Top 6 decentralized communication tools in 2026, actual test comparison between Briar, SimpleX and Session

The 2026 programmer hiring market is no longer the same beast it was in 2023. After AI's code-writing ability improved dramatically, the value structure of engineers has clearly polarized: the entry window for junior roles is narrowing, while senior engineers with judgment are scarcer than ever. Interview questions, the weighting of skills, and onboarding processes are all being rebuilt. This article surveys the main trends visible right now, along with how both candidates and employers can adjust. It avoids citing specific numbers from any company's published rankings and sticks to the broadly accepted picture.

The state of things in one sentence

Hiring across the entire industry is polarizing.

Junior roles are clearly shrinking. The zero-experience trainee pipeline has effectively closed or sharply tightened at many companies, and the industry widely feels that demand for the junior software engineer tier is far below what it was a few years ago.

Senior roles are in greater demand. Hiring counts and starting salaries for senior, staff, and principal engineers are trending up, and the talent war among top companies shows no sign of slowing.

The bar for the middle tier has been raised. Even when a role is still called mid-level, the 2026 requirements are close to what used to be the senior starting line: defaulting to working independently with one or two juniors and skillfully wielding AI tools to deliver.

Hiring processes have sped up overall. AI tools have compressed the time spent on screening, interviewing, and onboarding, so the cycle from receiving a resume to extending an offer is noticeably shorter than a few years ago.

The headcount budget structure is changing. For the same engineering team, the old model was a few seniors plus many junior and mid-level engineers; now it's mostly seniors plus a few mid-levels and almost no juniors. The total budget hasn't changed, but the per-person capability has gone up.

Trend one: interviews shift from pure algorithms to system design

Preparing for interviews by grinding algorithm problems alone is weakening as a strategy.

The reason isn't hard to understand. AI tools write code for standard algorithm problems very fast and easily get them right, and companies have found that candidates who score high on grinding problems don't necessarily write better production code once hired. Algorithms are no longer the best tool for distinguishing candidates.

New interviews emphasize system design, requirements decomposition, handling production incidents, technology-selection reasoning, and cross-team communication—abilities harder to simulate with automation. Algorithms are still tested, but the difficulty is often dialed down to medium-low, mainly to see whether a candidate can analyze clearly rather than apply a template.

Some AI companies' interviews are more radical, dropping traditional algorithm questions entirely in favor of a take-home mini-project plus system design plus a product discussion plus an assessment of AI collaboration skills, stacking several rounds to evaluate well-rounded ability.

The corresponding adjustment for candidates is to cut down pure problem-grinding time and instead read system-design classics like Designing Data-Intensive Applications, study the architecture of large open-source projects on GitHub, and prepare several retrospectives of your own projects—explaining clearly why you chose a given technology, what pitfalls you hit, and how you optimized in the end.

Trend two: AI collaboration ability starts entering interviews

A new round gaining popularity in 2026 is allowing AI tools directly in the interview.

In practice, the interviewer gives a task of moderate complexity and lets the candidate use tools like Cursor, Copilot, and Claude Code, but with screen sharing throughout. The focus is not whether you use AI, but how you use it: how you write effective prompts, how you review AI output, and how you decisively roll back where the AI went wrong.

This evaluation method is closer to real work than a traditional coding interview that bans AI. A candidate who can describe requirements precisely, spot logical flaws in AI output, and reasonably break tasks down so the AI iterates multiple times—while still writing code on the critical path themselves—will also perform better on real projects.

The corresponding preparation for candidates is to deliberately practice collaborating with AI in daily work: learn to write good prompts, learn to reject unreliable AI suggestions, and prepare a few projects you completed with AI where you can clearly explain what the AI helped with and what you did yourself.

Employers also need to adjust: standardize hiring criteria and train interviewers on AI-collaboration interviews, otherwise different interviewers will give wildly varying scores.

Trend three: portfolio and real contributions matter more than the resume

The traditional resume format is depreciating. Everyone is using AI to polish resumes, and the resulting uniformity makes it hard for recruiters to differentiate.

There are several replacements.

First is open-source contributions. A GitHub profile has become the new resume in many hiring processes. Which projects you actively maintain, whether you've had PRs merged upstream, whether you've maintained an npm or PyPI package—these are hard evidence of ability.

Second is technical writing. One in-depth article that clearly explains a complex problem you solved usually beats a ten-page resume. Medium, Dev.to, a personal blog—any of them works; what matters is content quality, not word count.

