The 2026 Global Intelligence Crisis

What 4 engineers with 10+ years of experience say about staying relevant in the AI era

I did no work for a year and no one noticed

Ask HN: What skills are future proof in an AI driven job market?

Early years matter because that is when foundational skills are formed. Debugging instinct. System intuition. Precision. Taste. Skepticism.

Jeremy didn't get laid off. He got leveraged.

继续做通用 UX 是最危险的位置，它正是最容易被 AI 和产品经理上下夹击的中间层。

Such tools promise to turn coders into project managers, letting them delegate and oversee many more tasks than they could cope with by themselves.

BLOG: How NOT to Answer the Salary Question

Knowing how discourse communities work will not only help you as you navigate the writing assigned in different general education courses and the specialized writing of your chosen major, but it will also help you in your life after college.

Because questions vary significantly from discipline to discipline and from field to field, it is important that you assess your questions according to the discourse community you are writing within.

At least oldmedia, even in its decline and with all its problems, still offers a careerpath.

Historically, he writes, colleges and universities aimed to imprint capital-C Culture—especially a familiarity with a nation’s great texts and intellectual traditions—on young people. Today, however, students more often are seen and see themselves as consumers who are buying diplomas in order to signal their employability. In this model, the values that animate higher education are job preparation, skill building, and networking, not intellectual engagement or humanistic fulfillment. The University in Ruins

You will be supported during your studies to gain the capabilities that are relevant to you and these will take time to develop.

This blog from the University’s Careers Service gives helpful examples of how you can evidence your digital capabilities when updating your CV.

The most troubling of the findings, to me at least, was the impact of the technology on job satisfaction, especially in terms of their creative contribution, even from the scientists that derived the most value.  It’s possible that this is a consequence of the changes and will prove temporary, or it’s possible that this will correct itself by attracting people with different skills and interests to the jobs.

A guide to finding diamonds in the rough

In my brag document, I like to do this by making a section for areas that I’ve been focused on (like “security”) and listing all the work I’ve done in that area there. This is especially good if you’re working on something fuzzy like “building a stronger culture of code review” where all the individual actions you do towards that might be relatively small and there isn’t a big shiny ship.

80/15/5. Spend 80% of your time on low-risk/reasonable-payoff work. Spend 15% of your time on related high-risk/high-payoff work. Spend 5% of your time on things that tickle you, regardless of payoff. Teach the next generation to do your 80% job. By the time someone is ready to take over, one of your 15% experiments (or, less frequently, one of your 5% experiments) will have paid off and will become your new 80%. Repeat.

We’ve (painstakingly) manually reviewed 310 live MLOps positions, advertised across various platforms in Q4 this year

Take these two statements:“I see what you're saying, but in my experience, the other way has worked best.”“In my experience, the other way has worked best, but I see what you're saying.”The first one negates “I see what you’re saying.”The second one negates “In my experience, the other way has worked best.”When you use “but”, it’s important to understand which part you are negating.

We wrześniu 2023 roku w większości badanych zawodów zanotowano spadki liczby ofert pracy rok do roku Największy widoczny jest w branży IT – pracodawcy opublikowali o 52 proc. mniej ofert rok do roku

擔任公部門機構、私部門組織或非營利組織的董事；教學、研究、出版或影片製作；聯邦、州或城市層級的公共或公民服務；對新創公司提供諮詢或投資；行業協會和聯誼會的領導職務；在構想論壇、節慶和會議上演講或籌辦這類活動。設想自己擁有一個工作組合，你的職務位於這個組合的正中央，四周有各種外部活動來補充這個職務，而你把你在每個領域中學到的知識，有效運用到其他領域。

Spend your career hanging out with people you like working with, doing work you enjoy, trying new experiences, and having fewer regrets.That’s how you retire one day & make it to your deathbed a happy human.

You don’t know what day it is. Monday feels like Saturday night fever.

I always allocate a year: six months to get up to speed on the internal culture, tools, and processes; another six months to get your first performance review as a “ramped-up” engineer.

I posted my interview tracker spreadsheet on Twitter under the guise of “being transparent.” The very next day, I was on a phone screen with a recruiter when they said “Yeah, I looked at the interview spreadsheet you posted on Twitter and just based on that I can tell you’re not going to be a good fit here. I just took this call as a favor to <redacted> since they referred you.”

