Technical interview preparation used to be a stable category: a problem bank, a book, a few mock interviews with friends. In the last two years almost every part of it has moved at once. Candidates compete for fewer entry-level roles, AI tools sit in every developer’s workflow, cheating on remote assessments has become a measurable problem, and employers are responding in two opposite directions.

This article pulls those threads together from public sources. Where a number comes from a company that sells a product in this space, we say so – including when that company is us.

How big is the market? Nobody agrees

Market-research firms put a number on “mock interview platforms”, but the numbers do not line up:

One estimate values the mock interview service market at about USD 1.02 billion in 2026.
Another puts mock interview platforms at USD 1.36 billion in 2025, growing at about 12.6% a year.
A third values the same category at USD 1.8 billion in 2026, growing at about 14.6% a year.

These differ by nearly 2x for roughly the same year, which mostly reflects different definitions – whether human coaching, job-board add-ons or general interview software are counted. The useful takeaway is the direction, which every report agrees on: double-digit growth, driven by remote hiring and AI-based practice tools. Treat any single headline figure, including these, with caution.

Force 1: fewer entry-level seats, more competition for each

The sharpest change is at the start of the career ladder. SignalFire’s State of Tech Talent report (May 2025) found that new graduates made up just 7% of hires at big tech companies, with new-grad hires down 25% from 2023 and more than 50% from 2019. At startups, new grads were under 6% of hires.

For candidates this means each interview matters more, and the bar for a junior role looks more like the old bar for a mid-level one. For preparation, it means “I have seen this question before” is no longer a competitive advantage – everyone has.

Force 2: developers use AI constantly, and do not fully trust it

The 2025 Stack Overflow Developer Survey found that 84% of developers use or plan to use AI tools, up from 76% a year earlier. Trust moved the other way: more respondents distrust the accuracy of AI output (46%) than trust it (33%), according to Stack Overflow’s summary of the results.

That combination – heavy use, low trust – is exactly what interviewers are now trying to measure. Producing code is cheap. Knowing whether the code is right, explaining why, and catching the case the tool missed is not.

Force 3: the integrity problem in remote assessments

Two vendors that sell assessment and detection products have published data on cheating. Both have a commercial interest in the problem being large, so read them as signals rather than census figures – but they point the same way.

CodeSignal reported in February 2026 that cheating and fraud attempt rates on its proctored assessments rose from 16% in 2024 to 35% in 2025, and for entry-level assessments from 15% to 40%. It also found that unproctored assessments showed score increases more than four times larger than proctored ones.
Fabric, an AI interview platform, analysed 19,368 interviews from July 2025 to January 2026 and flagged 38.5% of candidates for cheating behaviour, with technical roles higher than sales roles. Its definition is probabilistic (a model’s likelihood above 40%), which is worth knowing when comparing it with other figures.

Whatever the exact rate, the effect on hiring is real: a remote coding score on its own now carries less information than it did three years ago.

Force 4: employers are splitting in two directions

Companies are answering that loss of signal in two ways that look contradictory but share a goal – seeing how the candidate actually thinks.

Back to the room. Computerworld reported in August 2025 that Google, Cisco and McKinsey & Co. had re-instituted in-person interviews for some candidates, with AI-assisted cheating as the driver.

Let the AI in, and grade the human. In June 2025 Canva announced that it expects candidates to use AI tools in its engineering interviews, with harder, more ambiguous problems to match. Meta has also introduced an AI-enabled coding round alongside a traditional one; preparation guides such as Hello Interview’s describe it as assessing problem solving, code quality, verification and communication.

Both responses move weight away from “can you produce a correct answer in isolation” and toward explaining, defending and verifying your work while someone asks follow-up questions.

What the tools on the market actually train

Prep products fall into a handful of categories. None is wrong; each trains a different part of the interview.

Category	What it trains well	What it leaves out
Problem banks and courses	Pattern recognition, algorithm fluency	Explaining out loud, follow-ups, time pressure
Peer and paid human mock interviews	Realism, human judgment, behavioural nuance	Availability, repetition, consistent difficulty
General-purpose AI chat	Explanations on demand, quick drills	Structure, a clock, follow-ups on your answer
AI mock interview tools	Repetition, structure, feedback per session	Quality varies: check how feedback is produced
Real-time “interview assistants”	Nothing about your own skill	They feed answers during a live interview – see below

The last category deserves a plain statement. Tools that listen to a live interview and supply answers are marketed openly, and the data above suggests they are used. They are also what in-person rounds and detection systems are being built to catch, and they train nothing that survives the moment the assistance is gone. We covered the difference between practice formats in more detail in AI mock interview vs. LeetCode and peer mock interviews vs. AI.

What this means if you are preparing

Practice explaining, not just solving. The skill with the most scarcity value is a clear account of your reasoning, including trade-offs – see thinking out loud in coding interviews.
Practice being questioned. Follow-ups are where preparation that relied on recognition falls apart. Practice needs someone – or something – that asks “why?” about your answer.
Verify your own code. Whether the interview bans AI or requires it, the candidate who tests, spots edge cases and says what they checked stands out.
Practice honestly. A practice score earned with outside help measures the help. If you use a tool, use it to find where you get stuck, not to hide it.

What this means if you hire or grow engineers

Consistency is now the scarce resource. When scores are noisier, a structured first round – same questions, same rubric, evidence behind each score – is worth more than a harder puzzle. Our article for engineering teams covers how to set that up.
Separate conversation from code. A discussion of design and a graded coding exercise measure different things, and mixing them hides which one is weak.
Decide what you are measuring. Some teams now test AI-assisted work on purpose. Others want unassisted fundamentals. Both are defensible; being unclear about it is not.

Where Mentara fits

Mentara is built around the parts of the interview that this market now rewards: a structured technical interview for your role and stack with follow-ups on your own answers, coding assessments graded by hidden tests and kept separate from the conversation, and a report whose scores are tied to evidence from what you said. Its optional Realistic mode is designed as coaching rather than policing: it marks where you got stuck and shows what a session would have been worth without outside help.

For teams, Mentara for teams adds company interviews, candidate assignments with consent, and a team report built from aggregates rather than transcripts. Mentara launches in 2026 – the waitlist is open.

Sources are linked inline. Market-size figures come from commercial research publishers and disagree with each other; cheating figures come from vendors of assessment and detection products. Mentara sells interview practice, so read this article with that in mind too.

The technical interview prep market in 2026: what changed, and what it means for how you prepare