Skip to content

career

  • in career
  • 4 min read

2025 Wrapped

The Experiment Continues

Last year I wrote about my pivot to consulting - an experiment with two constraints: transactional contracts (no equity), and working at a slower pace. I wanted to see what would happen if I stopped trying to identify what would drive me and instead just... did good work.

A year later, the experiment has sharpened into something more specific. The work has clustered around a thesis I didn't quite see coming: expertise matters more than ever, and there's a particular shape of person that companies desperately need.

The FDE Moment

This year I spent a lot of time helping companies hire and build teams. Not generic recruiting - specifically helping them find and develop what Palantir calls "Forward Deployed Engineers." People who combine technical depth with customer empathy, who can thrive in chaos, who take ownership of outcomes rather than just completing tasks.

The most intensive engagement was with an early stage startup, where I helped build their founding technical team from scratch. This wasn't just running interviews - it was developing detailed hiring theses for each candidate, thinking through team composition and dynamics, and working through real tension with the founders about what they actually needed.

We evaluated candidates on a deceptively simple framework: are they smart, and can they win? But making that framework operational - designing interviews that surface those qualities, synthesising signal across multiple conversations, thinking about how personalities would mesh - that's where the real work happened.

I also worked with a services company on designing their whole FDE strategy. Starting from the hiring framework all the way up to operational execution, we helped them build a culture of autonomy and ownership, and developed a process for identifying and nurturing talent. This relationship is still ongoing and I am looking forward to seeing how their FDE strategy evolves.

The insight I keep coming back to: the FDE archetype is having a moment. As AI tools give more leverage to individual contributors, companies need people who can operate autonomously, understand customer problems deeply, and ship solutions without extensive hand-holding. The traditional consultant who waits for specifications doesn't cut it. Neither does the product engineer who wants to build in isolation.

Building Palantir Businesses

A related thread: helping companies navigate the Palantir ecosystem specifically.

There's a growing ecosystem of companies building on Palantir Foundry, and they face a common challenge: how do you hire, structure, and deliver when the platform itself provides so much leverage? The playbooks from traditional consulting don't quite fit. The playbooks from product companies don't either.

My 7 years as an FDE at Palantir, combined with the hiring work, puts me in an interesting position to help these companies figure it out. It's a niche, but it's a niche where I can add real value.

Testing the Intelligence Explosion

The most unexpected project of the year had nothing to do with consulting. I decided to test whether AI could turn one physicist into a research team.

I have a PhD in plasma physics, but I haven't done active research in a decade. Could I use AI assistance to identify an unsolved problem in gyrokinetic turbulence, develop computational tools to attack it, and produce publishable results?

The answer, it turns out, is yes - with caveats.

I rebuilt GANDALF, my PhD-era gyrokinetics code, from scratch in JAX. Claude wrote 100% of the code - roughly 3,000 lines that I never read. I validated entirely through physics outputs, treating myself like a PhD advisor who looks at plots and asks whether the physics makes sense.

The paper is now on arxiv. What took me 6-7 months as a PhD student took 30 days as a side project. Total cost: ~$550 in API credits.

AI amplifies expertise rather than replacing it.

Claude fabricated benchmark timings, claiming simulations ran on "Princeton's Stellar cluster" when everything actually ran on my MacBook. It invented development timelines, inflating one month to three years. It made up GPU runtimes for comparisons that never happened. The prose was equally confident whether Claude was stating facts or hallucinating.

The physics errors were worse. Wrong definitions. Incorrect cascade descriptions. Misinterpreted orderings. These were invisible to anyone without plasma physics training. I caught them because I have studied it. Without domain expertise, the paper would have been embarrassing.

The productivity gain is real. The AI handles implementation at superhuman speed. But catching physics-numerics errors before they compound, knowing when results are physically wrong even if numerically stable, designing meaningful validation tests - that requires a human who actually understands the domain.

I wrote about this in detail in a five-part series on my blog. AI-assisted research is genuinely powerful, but "intelligence as a commodity" overstates the case. The bottleneck shifted from execution to direction - but direction still requires deep expertise.

The Thread

Looking back at the year, there's a theme I didn't plan but keep encountering: expertise matters more than ever.

In hiring: companies need people who understand specific domains deeply, not generic talent that can be pointed at any problem.

In the Palantir ecosystem: success requires people who genuinely understand Foundry and how to deploy it, not just generic services firms.