Just because you’re getting a lot of offers to interview does not mean that you are a hot commodity. Nor does it indicate a high likelihood of obtaining an offer.

If you had asked me right after I got laid off how long it would take me to get back to work, I would have said three months – including two months of vacation. It took me a year.

“It only takes five minutes to break the cycle. Five minutes of exercise and you are back on the path. Five minutes of writing and the manuscript is moving forward again. Five minutes of conversation and the relationship is restored. It doesn't take much to feel good again.”

At one point, Eno had to earn money as paste-up assistant for the advertisement section of a local paper for three months. He quit and became an electronics dealer by buying old speakers and making new cabinets for them before selling them to friends.[12]

Once upon a time, I worked at a magazine, reporting to a white woman who, early in our working relationship, told me that she didn’t consider me a threat because “a black woman will never have this job.”She then proceeded to use every one of my ideas to completely redesign the magazine we worked for. It was the end of a moment in publishing when such a thing as a “big magazine job” still existed. I hung on because I really wanted to be an editor in chief one day and knew that quitting would take me out of the game.

A commonplace book is what a provident poet cannot subsist without, for this proverbial, reason, that "great wits have short memories;" and whereas, on the other hand, poets, being liars by profession, ought to have good memories; to reconcile these, a book of this sort, is in the nature of a supplemental memory, or a record of what occurs remarkable in every day's reading or conversation. There you enter not only your own original thoughts, (which, a hundred to one, are few and insignificant) but such of other men, as you think fit to make your own, by entering them there. For, take this for a rule, when an author is in your books, you have the same demand upon him for his wit, as a merchant has for your money, when you are in his. By these few and easy prescriptions, (with the help of a good genius) it is possible you may, in a short time, arrive at the accomplishments of a poet, and shine in that character[3].

By becoming a hybrid you can choose how you want to be unique. Countless unique combinations are available to you.

Being U-shaped requires bravery, because it’s so unusual. U-shaped people tend to be subjected to greater skepticism, because no one else really understands what they alone can see.

T-shaped. They tend to be natural leaders because they understand how different responsibilities overlap, and how to construct effective teams and processes.

The U-shaped path means developing skills that are not often found together. Like engineering and dancing, or singing and design.

The T-shaped hybrid path is one that many curious people follow. You grow your skillset and experience in areas that are adjacent to your dominant expertise. For example engineering and design, or singing and dancing.

Be aware that staying at a company too long means you are likely to earn less over your career. But moving too soon means you won’t get the necessary experience that may benefit you later on.

Learning and adaptation – 3 – 6 months – getting to grips with the new company, team, and their processes. Creating value for the organization – 6 – 12 months – adding value to the business by becoming a functioning member of the team.Becoming a role expert – 6 – 18 months – owning the role completely and helping to shape the direction of the team.

Software engineers typically stay at one job for an average of two years before moving somewhere different. They spend less than half the amount of time at one company compared to the national average tenure of 4.2 years.

The average performance pay rise for most employees is 3% a year. That is minuscule compared to the 14.8% pay raise the average person gets when they switch jobs.

If you work at a software company, there are some pretty common categories of work that people often avoid, including

Whenever people ask me for advice on career growth, I share what has worked reasonably well for me: find a growth company, one that really needs you to get work done, and then tackle the unpleasant work that everyone avoids.

Imagine for a moment that, by some quirk of the universe, you are sharing your workspace with a time traveller. Specifically, yourself from 1 year in the future. How will you react to your new co-worker?

A defining feature of the Harvard translational research training program Denny and I founded in 1999 (more here) was that our monthly speakers – brilliant medical innovators like Robert Langer, Denise Faustman, Judah Folkman, Jeff Flier – had to begin their talks by telling students about their real career journeys, which invariably were far more meandering and uncertain than the linear narratives generally deployed to introduce distinguished speakers, where one’s life path can seem like a series of deliberate steps leading up to the present moment.

you should never save for two meetingswhat you can accomplish in one.

Job interviews are not really about you. They are about the employer’s needs and how you can fill them.

