2026 Software Engineer Core Competencies Inventory, 5 Abilities That AI Can’t Replace

The speed of progress in AI's ability to write code in 2026 has indeed made many programmers anxious. What is really worth asking is not "Will AI replace programmers?" but "What can AI do now, what can't it do well, and which engineer abilities have become more scarce because of AI." This article takes stock of the 5 core competencies of software engineers in 2026 that will be difficult to replace in the short term with AI, and how to practice them.

All specific competency lists in this article are based on the general consensus in the industry, and the precise percentages and scores in any specific survey reports are not quoted, because such figures vary greatly from company to company and are easily distorted when quoted.

What exactly can AI do in 2026?

Let’s first look at what AI can do relatively stably, which can be roughly classified into several categories.

For function generation of simple to medium complexity, the probability that AI can provide usable code at once is quite high. Conventional React components, CRUD interfaces, and data processing scripts can basically be left to AI with confidence. In code review, AI can identify quite a few common bugs, such as null pointers, SQL injection risks, type errors, and obvious memory leak patterns. In terms of document generation, the quality of README, API documentation, and changelog generated from code is already usable.

For simple debugging, as long as the AI ​​is given a complete stack trace and context, most syntax and runtime errors can be located. For repetitive refactoring, such as batch renaming, function extraction, and migration API calls, AI is much faster than manual work. For unit testing, it is not difficult to let AI generate several test cases for a function to cover the happy path. LeetCode's algorithm questions below medium difficulty can be solved by AI in seconds.

In the past, these activities often accounted for a considerable part of the workload of junior engineers. Nowadays, AI can do everything. The quality may not be excellent but it is sufficient. This is the source of anxiety and the starting point for changes in job structure.

AI will still do bad things in 2026

There are several scenarios in which AI fails repeatedly in reality.

First, dismantle fuzzy requirements. As for "making a user-friendly reporting function", AI does not know what the real pain points of users are, and it is easy to make products in the wrong direction. Second, large-scale system design. Regarding the microservice architecture, cross-regional deployment, compliance, and capacity planning of more than a dozen services, the solutions given by AI often contradict themselves or ignore key constraints. Third, cross-context judgment. A bug may involve dozens of files, several teams, and design decisions made months ago, and AI cannot see this full context. Fourth, the social dimension of technology selection. When choosing between Rust, Go, Java, and Python, you must consider team skills, ecology, and the recruitment market. AI often prefers popular answers but may not be suitable for you. Fifth, response to production accidents and service failures in the middle of the night require rapid diagnosis, decision-making, and external communication. AI can assist, but only humans can make decisions and take responsibility. Sixth, cross-person collaboration and real dialogue with PM, design, operation and maintenance, and customers cannot be replaced by AI.

These six categories are where the value of senior engineers lies, and are also the direction in which the next five abilities will be developed.

Ability 1, system design and architectural judgment

What is system design judgment. Given a fuzzy business requirement, a feasible technical architecture solution can be provided, and expansion and maintenance issues in a few years can be predicted. For example, when building a real-time chat system that supports large traffic, AI can give a "standard answer", but the specifics must consider the business model, team structure, budget constraints, compliance requirements, and future expansion directions. The solutions given by AI are often one-size-fits-all.

Specific ability points: able to ask key questions (SLA, concurrency, data compliance, team size), able to take a reasonable path between SQL and NoSQL, monolith and microservices, able to predict bottlenecks in half a year, and able to perform evolutionary upgrades on existing systems instead of tearing down and rewriting them at every turn.

How to obtain it: read classics such as "Designing Data-Intensive Applications", look at the architecture of large open source projects, actively participate in the company's architecture review, and write your own design doc even if no one reviews it.

Value changes: This ability used to be a basic requirement in the senior level, but now it is becoming the key to differentiation, because the code itself is getting easier to write, and design judgment is difficult to mass produce.

Ability 2, business understanding and judgment

What is business understanding. Ability to reverse technical decisions from product and market perspectives, and do things that are valuable to both bosses and customers. AI can write code but doesn't know why. The "why" provided by engineers is the core.

Specific ability points: able to work with PMs to break down requirements, able to identify which features really affect key indicators and which are just cosmetic projects, able to hear unspoken pain points in customer interviews, able to push back unreasonable demands and propose better solutions, able to reversely drive business decisions from an engineering perspective, for example, changing the architecture can save a lot of costs.