In AI-assisted research: Claude can write thousands of lines of code, but someone still needs to catch the physics errors.

The intelligence explosion hypothesis is partially true. AI is a massive force multiplier. But it amplifies expertise rather than replacing it. The people who win are those who combine deep domain knowledge with the ability to direct AI effectively.

Looking Forward

The consultancy experiment continues. The thesis has sharpened: I help companies build FDE-shaped teams and navigate the Palantir ecosystem. The physics project proved I can still do research when motivated.

For 2026, I am interested in seeing how the AI-assisted research can generalise to other domains. Specifically thinking through effective AI adoption strategies. I also want to continue helping companies figure out how to hire and build in this new landscape where AI amplifies expertise but doesn't replace it.

We'll see how it goes.

  • in career
  • 1 min read

Reflection

Career decisions are hard.

I have pivoted my career a few times now. First time was about a decade ago when I switched from academia to industry and from physics to tech. It was a difficult decision. I was leaving behind something I had obsessed over for 17 years for the complete unknown. In hindsight it seems like such a crazy decision -- I didn't know much about Palantir. I really enjoyed all my interviews (I remember the questions and the interviewers a decade since) - this was the main positive signal. And the culture (as much as I could glean from the interviews) had a lot of similarities with academia, it felt familiar.

Boy was I fortunate. The 7 years I spent at Palantir defined the professional me. It taught me so many things -- technical, organisational, traits in people one should value and much more. It gave me the opportunity to wear many hats and grow stochastically.

Palantir gave me the opportunity and the courage to try new things. Leaving after 7 years was bittersweet - it was time for something new, but I missed my home.

The last 15-16 months have been very different. I have been trying to find the thing that keeps me engaged, makes me obsessed. I wouldn't say I have found it yet, but I am working on it.

  • in career
  • 4 min read

2024 Wrapped

Pivot

This year was a huge pivot for me. At the beginning of the year I was working on an AI startup founded by two of my friends. It was my first introduction to the world of AI, and a great learning experience. It developed my interest in applied AI - specifically, that area in between research and practical applications. Keeping up with the latest research, and then figuring out how to apply it to real-world problems.

However, by April I realised I needed a break. My partner pointed out that I had not taken a proper break since starting grad school, back in August 2008 And since then I had only worked at intense places - a PhD, Palantir, a crypto startup, and then the AI startup.

I took about 6 weeks off to reflect on what I wanted to do next.

Reflection and Experiment

One key realisation was that identifying what would drive me was not easy, especially looking forward. The last time I felt that level of narrow focus was when I chose to do my PhD - there was no doubt whatsoever in my mind that I wanted to do it. In fact there were many people who tried to talk me out of it, but I was convinced. And since finishing my PhD, I have oscillated between being driven by the impact, the people I work with, the day-to-day work, and the learning opportunities. At different times, each of these has been the primary driver.

Given this, I decided to do an experiment with two key constraints:

  1. I would explicitly not try to identify what would drive me, but instead set up transactional contracts. Tactically, this meant I would not take equity in any company I worked with. This would force me to be thoughtful about the time I spent on a project.
  2. I would work at a slower pace - 4 days a week.

Early Results

Initially I was worried that I would not be able to find enough work. But I was pleasantly surprised. I managed to land a couple of consulting gigs - one with a boutique consulting firm, and another with a startup. The consulting firm has been a great experience. I have been working with former Palantirians, and there's almost a sense of homecoming. The startup scratched the AI itch, giving me a chance to work on a real-world problem using AI.

The first couple of months went quickly - a honeymoon period of sorts. But one of the gigs ended, and I realised that I needed to be more proactive about finding work. As someone who has no idea about sales, this was a challenge. Honestly, I freaked out a bit, but then I found my own way of generating leads.

Sales

I started writing more. I wrote about my experiences at Palantir, especially the hiring process. This resonated with a lot of people, and I started getting inbound leads. Interestingly, not all were hiring related. I started working with another AI startup, a crypto startup, and a couple of hiring related projects.

So far this is the only way I have tried to generate leads, and it has worked well. I still don't feel confident that I will consistently manage to bring in business, but I will cross that bridge when I get there.

Hiring

The hiring projects have been the most non-trivial. I have worked with a few companies on their hiring, the most notable being Comand AI. The level of trust they have placed in me has been humbling. Hiring is one of the most high-stakes things an early stage startup does, and I am grateful for the opportunity to help them.

