Such rapid change is understandably scary, especially for people worried they will lose their job. And on a deeper level, it can feel like an existential threat – imagine that the skills you have spent a lifetime developing suddenly become obsolete? These concerns are entirely valid, but the future is far from hopeless. In fact, it’s a challenge that can be met – with the right kind of preparation. And that preparation isn’t just about learning how to use AI – it’s about learning how to think about AI in a fundamentally different way.

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When most people talk about artificial intelligence (AI) , they focus on the “intelligence” part—as if AI were a person, or at least a machine capable of thinking. But I don’t think that that’s the most useful frame for thinking about AI. Yes, when AI is given a task, it may deploy patterns of reasoning similar to a human, but that doesn’t make it sentient or conscious. That doesn’t make it human. A more helpful way to conceptually think about generative AI is to treat it like something far more mechanical: a transfer function.

In engineering, a transfer function is a mathematical model that describes how a system transforms an input signal into an output signal. You feed it something, it operates on that input (often in a way that’s opaque or hard to inspect), and it gives you a result. It’s a “black box” that takes an input and somewhat predictably turns it into an output.[3]

FM radio offers a helpful analogy for how engineers think about transfer functions. When someone speaks into a microphone at a radio station, their voice produces an audio signal — let’s call that the input, F(s). To broadcast it over long distances, this raw signal needs to be modulated onto a high-frequency carrier wave. That’s where frequency modulation (FM) comes in — a process we can represent abstractly as a system or “black box,” which we’ll call H(s). This system takes in the raw audio F(s) and transforms it into a modulated signal G(s) that can travel through the air to your radio. On the receiving end, your radio performs the inverse operation: it demodulates G(s) to recover the original voice signal — another system we might call I(s).[4]

Now, rather than getting bogged down in the complex math of modulation and demodulation, engineers often model each of these steps as a transfer function: a simplified abstraction of how inputs are converted to outputs.[5]

This is how we should start thinking about generative AI. It takes in inputs—questions, prompts, problems—and produces outputs—answers, designs, solutions. And while it’s tempting to focus on how the model arrives at those results, and to attribute some sort of “intelligence” to the mechanisms by which it does, what actually matters is whether the outputs are useful. Whether they help us solve the problem we started with.[6]

So, what does this mean for the future of jobs?

The Transfer Function Employment Worldview

Let’s imagine a world where the tasks for almost all digital “work”—writing, designing, coding, planning, etc.—can be handled by an AI agent that behaves like a transfer function. You give it a prompt. It gives you an answer. If the answer is good, you use it. If it’s not, you revise the input or try another function.

In this world, human effort revolves around three core activities:

1. Defining the input (the problem): What exactly do we want the AI to solve? This is where we figure out what matters, what needs to be built, what people want or need.

2. Evaluating the output (the solution): Did the AI produce something useful? Is it accurate, safe, valuable, and aligned with what we were hoping to achieve?

3. Acting on the solution in the physical world: Many jobs still require moving in the real world—fixing HVAC systems, delivering food, comforting a child, performing surgery.

These three activities lead to three categories of human employment:

Category 1 Jobs: LLM Direction. These are the people who define problems and evaluate outputs. They might work in product design, law, healthcare, marketing, education, or policy. Their job is to steward human needs and ensure that machines are solving the right problems in the right way.

Category 2 Jobs: Physical Implementation. These are the people who act on the outputs. They do things that AI cannot (yet) do effectively in the physical world—like building physical goods, implementing physical world services, repairing machines, transporting goods, providing hands-on care, etc.

Category 3 Jobs: Data Stewardship. I would also add in a third type of job, which I call “data stewardship” for people who are tasked with feeding the right data to the right AI systems. In a world where access to information is uneven—because companies, governments, and individuals all have protected datasets—someone needs to manage, broker, and gatekeep data access. This role may shrink if data becomes open and universal, but in practice, it’s likely to be critical for a long time. This is the part that is staring back at the black box and tweaking the black box.

So how do all these categories work together in the transfer function analogy of Generative AI? Well, consider the schematic diagram below. Category 1 jobs straddle the transfer function… they help define its input F(s) and measure its output G(s). Category 2 jobs take the G(s) and convert it into practical things that people need in the world that can’t be easily automated. And Category 3 jobs help manage the function of the transfer function itself – they help set the data that go into creating H(s).

