There is a widely-held belief in the technology industry right now that AI is coming for software engineering jobs. The narrative has become familiar: AI writes code, therefore engineers are redundant, therefore startups will hire fewer engineers, therefore the hiring market will quiet down and get easier.
This is almost entirely wrong. And the consequences of that misdiagnosis are going to catch a lot of founders and engineering leaders off guard.
The reality is almost the inverse. We are entering a period that will produce more startups, more engineering demand, more candidates in the market, and more complexity in the hiring process — simultaneously. For the first time in years, every one of these forces is moving in the same direction at the same time.
That is the problem Samani AI was built to solve. But to understand why our approach is necessary, you first have to understand why the storm is forming.
1. The Jobs Are Not Going Away — They Are Shifting
The concern that AI will eliminate software engineering roles is understandable. AI coding tools are genuinely powerful. They write boilerplate. They autocomplete functions. They dramatically increase developer productivity. For a time, the data seemed to support the anxiety: entry-level tech hiring dropped 25% year-over-year in 2024, and employment for software developers aged 22–25 fell nearly 20% from its 2022 peak.
But zoom out, and a different picture emerges.
67,000+ engineering openings at tech companies globally as of early 2026 — and accelerating.
As Robert Half’s 2026 technology hiring report notes, AI coding tools are handling routine work — which is freeing engineers to focus on system design, architecture, security, and shipping AI-native features. The demand for engineers who can shape products is not declining. It is expanding, and the expectations attached to those roles are rising.
“Software engineering jobs in 2026 will grow in volume and shift in shape.” — Final Round AI, 2026 Job Market Outlook
There is a meaningful difference between AI reducing the number of engineers a large established company needs for maintenance work, and AI reducing the number of engineers that a growing startup needs to build something from scratch. The former is happening. The latter is not. If anything, higher developer productivity per engineer raises the expected output of a small engineering team — which increases the stakes of every hire.
The share of AI and ML roles in the broader tech job market grew from 10% to 50% between 2023 and 2025. These are not positions being eliminated. They are new categories of demand being created. The engineering market is not shrinking. It is undergoing a structural shift that makes the quality of each hire more consequential, not less.
2. AI Is Triggering an Explosion of New Startups
Here is what the decline-of-engineering narrative misses entirely: AI is not just changing what engineers do inside existing companies. It is enabling an unprecedented wave of new companies to form.
The barrier to starting a software company has been falling for years — cloud infrastructure, open source frameworks, and no-code tools all played their part. But AI represents a qualitative step change. A solo founder with deep domain expertise can now build, test, and ship software at a pace that would have required a team of five or ten just three years ago. For the first time, technical ability is no longer the gating factor to starting a technology company.
$425B invested in 24,000+ private companies globally in 2025 — a 30% increase year-over-year.
$211B poured into AI-related startups in 2025, up 85% from $114B the year before.
North American startup funding alone surged 46% in 2025, reaching the highest annual total in four years. The seed stage remains active. And critically, the companies being funded are leaner — which means they need to hire precisely, not broadly. A startup that raised $3M on a team of three cannot afford to spend six months finding its first senior engineer.
The World Economic Forum noted in 2025 that AI is enabling small businesses to do what was previously only possible for large corporations, “fostering a new generation of highly capable and agile startups.” Y Combinator’s recent batches have reflected this clearly: a growing share of companies are AI-native from day one, built by domain experts rather than traditional software engineers, and scaling their engineering teams rapidly after initial traction.
The implication for hiring is direct: thousands of new startups are forming, many led by first-time people managers who have never run an engineering interview process. They are being asked to hire fast, hire well, and hire without the institutional knowledge or HR infrastructure of an established company. They are, in short, precisely the people most likely to get this wrong.
3. Big Tech’s Hangover Is Flooding the Candidate Market
At the same time that new startups are scrambling to hire, the large technology companies are still working through the consequences of one of the most significant over-hiring periods in the industry’s history.
During the 2020–2022 COVID boom, major tech companies hired at a pace that proved entirely unsustainable. The correction that followed has been sweeping and prolonged.
900,000+ tech jobs eliminated across U.S. companies in the three years from 2023 to 2025.
113,863 additional tech workers laid off in 2026 alone, as of May — an average of 904 per day.