Build identity capital. In your 20s do three fascinating things that job interviewers and dinner companions will want to ask you about for the rest of your life.

A recent book that advocates for this idea is Range: Why Generalists Triumph in a Specialized world by David Epstein. Consider reading Cal Newport’s So Good They Can’t Ignore You along side it: So Good They Can’t Ignore You focuses on building up “career capital,” which is important for everyone but especially people with a lot of different interests.1 People interested in interdisciplinary work (including students graduating from liberal arts or other general programs) might seem “behind” at first, but with time to develop career capital these graduates can outpace their more specialist peers.

if you are on the job market looking for a team that cares more about being agile than going through the motions to look agile, ask these questions

an asshole test. You see who turns into an asshole under pressure and they don't make it to the next round".

Reading puts candidates at ease compared to writing code.  As an interviewer, stress is your enemy because it raises adrenaline which lowers IQ by several points, causing you to miss good candidates.   Candidates prefer reading partly because they are relieved to not have to write code, but also because the interviewer can easily adjust the reading questions to accommodate for the candidate’s skill.

With schools open now, State governments must set clear priorities, says Anurag Behar, CEO, Azim Premji Foundation. The syllabus from Classes I to XII must be re-configured, reducing content without compromising on core learning objectives.

Technology → Cool Stuff to Work OnIntellection → Smart People to Work WithCertainty → Repeatability in Work Environment

So when the morale on the team dips (usually related to lack of clarity or setbacks making things take a long time) people leave the team, which further damages morale and can easily result in a mass exodus.

People come and go (which has the net impact of making you feel like you are just a resource)

“What is your definition of success?” I hemmed and hawed a bit, until I finally said, “I suppose success is your days looking the way you want them to look.”

A lot of us are going to die of unpredictable diseases, some of us young. Really, don't spend your life getting fitter, healthier, more productive. We are all going to die, and Earth will explode in the Sun in a few billion years: please, enjoy some now.

While people who are both trustworthy and competent are the most sought after when it comes to team assembly, friendliness and trustworthiness are often more important factors than competency.

The researchers found that people who exhibited both competence, through the use of challenging voice, and trustworthiness, through the use of supportive voice, were the most in-demand people when it came to assembling teams.

Rather than being led by a single ambiguously defined and overburdened CDO, data teams should have two representatives in senior management: A VP of data responsible for managing the team’s daily operations, and a chief analytics officer.

For example instead of "the team were incredibly frustrating to work with and ignored all my suggestions", instead "the culture made it very hard to do quality engineering, which over time was demoralising"

Before we dive any further, let’s clarify first what we mean by a career ladder. Career laddering is typically a system used to show what expectations are at different levels of a role, a purpose of which is defining how one might be promoted. This can have different forms, but tends to be an internal document that states the expectations of a staff member at any given stage of their career.

The real world is the polar opposite. You’ll have some ultra-vague end goal, like “help people in sub-Saharan Africa solve their money problems,” based on which you’ll need to prioritize many different sub-problems. A solution’s performance has many different dimensions (speed, reliability, usability, repeatability, cost, …)—you probably don’t even know what all the dimensions are, let alone which are the most important.

The hard truth that many companies struggle to wrap their heads around is that they should be paying their long-tenured engineers above market rate. This is because an engineer that’s been working at a company for a long time will be more impactful specifically at that company than at any other company.

I can give the following advice: be a multiplier! I've seen many talented senior engineers who were very productive on their own but failed to help others to grow.

you don’t want to miss out on a great engineer just because they spent all of their energy making great products for prior employers rather than blogging, speaking and coding in public.

First, try to avoid making career decisions while in a bad mental place. Take a two week vacation. Get out of your house a few times if you’ve been living and working at home for the last eighteen months. Try to restart something you loved but have stopped due to pandemic concerns. You don’t need to find a sustainable long-term solution, just enough of a break to find some space to reflect before making life-altering changes.The second piece of advice I offer is that great careers are often presented as linear growth stories, but if you dig deeply enough they often have a number of lulls embedded inside them. When you’re high energy, these lulls are opportunities to learn and accelerate your trajectory. When you’re low energy, they are a ripe opportunity to rest. It’s easy to forget about these pockets in retrospect, and I recently realized I’ve gotten so practiced at narrating my own career in a particular way that I’ve forgotten my own lulls that have made the faster periods sustainable.