Examples

To illustrate how these jobs may work in action, let’s look at healthcare. Imagine a patient walks into a clinic with abdominal pain:

• The doctor (a Category 1 role) listens to the patient, asks follow-up questions, and identifies what the likely problem is. That’s the first type of activity defined above—defining the input. Then the doctor might plug symptoms into a diagnostic tool powered by AI. The AI recommends a diagnosis and treatment. The doctor then reads the output and decides if it makes sense, possibly consulting a second AI or his/her own judgment. That’s the second type of activity—evaluating the output as good or bad.

• If a blood test is required, then a phlebotomist is called in to draw blood (a Category 2 role). That’s the third type of activity—a physical action that the AI cannot do.

• If the AI suggests surgery, and robots aren’t advanced enough, the surgeon performs the operation—again, the third type of activity.

• Of course, someone might be needed to help manage which personal health data are used by the LLM in diagnosis, how these data are used, and whether that is ok. That’s an example of a Category 3 role (data management).

Another example focuses on Hollywood filmmaking:

• In the future, AI might be able to write scripts, generate storyboards, create synthetic voices, edit footage, and even render entire scenes. But it still can’t decide why a story needs to be told a certain way. That’s the job of the director—a classic Category 1 role. They decide on the creative vision and then evaluate what the AI produces. Is the tone right? Is the pacing effective? Does it move the audience?

• Meanwhile, the cinematographer might still physically handle real-world camera setups when shooting on location (if we haven’t fully digitized everything), and actors may still be needed for human connection and nuance that audiences value. These are Category 2 roles.

• The data manager here might be the person curating massive datasets of film styles, audience preferences, or historical footage that the AI can pull from. He/she might also manage branding data and name-image-likeness (NIL) data, which require pre-authorization and contracting to use. A Category 3 role.

So even in a highly digitized world, we still need three categories of humans: those who guide and evaluate AI, those who do the physical things AI can’t, and those who help manage what data the AI can/cannot have access to.

What This Means for the Future of Jobs

This way of thinking helps us answer some tricky questions about the future of work. Rather than worrying about whether AI will “take our jobs,” the better question is: which category of job will each of us evolve into?

If you’re a writer, designer, or knowledge worker, your future might look less like “doing the work” and more like setting up the problem and checking the solution. You become a kind of creative director—not the artist, but the person guiding the art. Not the coder, but the one asking the system to code something, checking it, refining the brief.

If you’re a hands-on worker—plumber, nurse, electrician, chef—your work may remain remarkably unchanged for some time. The AI can suggest what to do, but it still can’t crawl under a sink, hold a hand, or cook an omelet (yet). That’s good news for Category 2.

And if you work in data infrastructure, legal compliance, or security, you may find yourself in a growing Category 3 role: the invisible hand that determines what the transfer function can see.[7]

Final Thought

In a world of transfer functions, the key skill is not learning how to out-compete the AI. It’s learning how to collaborate with it—to become the person who defines the right problems, evaluates the right solutions, and ensures the work leads to real-world impact. We’ll still need judgment, ethics, empathy, and taste. The difference is that we’ll use those qualities less for doing the task and more for steering the system. So, ask yourself: when the transfer function does the middle, what part do you want to play—upstream, downstream, or behind the scene? [8]

[1] Goldman Sachs Global Investment Research: Generative AI could raise global GDP by 7% (2023)

[2] World Economic Forum: The Future of Jobs Report 2023

[3] Note that transfer functions can be deterministic, where the output always matches the input for multiple trials, or non-deterministic (probabilistic), where the output is influenced by an element of randomness. LLMs used in Generative AI are best defined as probabilistic transfer functions because the same prompt can generate different responses. In LLMs, the randomness or creativity of the output is modulated by setting a parameter called "temperature".

[4] For the geeks out there, the “s” in the three functions above is a complex variable, and s = s + jω , wherein ω is the frequency of the signal (in radians), j is the square root of -1, and s is the real part of the variable. In most practical situations, s = 0.