Third is a project demo. A product you can demonstrate live is more intuitive than a list of "things I've done." Ideally attach both a GitHub link and a live URL, so recruiters can click in and see both code organization and the finished experience.

Fourth is community influence. Conference talks, meetup presentations, and high-quality answers on Stack Overflow all prove professional ability.

The corresponding strategy for candidates is to continuously maintain at least one personal open-source project while employed, write some technical articles at a steady pace, and pick an open-source project to contribute to, getting a few PRs actually merged. None of these is big on its own, but after a year or two of accumulation, they become an asset no resume can replace.

On the employer side, state clearly on the JD that portfolio links are welcome, and train HR to judge code quality from GitHub rather than just looking at star counts.

Trend four: take-home tasks and trial work samples become more common

Take-home tasks and short-term trial employment are increasing.

The first model is a paid trial task. The company gives the candidate a specific two-to-five-day task, paid formally, as part of the interview process. This is more common at remote-first companies.

The second model is a shorter full-time trial. Sign a short-term contract with the candidate to actually work for a period—continue if it fits, end if it doesn't. This is common in remote teams, since the chance for face-to-face observation is inherently scarce.

The third model is the reverse interview. In the final round, the candidate proactively asks about the company's technical decisions, team cadence, and culture, while the company also uses this to gauge the candidate's judgment.

Why is this kind of process increasing? AI makes it hard for resumes and short interviews to distinguish real ability, while a slightly longer collaboration reveals what someone is like on a real project.

The corresponding strategy for candidates is to take even unpaid take-home tasks seriously; a project that shows complete engineering consideration can make you stand out. Performance during a trial period often directly determines whether the offer stands.

On the employer side, note that the task must have a time limit and clear evaluation criteria, and work beyond a certain duration must be paid—otherwise you may cross labor-compliance red lines in some countries.

Trend five: remote-first, but video interviews demand more

Remote work remains mainstream in 2026; most software engineers work remotely at least a few days a week.

But remote hiring has clearly raised the bar for video interviews.

The camera is usually required to stay on throughout, as companies want to directly observe a candidate's state and reactions.

More and more companies have added anti-cheating measures to the algorithm round: screen sharing or dedicated proctoring tools to ensure the candidate isn't secretly calling on ChatGPT to do work that should be their own.

Communication ability matters more than spoken fluency itself. In a remote environment, written communication outweighs verbal communication, and recruiters make holistic judgments from the clarity of a candidate's Slack messages, the quality of PR comments, and the logical structure of their documentation.

Time-zone collaboration ability is a new implicit metric. If the team spans time zones, the interview will assess the candidate's expression and response speed in asynchronous communication.

Proficiency with online collaboration tools has become table stakes. If you're not fluent with Notion, Linear, Slack, Figma, and GitHub, daily collaboration will slow your colleagues down by half, and some companies will have candidates demonstrate tool use under screen sharing.

The corresponding strategy for candidates is to set up a good home work environment—lighting, microphone, and network all need to be stable—and to showcase your communication style on public channels like LinkedIn, so recruiters can sense your level of expression before the interview.

How these trends affect salary

Specific numbers from various rankings shift frequently, so it's safer not to cite precise ranges and to speak only of direction.

Starting salaries for entry-level roles are loosening overall, even slightly declining, because demand at this tier is itself shrinking while employer expectations are rising.

Mid-level salaries are roughly flat or slightly up, but the bar has approached the old senior level.

The salary curve for senior and above is clearly trending up; at top companies, total compensation for senior engineers keeps setting new highs, with the increases at the staff and principal tiers especially pronounced.

The trend in China points the same direction, with a different specific structure. At first-tier giants, the total-comp jump from leveling up at mid-level and above still meaningfully exceeds linear gains from experience, and the real increases for senior engineers are concentrated at top companies and strong AI business lines.

The takeaway for individuals is direct: bet your career planning on the senior direction, rather than moving laterally to the next junior role. After senior, each level up usually offers a salary jump far exceeding ordinary linear experience-based growth.

The state of several job categories

By hiring heat, AI and machine-learning engineers have seen the largest increase, with LLM fine-tuning, RAG systems, and agent engineering as the main demand directions.

Platform and infrastructure engineers are equally in demand; Kubernetes, observability, and SRE have surged because infrastructure complexity has risen in the AI era.

Security engineers are badly needed in supply-chain security, AI model security, and enterprise compliance.