A good philosophy to live by at work is to “always be quitting”. No, don’t be constantly thinking of leaving your job 😱. But act as if you might leave on short notice 😎.

An incomplete list of skills senior engineers need, beyond coding

When people value their attention and energy, they become valuable

career capital

From an employers perspective, I believe there are many advantages:

pronunciation cues in an individual’s speech communicate their social status more accurately than the content of their speech.

reciting seven random words is sufficient to allow people to discern the speaker’s social class with above-chance accuracy.

One of the most difficult transitions for leaders to make is the shift from doing to leading.

A second trap is thinking that you just need to work on yourself in order to grow your career. For example, "To progress, I just need to get better at [insert skill.]" But you are only one part of the equation.There is a whole other part of the equation, which is your environment. Your environment either limits or amplifies your own ability to get better at a skill.

Evaluating impact isn't easy. It is complicated by a few factors:Impact is the result of multiple other variables.Those variables are interrelated and have confounding factors.Impact can be subjective to the individual.A lot of times you aren't even aware of what is holding you back, or what to evaluate on.

"Should I leave my current role?""How do I compare or choose between two or more opportunities?""What is preventing me from moving forward?"

I can't recommend the Data Engineer career enough for junior developers. It's how I started and what I pursued for 6 years (and I would love doing it again), and I feel like it gave me such an incredible foundation for future roles :- Actually big data (so, not something you could grep...) will trigger your code in every possible way. You quickly learn that with trillions of input, the probabily to reach a bug is either 0% or 100%. In turn, you quickly learn to write good tests.- You will learn distributed processing at a macro level, which in turn enlighten your thinking at a micro level. For example, even though the order of magnitudes are different, hitting data over network versus on disk is very much like hitting data on disk versus in cache. Except that when the difference ends up being in hours or days, you become much more sensible to that, so it's good training for your thoughts.- Data engineering is full of product decisions. What's often called data "cleaning" is in fact one of the import product decisions made in a company, and a data engineer will be consistently exposed to his company product, which I think makes for great personal development- Data engineering is fascinating. In adtech for example, logs of where ads are displayed are an unfiltered window on the rest of humanity, for the better or the worse. But it definitely expands your views on what the "average" person actually does on its computer (spoiler : it's mainly watching porn...), and challenges quite a bit what you might think is "normal"- You'll be plumbing technologies from all over the web, which might or might not be good news for you.So yeah, data engineering is great ! It's not harder than other specialties for developers, but imo, it's one of the fun ones !

Downloading a pretrained model off the Tensorflow website on the Iris dataset probably is no longer enough to get that data science job. It’s clear, however, with the large number of ML engineer openings that companies often want a hybrid data practitioner: someone that can build and deploy models. Or said more succinctly, someone that can use Tensorflow but can also build it from source.

When machine learning become hot 🔥 5-8 years ago, companies decided they need people that can make classifiers on data. But then frameworks like Tensorflow and PyTorch became really good, democratizing the ability to get started with deep learning and machine learning. This commoditized the data modelling skillset. Today, the bottleneck in helping companies get machine learning and modelling insights to production center on data problems.

Overall the consolidation made the differences even more pronounced! There are ~70% more open data engineer than data scientist positions. In addition, there are ~40% more open ML engineer than data scientist positions. There are also only ~30% as many ML scientist as data scientist positions.

Data scientist: Use various techniques in statistics and machine learning to process and analyse data. Often responsible for building models to probe what can be learned from some data source, though often at a prototype rather than production level. Data engineer: Develops a robust and scalable set of data processing tools/platforms. Must be comfortable with SQL/NoSQL database wrangling and building/maintaining ETL pipelines. Machine Learning (ML) Engineer: Often responsible for both training models and productionizing them. Requires familiarity with some high-level ML framework and also must be comfortable building scalable training, inference, and deployment pipelines for models. Machine Learning (ML) Scientist: Works on cutting-edge research. Typically responsible for exploring new ideas that can be published at academic conferences. Often only needs to prototype new state-of-the-art models before handing off to ML engineers for productionization.