[5] This is a gross simplification of transfer functions and their incredible value to engineers (which involve, among other things, simplifying the math used for time-based systems). But the point of this article isn’t’ to dive into electrical engineering control theory – it’s to use the transfer function analogy to help explain how to think about employment in the age of Generative AI.

[6] Of course, engineers will rightly note that this analogy has its limits. Transfer functions in control theory are typically linear, deterministic systems that convert signals from the time domain to the frequency domain. Generative AI, by contrast, is nonlinear, probabilistic, and doesn’t involve those kinds of time-to-frequency transformations. But the metaphor still holds at a conceptual level: in both cases, a system takes an input, performs a complex and mostly opaque transformation, and produces a usable output.

[7] Robert Capps of The New York Times Magazine recently published “22 New Jobs A.I. Could Give You”, where he shares his perspective on how human jobs may evolve in a Generative AI world.

[8] Thanks to Cansa Fis, Lily Lu, and Cam Hauser for providing valuable feedback on this essay!

This experience (and my reflection on it) has had me asking a question lately: What if everyone had the opportunity to wake up each day and go to work at a job that they truly loved? Not a job loved because it was comfortable and paid well, but rather, because it was the perfect fit for one’s personality and purpose. A job with a mission that resonates deeply with core beliefs and life goals. A job that simply feels right — inspiring, challenging, and truly understanding. A job where one’s unique talents are put to their very best possible use.

If that’s your dream—work that aligns with who you are—then you can’t just expect it to land in your lap. You have to start with knowing what matters to you. Of course, that doesn’t mean you’ll know all the opportunities out there – you won’t. But when you’re clear on your values and what gives you energy, you’ll be far more likely to recognize the right fit when it appears. And that’s why one of the most important questions in any job interview is also the most revealing: Why are you applying to this job?

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One of the most unexpectedly rewarding parts of my time at Amazon was getting to interview and hire for our new projects—a task that taught me a lot about what really matters in building a great team. In these interviews, my task was to ask specific questions (usually from a template) to assess (i) the candidate’s skill in 1-2 functional abilities and (ii) the candidate’s demonstration of 2-3 of Amazon’s 16 Leadership Principles (LPs).[1] For functional abilities, applicants would typically have to perform a programming task (for software engineering roles), or to walk us through business case study (for management roles). For leadership principles, we would usually ask the applicant to “describe a time…” that he/she showed competency of that principle. Naturally, candidates who demonstrated functional competency and had good stories from their lives on how they demonstrated LPs would get high ratings. Within a few days of the interview loop, the hiring team would convene and discuss their answers to these questions.[2]

This interview structure was pretty good at helping the hiring team objectively compare the qualifications of candidates competing for a role. However, to me? It never felt complete. You see, unlike other parts of the company tasked with continuing to build XYZ at 5% greater efficiency or 5% lower cost, the goal of our special division of Amazon was to come up with new, world-changing ideas from scratch. To imagine. To create. To build before the plans were written. Our division lived in an ambiguous world where everything was possible but not everything could (or should) be done. So, for us, more important than functional capabilities or stories of past accomplishments was knowing if the candidate was motivated in the right way. Think about that: you can find someone who has all the capabilities to do a good job, and has all the stories to show that they have leadership qualities, but how do you know if he/she really cares about what you’re trying to accomplish? How do you know if that person is going to show up each day and give it his/her all, or just show up each day and clock-in and clock-out?

In Amazon, we called this question the "Mercenary vs. Missionary" question. Let me explain. Mercenaries in a workplace—much like mercenaries in battle—are working for their pay.[3] They will deliver 100% of what you ask them, collect their paycheck, and go home, fulfilling their obligation.[4] Mercenaries make up the vast majority of job applicants, and being one is by no means a bad thing. The good ones can be excellent and even ideal employees for many teams.

But when you’re hiring for a moonshot division like Google X or Amazon Grand Challenge—places focused on building things brand new to society—you need more than just reliable, clock-in, clock-out employees. You need people driven by more than just a paycheck. To build a team like that, you need missionaries: people who believe in the mission itself. Missionaries aren’t motivated by salary or promotions to help climb the corporate ladder; their real satisfaction comes from building the product, launching the service, and seeing the greater impact in the world. They understand that meaningful work often demands more than 100%, and the best ones will find ways to push even further. They stick around to work after business hours, not because their boss told them to work later, but because they want to — because it gives them energy and joy.

