview panels at top technology companies are catching up to that reality in specific, structural ways, and the preparation experienced engineers used 18 months ago no longer maps cleanly to the loop they will walk into today.

Three specific changes within the FAANG interview loop account for most of what's different.

A note on the data: The patterns in this post come from two sources. The first is Formation's own interview reflection data. Between September 2025 and March 2026, Formation Fellows submitted 89 interview reflections across 69 companies after their onsites and phone screens, covering Big Tech, AI-native companies like OpenAI and Anthropic, enterprise software, consumer platforms, and a long tail of startups. Fellows log these reflections, whether they pass or not, giving us a running view of how specific company loops are evolving. 

The second is public research from outside the interview prep industry, including the Stack Overflow Developer Survey, the JetBrains Developer Ecosystem Survey, Thoughtworks Technology Radar, PwC's Global AI Jobs Barometer, and independent analyses from METR and CodeRabbit.

Shift 1: System design interviews have split into three categories

Experienced candidates at top technology companies now face three distinct kinds of system design prompts:

• Traditional system design (the classic loop prompt)
• ML system design (recommender systems, ranking, model training, and serving pipelines)
• Generative AI system design (LLM-backed applications, RAG pipelines, agentic workflows)

Generative AI system design is new and has moved from niche to standard in under 18 months. 

Fellows have reported prompts asking them to design ChatGPT-style services, retrieval-augmented question answering systems for enterprise knowledge bases, LLM-backed customer support workflows, and multi-agent travel planners that chain tool calls together.

OpenAI has been running extended system design rounds built around prompts like "Design the OpenAI Playground" and "Design a high-scale chat application." Meta now requires expert-level competency in both software engineering and machine learning for its ML engineer roles.

The skill being tested is real architectural judgment with AI in the request path. Strong answers treat model serving latency as a first-class design constraint, discuss inference cost alongside database sharding, handle probabilistic output the way they would handle any other failure mode, and build evaluation and guardrails into the design from the start.

What this means for your prep

If your system design practice only covers the canonical prompts, you have a gap. 

Some ways to prepare:

• Practice three to five generative AI system design prompts before your loop. RAG, agentic workflows, and LLM-backed chat services are the most common shapes.
• Learn to reason about token costs, context windows, caching strategies, and model routing as core capacity concerns.
• Build a mental model for evaluating probabilistic systems, including prompt regression tests, guardrails, and quality scoring.
• Keep traditional system design sharp. It still opens most loops.

Shift 2: Behavioral rounds now center on AI

Behavioral rounds used to occupy 10 to 15 percent of a typical interview loop. In Formation's recent reflection data, they now occupy 30 to 40 percent. The growth came from adding new question types.

The largest share of that new content is some version of the same underlying question: how do you actually use AI in your work?

In the Fellow data, 58 percent of reports from 45 of the 69 companies include a question of this shape. It appears across every segment tracked, from Big Tech to mid-stage AI-native startups to enterprise software companies still shaping their AI strategy. 

The phrasing varies. Fellows have recorded questions like:

• "How do you think about AI, and how have you started to integrate it into your work, technical and non-technical?" 
• "How would you approach debugging and refining AI-generated code that fails on edge cases?" 

At many other companies, the question comes up in a standard "tell me about a recent project" prompt.

Interviewers here are surfacing signal about judgment:

• Which tasks does the candidate delegate to AI?
• Where do they override the model?
• How do they verify output?

Strong responses share a specific moment where the candidate caught a hallucinated API, refused an AI suggestion, or decided to solve a problem themselves. Tool lists and unverified productivity claims tend to fall flat. A candidate who claims AI makes them dramatically faster without supporting evidence triggers the same skepticism hiring panels bring to any unverified claim.

What this means for your prep

Build a specific story about your AI usage before you walk in. It should include a concrete example where you caught an error, made a deliberate judgment call, or refused a suggestion. General claims about productivity without supporting detail will not land at this level.

Shift 3: AI-assisted coding rounds are inside the loop

The most format-specific change in the 2026 FAANG interview landscape is the addition of a dedicated AI-assisted coding round. At Meta, Fellows consistently report an "AI-Enabled" round as a discrete slot in the loop, separate from the traditional DSA round. At Netflix, the standard CodeSignal General Coding Assessment is paired with an "AI-Assisted GCA" follow-on round. Shopify, Scale AI, and a handful of other AI-native companies run similar formats.