How to obtain: proactively contact PMs, sales, and customers, read company performance reports, understand the basic economic model of your industry, develop the habit of quarterly review, and reflect on the impact of your engineering decisions on the business.

Value changes: This ability was previously emphasized at higher levels, but is now expected to be possessed by mid-level engineers. After AI improves code efficiency, business judgment becomes the root cause of output differences.

Ability 3: Diagnosis and decomposition of complex problems

What is a complex diagnosis. When faced with a bug, performance problem or user complaint that has no clear steps to reproduce, the root cause of the problem can be identified. AI can assist analysis, but it lacks complete observations and long-term intuition.

Specific ability points: being able to set up an observability system, being able to use trace, log, and metric signals to verify each other, being able to hypothesize-verify-overturn-re-assuming, being able to make reasonable decisions when the data is incomplete, being able to distinguish symptoms from root causes, and being able to sort multiple possible causes by probability.

How to obtain: Actively participate in every production accident and write RCA. Learn distributed tracing tools such as OpenTelemetry, Jaeger, Tempo, etc. Read Site Reliability Engineering. Use low-level tools such as strace, tcpdump, and Wireshark more often every day, and practice them even if you don't need them now.

Changes in value: In the AI ​​​​era, the more complex the code and the larger the system, the more difficult it is to diagnose problems when they occur, and the higher the value of people who can diagnose.

Ability 4: Judgment and use of AI tools

The fourth ability sounds contradictory but it is real - whether AI can be used well or not is itself an ability that requires deliberate training.

Specific ability points: Ability to write clear prompts for AI, clearly explaining the background, constraints, and expected output format. Can identify AI-made APIs, non-existent npm packages, and outdated code patterns. It allows AI to iterate in multiple rounds to approach the correct result, instead of just merging after seeing the first version. It allows AI to output multiple solutions at one time and then compare them. You can quickly correct the AI ​​when it makes a mistake, instead of being led by it.

Counterexamples are very common: you tell AI "write a user login function" and merge directly. The code looks reasonable but leaves SQL injection or token processing vulnerabilities. This is a classic pitfall of uncensored AI output.

How to get it: Use AI tools in your daily work and hone your skills in real scenarios. Pay attention to one or two high-quality AI engineering communities and read the official documentation and update logs of the tools instead of just reading hot search articles.

Value changes: For engineers of the same level, the difference in output between those who can use AI and those who cannot use AI can be several times greater. This is becoming a new watershed.

Ability 5: Communicate and influence others

One of the most overlooked. Let your team, customers, superiors, and subordinates understand your ideas and influence them to take action. AI can help you polish emails, but AI cannot replace real interpersonal scenarios such as negotiations, disputes, and decision-making.

Specific ability points: Able to explain a complex technical decision to non-technical people in a few minutes. Ability to clearly articulate trade-offs in design reviews. Ability to remain professional during conflict and move things forward. Ability to write persuasive design docs. Able to find consensus in cross-team communication. Can help new people grow.

How to obtain: Participate in public lectures at a fixed frequency, such as intra-team tech talks. Write a design doc or technical blog every month. Read classics such as "Nonviolent Communication" and "Crucial Conversations". It can also be helpful to subscribe to a writing training resource for engineers.

Value changes: AI improves the productivity of individual soldiers, but it also increases the complexity of cross-person collaboration. People with weak communication are easily isolated even if their code is good.

Practice paths for these 5 abilities

If you want to spend one or two years focusing on upgrading these five abilities, there is a relatively clear rhythm.

First use a month to do a self-assessment and rate yourself on each of the 5 abilities. Be honest. Next, pick the weakest one, focus on it for a few months, and fix it for a few hours every week. You will see obvious differences in a few months. After you make progress with the first one, add the second one and practice together. Later, challenge some projects that can use the five abilities at the same time, such as leading a cross-team project, responsible for reviewing a production accident, and writing a company-level design doc.

Don’t pursue all 5 items at the same time. Every engineer has a different style. Some are naturally biased towards architecture, some are biased towards business, and some are biased towards diagnosis. Strengthening the 2 to 3 items in which they have comparative advantages to an outstanding level is enough to stand out.

Special advice for young engineers in the AI ​​era

For engineers who have just entered the industry, several empirical suggestions are worth referring to.

First, don’t spend time writing boilerplate. Let AI write what AI can write. Spend your time practicing judgment and architecture. Second, do more hands-on work and less lectures. It is better to write an in-depth article yourself than to read a hundred articles, and develop the habit of regularly producing technical articles. Third, take the initiative to find a mentor. The judgment of senior engineers is the product of accumulated experience. You need shortcuts. Asking people for advice at a fixed frequency is much more effective than reading books.