The layoffs have not been evenly distributed. Software engineers and developers, quality assurance engineers, product managers, and project managers have been the most affected categories. Challenger, Gray & Christmas tracked approximately 55,000 layoffs in 2025 where companies explicitly cited AI-driven efficiency gains as the justification.
The result is a candidate market of remarkable depth. Experienced, well-credentialed software engineers — many from Meta, Google, Amazon, Microsoft, and other major employers — are actively looking for roles. For a startup, this sounds like good news. More candidates means more options.
More candidates does not mean better signal. It means more noise — at precisely the moment when hiring teams have the least bandwidth to process it.
The average time for a software engineer to find a new position went from 6–8 weeks in 2021 to 5–7 months by 2025. Software engineers are now the hardest category to place — not because they are unqualified, but because the sheer volume of candidates, combined with the difficulty of technically evaluating them, has created a paralysis in many hiring processes.
4. The Collision: More Startups, More Candidates, Worse Processes
Place these three forces alongside each other — growing engineering demand, an explosion of new startups, and a flooded candidate market — and a predictable outcome emerges: hiring processes are getting significantly worse, for everyone involved.
The numbers are stark.
68.5 days The average hiring journey from first contact to offer in 2026. Candidates expect an offer in 1–2 weeks.
71 days The average interview loop for senior tech candidates in 2025. One candidate, nearly two and a half months.
53% of job seekers experienced employer ghosting in 2025 — a three-year high.
Recruiters are overwhelmed. Application volumes are surging, with job seekers submitting 32 to 200+ applications on average before receiving an offer. Most online applications result in a 0.1–2% success rate. Hiring managers at startups — many of whom are first-time people managers with no HR support — are attempting to evaluate candidates against technical standards they may not have the bandwidth to assess themselves.
The informal evidence is even more vivid. Reddit threads in r/cscareerquestions and r/ExperiencedDevs have become repositories of hiring horror stories: five-stage interview loops followed by silence, six-week processes abandoned after final rounds, candidates ghosted after in-person panels. One recruiter’s post describing the “new normal” of extended, chaotic hiring processes amassed over 7,000 upvotes and nearly 300 comments of agreement. Fortune reported in March 2026 that candidates ghosted by employers hit a three-year high — not because companies are indifferent, but because teams are genuinely unable to keep up.
It’s become so prevalent that entire Reddit threads and LinkedIn posts are devoted to ‘ghost stories’ from job seekers — some even after final interviews. — Fortune, March 2026
The AI resume problem compounds everything. Candidates now use AI tools to tailor every application to every job description with precision that was previously impossible. The polished, keyword-optimized resume that emerges tells a recruiter almost nothing about what a candidate can actually do. Traditional resume screening has become close to meaningless as a signal. The volume of credible-looking applications is higher than it has ever been, and the signal-to-noise ratio has never been lower.
5. The Existing Solutions Are Not Working
The responses most startups reach for — recruiting agencies and their own networks — were inadequate before these structural shifts. In the current environment, they are failing outright.
The agency model
Traditional recruiting agencies typically charge 15–30% of a candidate’s first-year base salary. For a software engineer earning $200,000 — increasingly common in major US markets — that is $30,000 to $60,000 per hire. What this fee buys, in practice, is LinkedIn sourcing, a 30-minute screening call, and a resume that, in 2026, may well have been partially written by a language model. There is no deep technical evaluation. There is no code review. There is no structured assessment of whether the candidate can actually perform the role. The technical vetting is left entirely to the startup — the team already stretched thin — with agency fees collected regardless of whether the hire works out.
Industry data confirms what most startup founders already know from experience: 20–30% of new hires facilitated through traditional agencies do not work out within the first year. Agencies themselves acknowledge that only 25% of talent acquisition professionals feel highly confident in their organization’s ability to measure quality of hire. The fee is not a quality guarantee. It is a sourcing fee.
The network lottery
The alternative that many early-stage startups default to is hiring through personal networks: referrals from investors, alumni Slack communities, former colleagues. When this works, it works well. But the hit rate is low, and there is no systematic fallback when the network runs dry — which it typically does after the first two or three engineering hires. Network hiring is a lottery with good odds for the first round and declining returns thereafter.