Full-stack engineers: as a single person's output rises in the AI era, small companies increasingly prefer one full-stack engineer over three specialists.

Front-end engineers: demand is stable but requirements have deepened. Knowing only React is no longer enough; accessibility, performance optimization, and design tokens are all becoming default requirements.

Mobile development: demand is stable but growing slowly, with React Native and Flutter continuing to gain share.

Data scientists: this role itself keeps shrinking. Many traditional data-analysis tasks have been automated by AI tools, and the hybrid Analytics Engineer role—DBT plus dashboards plus business judgment—has gradually replaced part of the traditional positions.

A six-month preparation rhythm for candidates

If you are or will soon be looking for a new job, you can use six months for a paced preparation.

Month one: clean up your GitHub. Write good READMEs for public repos, polish two or three core projects to presentation quality, and archive repos you forked but don't maintain.

Month two: pick the technical area you know best and write one in-depth article, posting it on Medium or your personal blog—quality over quantity.

Month three: focus on system design. Read Designing Data-Intensive Applications cover to cover, watch series content like ByteByteGo, and prepare answer templates for five common system-design questions.

Month four: start heavy practice in AI collaboration. Use AI 100% in daily work until you're effortlessly fluent.

Month five: do several mock interviews on platforms like Pramp and interviewing.io, covering system design, AI collaboration, and behavioral interviews.

Month six: only now start formally applying, prioritizing referrals through your network—the response and conversion rates from referrals are an order of magnitude higher than mass applications.

A few things the employer side can do

If you are an employer hoping to hire the right people in 2026, several things are worth doing.

Write honest JDs. State the tech stack, team size, remote percentage, and salary range clearly; vague JDs usually have very low application conversion.

Shorten the interview process. Compress five rounds to three or four; candidate experience directly affects whether they accept the offer.

Pay for take-home tasks. Any task over a few hours should carry reasonable compensation—both a sign of respect and a way to lower labor-compliance risk.

Train interviewers at least once a year. Standardize evaluation criteria to avoid severe imbalances among interviewers.

Tech branding isn't just marketing—it's key to attracting candidates. Technical blogs, open-source projects, and conference talks all fall into this long-term investment.

Frequently Asked Questions

I'm a junior engineer—can I still break into this industry?

Yes, but it's harder than a few years ago, and three paths still work. First is an internship; top companies still have slots but competition is fierce. Second is joining an early-stage startup willing to take a junior and let you own a slice independently. Third is bootcamp plus freelance to start, accumulating experience through projects before applying for full-time roles. The most critical thing is to have three to five decent projects on GitHub first, so recruiters can see your ability at a glance rather than getting a resume with no code to back it up.

Are senior engineer raises really that dramatic?

The raises are concentrated at top companies and strong AI business lines. At companies like Meta, Google, Anthropic, and OpenAI, the increases for senior engineers are clear, while raises in traditional industries or non-core roles are limited. If you happen to be in a low-raise position, you can consider jumping to the core of an AI business—but assess stability before jumping, because some AI companies also started tightening budgets in 2026.

Is AI really replacing programmers that fast?

Media claims are generally exaggerated. AI tools do greatly boost a single person's output, but fully replacing complex engineers is still far off. The actual situation right now is that senior engineers' output has doubled while juniors' output gains are limited, so companies no longer need as many juniors. This is structural replacement, not wholesale replacement. Within the foreseeable horizon, senior engineers won't disappear en masse, but junior entry roles will be significantly fewer.

I don't have a formal CS education—can I still get a software engineering job?

Yes. In 2026, employers weigh credentials far less than in 2018; what matters is proof of ability. Open-source projects, technical blogs, and bootcamp-plus-project experience are all viable paths. That said, top companies still prefer a CS degree at the entry level, while at the senior level credentials barely matter—your work and experience speak directly.

Is it still worth getting into programming now?

Still worth it, just with a changed path. Absolute pay levels remain at the front of the tech industry, and total comp for senior engineers at top companies is a very competitive tier. But you should aim straight for mid-level and above rather than junior. The best path for young people is a CS or related degree plus an internship plus a project portfolio, graduating at near-mid level. People switching careers before thirty need two to three years of self-study plus project accumulation, then start at a mid-level role. Going from a complete blank slate to senior at a big company usually takes five-plus years, and AI won't meaningfully shorten that time.

Inspired by Ruan Yifeng's Science and Technology Enthusiast Weekly, Issue 389 https://www.ruanyifeng.com/blog/2025/08/weekly-issue-389.html