Now, I know what you might be thinking: Adam, isn’t this just a sneaky way to find people who will work all day and all night? Isn’t this focus on “mission” just a cover for exploitation, like we sometimes see at tech companies? Honestly, I used to worry about that too. But over time, I realized that’s not the goal — and it’s definitely not the point. Yes, I want people who are willing to work a little longer and stay a little later. But not because someone tells them to, and not because they’re being pushed or punished. I want people who choose to put in that extra energy, because they believe in what we’re building. Because the work itself gives them meaning.

When you build a team like that, something powerful happens: people show up excited every day, and that excitement is contagious. The energy becomes self-sustaining, creating a virtuous cycle of motivation, collaboration, and real joy in the work. It isn’t about pushing people to exhaustion — it’s about unlocking a deeper sense of purpose that makes teams truly come alive. In my experience, the difference between a team driven by the mission vs. one driven by personal goals (money or status) is night and day. When people believe they’re making a meaningful impact, the culture transforms, performance accelerates, and the work becomes both productive and deeply fulfilling.

But here's the problem – you want missionaries, but you can’t just ask interview candidates if they are mercenaries or missionaries. You can’t say: are you willing to work more than 100%? I mean you could, but it makes you sound like a dick and you won’t get an honest answer. So you have to be a little more coy in your questioning. You need to ask what I call the “secret question”.

The secret question of all interviews is so simple that few notice how powerful it is: Why are you applying for this job? The magic of this question is not in the question itself, but in the answer. Case in-point: almost 9 out of 10 jobseekers I ask this question to will answer it in the following way:

“I’m applying for this position because I think it would be a great opportunity for me to use my knowledge of machine learning, to build my managerial skills working with dynamic, cross-functional teams, and to collaborate with a group of really interesting people. Plus, Amazon is a great company that’s doing some amazing work in the world. And honestly, I’d love living in Seattle — the access to nature and the outdoors is incredible. Overall, I think this would be a great role for me.”

This is a perfectly honest and rational answer to the question – the candidate is very clear about why he/she is applying for the role. But there is something a little misaligned. Something really, really big. And that misalignment is that the interviewee’s energy is not focused toward the mission, but towards his/her needs. There is no mention of the reason the job exists. No mention of what the team needs. Let’s contrast the answer above with this one:

“Why am I applying for this position? Honestly, because it’s one of the coolest projects I’ve ever heard about. I recently read your group’s paper about building a new generative AI agent to help people earn a bachelor’s degree for a fraction of the cost they would pay at a traditional university — and I thought, wow – that could improve the lives for millions of people. How can I help? So, I started researching: what kinds of ML tasks would be critical for making this happen? I even put together a list of key tasks that might be helpful [applicant hands over the list]. What do you think — could I just start building some of these today? I mean, I really believe in this project. I can’t think of anything more exciting than getting up every day and using my skills to help make it real.”

See how much more energy is in this answer? See how motivated the applicant is? I would 100% hire this applicant over someone with higher technical competency, but a more mercenary attitude.[5]

Understanding a job applicant’s motivation is critical — not just because it predicts how much energy he/she will bring to the work, but because it signals something even more important: his/her capacity to learn. In zero-to-one jobs, answers are rarely obvious, and challenges often fall outside anyone’s existing expertise. In these moments, the ability to stay curious, self-teach, and push into new areas matters more than simply “already knowing” the right answer. People who are truly motivated by the mission are far more likely to do that — to keep learning, even when it’s hard or inconvenient.

So, whether you’re an employer building a team or a jobseeker looking for your next role, remember what’s at stake: the chance to create a world where more people feel deeply connected to their work. For employers, that starts with asking the right question—and listening closely to find those rare candidates who believe in the mission, not just the perks. And if you’re lucky enough to find them, don’t just compensate them, but invest in them by paying them very, very well and giving them opportunities to grow – missionaries can’t survive on passion alone. For jobseekers, it means being honest about why you’re applying. If you truly care about the work and the people doing it, say so—and show it. That kind of alignment isn’t just good for productivity. It’s how we move closer to a world where more of us wake up, go to work, and feel like we’re exactly where we’re meant to be.[6]

[1] The LPs are not just corporate blah. They are taken very seriously in Amazon, and form the basis of the company’s culture: the “glue” that binds 1.5 million employees worldwide.