For senior engineers, the essence of the so-called "age crisis" is often not age, but whether they stop learning. Three types of people will always be competitive: those who insist on learning new things, those who have been deeply involved in a certain vertical field for many years, and those who are conscious of going deeper in the direction of architecture or management.

Differentiation strategies for ordinary engineers

Not everyone has to work as a staff or principal. Ordinary engineers can still make a good living in the AI ​​era.

Strategy one is to delve deeply into a vertical field. In industries such as medical care, finance, manufacturing, education, games, etc., with several years of experience and the support of AI tools, any company needs you. Strategy two, be a tool developer. Every company has needs for internal tools, and the position of internal platform is often more stable than business development. Strategy three, do technical evangelism. People who can write, speak, and have community influence are still scarce in the AI ​​era. Strategy four, be an open source maintainer. A widely used open source project is a resume in itself. Strategy 5: Be a technical consultant or fractional CTO. A senior engineer will serve several early-stage companies at the same time. The degree of freedom and returns are higher than in a full-time job.

None of these are dependent on squeezing into a senior position in a big factory, and are suitable for people with different personalities to move at their own pace.

The long-term relationship between AI and engineers

In the short term, the profession of software engineer will not disappear. All existing companies still need engineers. AI is more about amplifying the output of high-level engineers while compressing low-end jobs. In the mid-term, the career pattern will change, gradually transitioning from "coding mainly" to "judging and guiding AI mainly". Coding ability is still necessary, but more important is judging what to write, how to verify, how to deploy, and how to maintain. It is difficult to linearly extrapolate things that last more than ten years. The speed of technological revolution is unpredictable. The conservative approach is not to draw absolute conclusions.

The best coping attitude is not to be anxious and not to lie down. Learn one or two new tools every year, reflect on the value you create once a month, and maintain a certain output rhythm every week. This active state itself is the most stable survival strategy in the AI ​​era.

FAQ

Which of these 5 abilities should be practiced first?

It depends on which stage you are at. If you are a beginner, practice ability four first, which means making good use of AI tools. If you don’t practice, you will not be able to keep up with your colleagues. If you are at the intermediate level, practice your ability and system design first. This is the key to advancing to senior level. If you are already senior, focus on ability 2, business understanding and ability 5, communication. This is the key to going up. Ability 3 diagnosis is a basic skill that everyone should have, but it is more difficult and can be accumulated slowly in daily work.

Can I develop these abilities without experience in a large factory?

Yes, but the path is different. Large factories have complete design review, SRE processes, and cross-team collaboration scenarios, and these abilities will be naturally trained. Small companies or freelancers need to take the initiative to create scenarios on their own: insist on writing design docs even if no one is reviewing them, participate in open source projects to gain experience in large-scale collaboration, and share and practice communication in the technical community. After a few years of persistence, even a small company with active learning can reach a good level.

Will engineers’ incomes decline in the AI ​​era?

Overall revenue is not expected to decline, but differentiation will increase. The income of senior and staff levels is expected to continue to rise, entry-level positions will be compressed, and mid-level positions will be flat or slowly rising. The most critical thing is not the absolute value, but whether you are continuously upgrading your capabilities. People who have been stuck at a low level for many years will be depressed by the market no matter what the industry.

Do I want to learn hardware or underlying technology?

Look at the direction. If you do application development, hardware is not mandatory, but understanding OS, network, and containers is still the foundation. If you do AI infrastructure, performance optimization, and embedded, the hardware and bottom layer are the core. Generally, engineers do not need to be able to write drivers, but they must be able to use tools such as strace and perf to troubleshoot problems. Basic computer principles are still compulsory.

Does a 35-year-old engineer still have a chance?

Absolutely. Age is not a technical ceiling, but a starting point for experience accumulation. The problem is mentality. Continuously learning new things, delving into a certain vertical field, and consciously developing in the direction of architecture or management, these three types of engineers over 35 years old still have a good position in the AI ​​era. If you stop learning after the age of 35, it is indeed easy to be eliminated, but this has nothing to do with age, but with mentality.

Source of inspiration: Issue 387 of Ruan Yifeng's "Technology Enthusiasts Weekly" https://www.ruanyifeng.com/blog/2025/08/weekly-issue-387.html