How the format works

In the Fellow dataset, 68 percent of AI-related interviews at these companies use this format. Eleven of the 69 companies run it. The round typically happens in CoderPad or CodeSignal with model access built in, and candidates work alongside models like GPT-4o mini, Claude Sonnet, or Gemini 2.5 Pro during the round.

The problems themselves are not dramatically harder than traditional DSA problems. What changes is the evaluation criteria.

Interviewers are evaluating:

• Prompt quality. Are you asking the model the right questions?
• Task decomposition. Can you break a problem into AI-delegable pieces?
• Error detection. Do you catch wrong or fabricated output?
• Verification discipline. How do you confirm the model's answer is correct?

The premium skill here is knowing when AI output is wrong and why, which requires the kind of engineering fundamentals many candidates stop practicing once AI tools are available.

Common failure modes:

• Treating the round as a productivity shortcut. Candidates who accept every suggestion tend to underperform.
• Holding back out of caution. Not using the tools fluently reads as unfamiliarity.
• Using the tools without verifying. This reads as poor engineering judgment.

Candidates who treat the AI as a collaborator whose output requires verification consistently do well.

What this means for your prep

Practice the format, not just the problems. Some way to prepare:

• Practice coding with an AI assistant turned on. Use tools like Claude Code, Codex, Cursor, or GitHub Copilot, and pay attention to the verification habits you build.
• Work through medium-difficulty DSA problems with AI collaboration. Notice where the model helps, where it drifts, and where you need to intervene.
• Practice prompting clearly. The quality of your prompt is a signal interviewers can see.
• Keep your fundamentals sharp. You cannot catch a model's mistakes if you have not internalized the underlying patterns.

Why this matters for SWEs preparing for FAANG in 2026

Three structural shifts at once are a lot to absorb. Read together, they point to a consistent underlying change in how FAANG interview panels evaluate experienced engineers: the signals candidates are expected to demonstrate have become more specific, and the baseline is moving up quickly.

Preparation that worked in 2024 no longer covers the full loop. An engineer who only practices traditional DSA problems and canonical system design prompts will walk into a Meta interview and find an AI-Enabled round they haven't rehearsed, a behavioral question about AI usage they haven't thought through, and a system design prompt that asks them to architect around probabilistic output.

Each of those is learnable. Each takes real preparation time.

The compensation data reflects what companies are willing to pay for this combination of skills. PwC's 2025 Global AI Jobs Barometer found that AI-skilled workers earn a 56 percent wage premium, up from 25 percent one year earlier. At the staff engineer level, the AI premium has widened to 18.7 percent. Levels.fyi data shows the gap between AI and non-AI staff engineers at companies like Intuit now approaches $400K in total compensation.

What to do now

The three shifts above require three distinct preparation tracks:

• Build generative AI system design into your rotation. Pull prompts specifically about LLM-backed systems and practice them the same way you would traditional system design. Work through the tradeoffs: model serving latency, inference cost, probabilistic output handling, evaluation strategy, and guardrails. If you can only articulate a traditional architecture, you are not covering the full loop.
• Develop a specific AI usage story before your next interview. Audit how you actually use AI tools in your current work. Identify at least one moment where you caught an error, overrode a suggestion, or made a deliberate judgment call about what to delegate and what not to. That specific example is what behavioral rounds are probing for. A general answer about productivity will not land.
• Practice the AI-assisted coding format, not just the problems. If you have not run a coding session with AI access and deliberately practiced prompt quality, task decomposition, and output verification, you have not rehearsed the format. The evaluation criteria in an AI-assisted round are different enough from a standard DSA round that they require separate preparation time.

Start with whichever gap is largest. For most engineers who prepared heavily in 2024, that is generative AI system design.

Ready to prepare for the 2026 FAANG loop?

Formation works with experienced engineers preparing for the kinds of loops described in this post. Our mentors include engineers from the companies running these new interview formats, and our curriculum reflects the three shifts covered here.

Learn more about Formation's program or join an upcoming Studio Session to see how structured preparation changes outcomes.