[2] For those unfamiliar, Amazon’s interviewing process comprises two stages. Stage 1, called a “phone screen,” is a 30-60-minute call with a recruiter or hiring manager to confirm the candidate meets the basic qualifications. If successful, the candidate moves to Stage 2: a full-day interview known as a “loop.” A loop typically comprises 5-7 one-on-one interviews with members of the hiring team over the course of 8 hours (usually you get a 30 min break for lunch – classic Amazon!). Each one-on-one interview lasts an hour, with most of the time spent with the job applicant answering questions posed by the interviewer.

[3] Mercenaries don’t have to be focused exclusively on the paycheck. They can also be focused on professional ambition (e.g., seeking to use the job as a stepping stone to a better position within the company), or for a title that their family can be proud of. But the motivation remains the same – a primary focus on personal goals.

[4] I say 100% here, but for the teams that I’ve been part of, if we ever got the impression in an interview that someone would work less than 100%, that would be an immediate "no-hire".

[5] Within reason. Energy alone can’t make you a great programmer or give you the wisdom that comes from deep experience and competency in a subject. But it really helps.

[6] Special thanks to , , and for providing valuable feedback on this essay!

I knew my dad offered his advice with the best of intentions. He was worried I’d end up unemployed (or even worse, unemployable). You see, back in his generation, having a job meant stability, value, and respect. Being jobless was a mark of dishonor – a sign that you didn’t have anything that employers wanted. It was OK to change jobs occasionally, but you always lined up the new job before leaving the current one – otherwise you’d be setting yourself up for failure.

But what my father didn’t realize was that while my first job as a management consultant provided a good salary and was a valuable apprenticeship in the business world, it also came with a lot of stress and anxiety. I had no free time. I was working so hard that my meals alternated between dollar pizza, peanut-butter crackers, and frozen Trader Joe’s pasta. My weekends had two goals: catching up on lost sleep, and catching up on work I was supposed to have completed the week before. Working in this way posed a dilemma – while a steady paycheck provided me with a sense of comfort, how was I supposed to figure out what I really wanted to do in life if all my time and attention was focused on the job supplying that paycheck? Or more succinctly: how can one focus on happiness if one is preoccupied with survival?

Well, it turns out that the natural world has a solution to this career problem, and it lies in one of the most unexpected places: the doodles I took in my AP biology notebook.

Many of you are probably familiar with bacteria from high school. These little organisms, despite being just one cell, are quite resourceful. In fact, one of the most common bacteria in the world, E. coli, has a clever way of finding food. It first senses the general direction of food, and then it rotates its flagella (whip-like structures) to shoot off more-or-less towards it ("running"). After doing this for a while, when it realizes that it’s not perfectly on target, it stops, lets its flagella flail about randomly, and re-orients itself toward the food ("tumbling"). It then starts running again, more accurately toward the food. It's through this iterative process of "tumble and run" that E. coli achieves its goal.

So then, how in the world does the behavior of bacteria relate to anyone's career? Consider this: E. coli has learned that simply driving forward at full speed isn't enough to reach a valuable destination. That's why it periodically tumbles – it pauses, assesses, and reorients itself. This secondary, evaluation mode helps it figure out if it’s on-track or off-track. In a parallel sense, I realized I could adopt a similar "two-mode" approach for my career, alternating between running (“execution mode”), and tumbling (“exploration mode”).

In execution mode, my goal is progress: I get up each day focused on moving one-step closer to my goal. In the past, when I was in this mode, I had a job, I was in school, or I otherwise had something that I was working toward. For example, when I was doing research for my Ph.D., I was 100% in execution mode – I had to publish roughly one first-author paper each year, and that meant careful planning. Sure, there was a lot of brainstorming, experimentation, and learning, but the key task each day was to bring me one step closer to that next paper, and hence, to that next chapter of my dissertation, and ultimately, to graduation. When I was at Google and Amazon, the goals were different and the tasks were different, but the approach was the same: move forward each day and make progress on the goal.