Neither of these approaches was designed for the market that exists in 2026: thousands of new startups in need of fast, precise, technically-validated hiring, in a candidate pool of unprecedented volume and opaqueness.
6. Why Samani AI Exists
Samani AI was built from the observation that technical vetting is placed in the wrong position in almost every startup hiring process. It happens at the end — after the recruiter call, after the hiring manager screen, after multiple internal discussions — at the point where the most time and cost have already been invested. When the technical interview reveals that a candidate isn’t the right fit, weeks of process have been wasted.
The fix is structural, not incremental. Move the technical interview to the beginning of the process. Do it before your team meets the candidate. Do it rigorously — not a brief screen, not a resume review, not a behavioral conversation dressed up as a technical evaluation. A real, deep technical interview, conducted by people who can accurately assess the skill required for the role.
This is what Samani AI does.
By the time a candidate reaches a startup’s team through Samani AI, they have already been technically validated for that specific role. The hiring team’s first meeting with a candidate is a final interview — a conversation about ambition, culture, and long-term fit. Not a test. The time between initiating a search and extending an offer compresses from months to days.
This matters to founders and CEOs because it is a cost and speed problem: every week a critical engineering role goes unfilled is a week of delayed product development. It matters to CTOs and engineering leads because it is a quality problem: a wrong hire is not just expensive to unwind, it is disruptive to the team around that hire. And it matters to candidates because a process that respects their time — that tells them clearly and quickly whether they have cleared a technical bar — is fundamentally more humane than the status quo.
The best technical hiring processes front-load the signal. By the time your team meets a candidate, everyone in the room already knows they can do the job.
We are building this at exactly the right moment. The structural forces described in this post are not temporary. The wave of new startups is not slowing. The pool of available engineering talent is not shrinking. The inadequacy of traditional recruiting is not improving. These forces are converging and compounding, and they are doing so at a pace that the industry’s existing hiring infrastructure was not designed to handle.
Technical hiring is about to become the hardest operational problem facing startups. Samani AI exists to make it the easiest decision you make.
If this is a problem your team is navigating right now, we’d like to talk.
Visit samani.ai to learn more or to get in touch.
Sources & Research
Software engineering job market 2026 — Robert Half Technology Hiring Report 2026; Final Round AI Job Market Outlook 2026; CNN Business, April 2026 (“The demise of software engineering jobs has been greatly exaggerated”)
Entry-level tech hiring decline — Stack Overflow Blog (“AI vs Gen Z”); IEEE Spectrum (“How AI is Reshaping Entry-Level Tech Jobs”)
Global venture funding 2025 — Crunchbase: “Global Venture Funding in 2025 Surged As Startup Deals and Valuations Set All-Time Records”; North American funding up 46%, Crunchbase 2025 Annual Report
AI startup investment — Bessemer Venture Partners, State of AI 2025; TechCrunch, January 2026
Tech layoffs — Crunchbase Tech Layoffs Tracker; TechCrunch Layoffs List 2025; InformationWeek 2026 Layoffs; Challenger, Gray & Christmas (AI-cited layoffs); LongYield Substack “The Great Tech Reckoning”
Time-to-hire and candidate experience — SHRM (45-day average time-to-fill); MyTSP Blog (“Average Time to Hire & 2026 Recruitment Statistics”); InterviewPal (“How Long It Really Takes to Get Hired in 2025”)
Software engineer placement time — WhiteTruffle (“Tech Layoffs 2025–2026: What Engineers Need to Know”); Codesmith.io; AEI (“Are Software Jobs Collapsing?”)
Ghosting statistics — Fortune (“Job seekers aren’t imagining things”, March 2026); The Interview Guys “2025 Ghosting Index”; Recruiterflow Blog
Application volume statistics — HiringThing 2025 Job Application Statistics; SHRM data
Recruiting agency fees and quality — Dover.com (“Tech Recruiter Fees in 2025”); PredictiveHR (“The Recruiting Process is Broken”); SignalFire State of Tech Talent Report 2025
AI share of tech job market — Sundeep Teki (“Impact of AI on the 2025 Software Engineering Job Market”); Yahoo Finance / Aptitude Research 2026
World Economic Forum on AI and entrepreneurship — WEF, April 2025 (“How founders are shaping the future of startups with AI”)
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