I scraped the homepage URLs of every YC company since 2012, producing an initial pool of ~1400 companies. Why stop at 2012? Well, 2012 was the year that AlexNet won the ImageNet competition, effectively kickstarting the machine learning and data-modelling wave we are now living through. It’s fair to say that this birthed some of the earliest generations of data-first companies. From this initial pool, I performed keyword filtering to reduce the number of relevant companies I would have to look through. In particular, I only considered companies whose websites included at least one of the following terms: AI, CV, NLP, natural language processing, computer vision, artificial intelligence, machine, ML, data. I also disregarded companies whose website links were broken.

There are 70% more open roles at companies in data engineering as compared to data science. As we train the next generation of data and machine learning practitioners, let’s place more emphasis on engineering skills.

When you think about it, a data scientist can be responsible for any subset of the following: machine learning modelling, visualization, data cleaning and processing (i.e. SQL wrangling), engineering, and production deployment.

BERT engineer is now a full time job. Qualifications include:Some bash scriptingDeep knowledge of pip (starting a new environment is the suckier version of practicing scales)Waiting for new HuggingFace models to be releasedWatching Yannic Kilcher’s new Transformer paper the day it comes outRepeating what Yannic said at your team reading group

There’s a story about an art teacher that split their class in half. They told one half of the students that they’d be graded based on a single piece of work, and the other half that they would be graded on the quantity of work produced. The half that was being graded on quantity ended up producing higher quality pieces. By iterating and learning from their mistakes they actually ended up producing better work than the students that only had to produce one piece. Quantity leads to quality. Share your work Sharing work helps you to think and develop. The feedback you get feeds into the next iteration.

Junior school is great because you don’t learn to find a job. You’re at school to grow as a citizen, not to prepare to your future life, and actually you’re better not to think about your future if you don’t want to question yourself about the meaning of your existence. This is the same thing for side projects. See them as a way to discover a new topic and enlarge your horizon, not as something that you could action in your current position or to find a new job.

Even from a purely technical point of view, you will not really understand a library or a framework by just sticking to the tutorial or its basic features. You’ll have to dig into and to face its most painful aspects to deeply understand it.

Today, I recommend not hiding your side projects, but to not displaying them as a primary part of your identity, to not scare the recruiter and let him feel that you have a secondary plan.

You can generally assume that a developer coming from a cutting-edge company has better chances to be a great developer than a developer coming from a Java 1.4 shop. He may not be smarter, but he has been hired by a company with a most demanding hiring process, and has been surrounded by some of the smartest developers.

First, most recruiters don’t care about your personal projects or how many meetups you went during the year. What matters the most is your current company - and by that I mean the name of your current company.

Daydreaming at Work Can Fuel Creativity

What the question does is that it creates a powerful anchoring effect. A candidate who gives this information away will typically ask for only a bit more than what she is currently getting. The human resources dude will then have a big internal smile: the candidate's expected salary is below the range that was decided for that position. He will then happily give the candidate 3.000 dollars more than what the candidate asks for.

To be reliably able to focus on something, you need to be intuitively, emotionally invested in the outcome.

The output of knowledge workers is extremely skewed based on focus. The productivity tiers seem to be:<10% focused on the job at hand: meaningful risk of getting fired.10-50% focus: “meets expectations,” gets regular raises.50%+ focus: superstar, 10x engineer, destined for greatness.

What you actually need from an ML/Data Science person:- Experience with data cleaning (this is most of the gig)- A solid understanding of linear and logistic regression, along with cross-validation- Some reasonable coding skills (in both R and Python, with a side of SQL).

deep learning is good in some scenarios and not in others, so anyone who's just a deep learning developer is not going to be useful to most companies.

Having worked with researchy vs more product/business driven teams, I found that the best results came when a researchy person took the time to understand the product domain, but many of them believe they're too good for business (in which case you should head back to academia).

but companies will discover that ML is (for the vast majority) a shiny object with no discernible ROI. Like Big Data, I think we'll see a few companies execute well and actually get some value, while most will just jump to the next shiny thing in a year or two.