But there is another way to work, which I call exploration mode. In this mode, I get up each day not to make progress toward a goal, but rather, to open new doors and discover things that I hadn’t ever thought about. When I’m in exploration mode, I plan my life around attending local meetups on topics I’m interested in. I travel to attend conferences in areas of science or business that I find interesting. I reach out to old connections from LinkedIn to ask if they would like to grab coffee. I publish articles online. Once, I even flew across the country—from Boston to San Francisco—just to have lunch with an old colleague because I had heard he was building something interesting and I was curious. And because I don’t know exactly where I’m going when I’m in this mode, I optimize my life to be playful and spontaneous. I have faith that something will pop up that might surprise me.

See, the biggest value that you get out of exploring is that you give yourself the potential to uncover the “unknown unknowns”: the stuff you would find interesting that you didn’t ever know to look for. You set your life up for serendipity, not for accomplishment. You recognize that life is often governed more by chance and probability than by planned implementation.

Why is it important to have two modes? Well, it’s because when you’re in execution mode, there’s no time to look for new jobs or to “explore”. Even if you think you’re exploring, you’re still getting up each day with 90% of your attention focused on your immediate bill-paying work. But when you give yourself time to explore, you give yourself the gift of figuring out what you really want. You get off hamster-wheel of jumping from job-to-job focused on opportunity or comfort but knowing deep inside you that you are not doing what you really were meant to be. You step away from fear and step towards the life you really want.

Because it’s impossible for me to fully embrace both execution mode and exploration mode simultaneously, I prefer to alternate between these modes in life—much like how E. coli switches between running and tumbling. Practically, this means that if after several years of focused work, I feel I'm not progressing toward my life goals, I switch to exploration. This usually means leaving my job on good terms and spending several months (or up to a year) exploring what I want to do next. Academics call this a "sabbatical," traditionally a one-year break every seven years. But this approach isn't limited to academia—professionals in business can also benefit from this type of intentional pause and reflection.

Of course, the irony here is that conducting a successful exploration period in life takes serious planning and a financial cushion. The modern world—from rent, to food, to student loans, to healthcare costs, and more—makes this a really, really difficult thing to do. And the reality of financial constraints means that for many people, leaving a steady job just isn't a realistic option – many people just don’t have the savings or resources to quit their jobs cold-turkey. While cutting expenses and building-up one’s savings can be a good start, doing so isn’t often isn't feasible when managing debt, supporting a family, or dealing with essential ongoing costs.[1] I was able to make it work by stripping away all non-essentials, downgrading to a bare-bones apartment, and supplementing my income with occasional consulting projects, but I know that not everyone can.

Note that the benefits of switching to exploration mode may vary depending on one’s career path. Exploration might be less valuable for people who have invested heavily in specialized training (e.g., physicians or airline pilots), then for those in creative fields like filmmaking or entrepreneurship. And of course, if one is already satisfied and fulfilled in one’s current work, there may be no need to seek out new directions at all. However, for those feeling stuck or dissatisfied with their career trajectory, a pause can be worth fighting for. Such a deliberate break can help prevent not just burnout, but the more profound risk of spending years moving towards a destination that doesn't align with one’s true aspirations.

In the end, I’ve discovered that treating my career as a continuous, linear path of constant execution is exhausting and limiting. By always planning and doing, I was only seeing the options I already knew about. It was when I started alternating my career between periods of execution and exploration, similar to how E. coli adapts, that I was able to let go of rigid planning and become more open to unexpected opportunities. I was able to move from someone who was burned out and frustrated to someone who was getting up each day focused on what I wanted to work on - what I was most interested in doing. It’s funny - sometimes even the simplest of creatures in nature inspire solutions to our most complex problems.

[1] While I find that alternating serially between periods of execution and exploration in my career is especially effective, for people who don’t want to leave their current jobs, one approach is to carve out small pockets of time (e.g., one day a week, like a Saturday) where the sole focus is on exploration mode. The key is to intentionally separate the two modes of thought and avoid multitasking. It’s this separation (and the resulting ability to focus) that I have found most useful for opening my mind up to new possibilities.