It has also pushed me out of my comfort zone. Yes, I learnt a lot about hiring at Palantir, but fully owning the end to end outcome is a different beast. Additionally, I now also have to articulate my approach and be methodical about it - forcing me to rely less on instinct.

Highlights and Conclusion

Over the course of this year, I have worked on the following tech:

  • Palantir Foundry
  • Python: to build LLM-based applications for the AI startups
  • Golang: to build tools in the crypto space for the crypto startup

And I have learnt about:

  • Golang concurrency
  • AI tools: Claude, Zed, Gemini, etc.
  • RAG architectures
  • Being a freelancer
  • Designing a hiring process

I am happy with how the year has gone. I have managed to find work, and I have enjoyed the work I have done. Interestingly, even though the experiment started off as transactional, I have found myself getting attached to the work. I have been invested in the outcomes, and I have cared about the people I have worked with. This doesn't come as a surprise - I have always needed to care about the work I do, and/or the people I work with. But it's interesting to see how this has played out with the transactional initial conditions.

I am looking forward to 2025. I am excited about the work I have lined up - specifically in the AI space, as well as the hiring work. The AI projects should give me the opportunity to learn and grow as an engineer, and the hiring work will give me the chance to learn how to build a business. I am not going to force my hand either way - we'll see how it goes.

  • in career
  • 3 min read

Stochastic Growth Trajectory: Good or Bad?

We often have preconceived notions about the meaning of career growth. Typically, it's assumed to mean climbing the corporate ladder, moving into higher positions, and often transitioning into management. However, this linear path isn't the only way to grow professionally. My personal journey led me to experience a different approach: the stochastic growth trajectory.

Increasingly Confused

Throughout my career I have been nothing if not confused. In fact, the confusion has gone up over time. The last time I was confident of anything was when I was 13 years old - when I was convinced I wanted to do Physics. It was towards the end of grad school when I started considering leaving academia. But at that point I didn't know if anyone would hire me, and in what role. And I was really surprised to get hired by Palantir. This unexpected turn would shape my understanding of career growth in ways I couldn't have anticipated.

Palantir's approach to Growth

I spent 7 years at Palantir, and I can confidently say that I would not have stayed for that long if Palantir did not have the healthy approach to growth that it has. I got to explore a lot of different things in a relatively short timeframe. But there was more to it than just accelerated growth.

At Palantir it is expected that you own your growth. You are expected to be self-aware, critical, and intentional about how you grow. This is not an easy thing to do, especially early in your career. But it is what allows for people to grow into positions where they can have an outsized impact. It is also what keeps employees engaged and driven.

For me personally, this meant I was able to be an SRE, a Tech Lead, a backend developer, a product manager, a sales engineer, a data scientist, a data engineer, and a people manager during my 7 years at Palantir. This is what allowed me to go from managing a team of 20-25 people to being an IC dev on a backend team because I wanted to invest in my technical skills. Even when I moved from management to an IC role, I was still growing.

Flip Side

There is however a flip side to this.

Firstly, the responsibility of figuring out what growth means is completely on you. You have to figure out not just what growth means, but also how to grow in those areas. This can be quite hard. I was fortunate to have found the right leads, mentors and peers to help me.

Secondly, the micro-decisions you might make while trying to figure out what to work on next might not necessarily lead you to your macro-goals. To be honest, even today I am still figuring out what my macro-goals are. And the increasing awareness of not-knowing what I want to do gives me pause in figuring out what project to take on next.

Conclusion

I don't really have a conclusion for this post. I don't know if a stochastic growth trajectory is good or bad. Looking back I found the that "own your growth" worked for me, maybe. But it is not for everyone, and I don't even know if it worked for me or not.

Perhaps one way to think about it is to not worry much about macro-goals, especially if you don't have clarity about what they are. Instead focus on learning and growing in the near-term, and having faith that whatever you end up working on, there is a high chance that you will learn something new, and grow.

If you are curious, genuine, and keen to learn, you will land on your feet.

Is a PhD worth it?

Personally, it was worth it for me. I was fortunate to work with amazing people like Bill Dorland, Alex Schekochihin and many more. And I got to work on fun problems.

But, if you are asking this question, it probably isn't worth it for you. You should only do a PhD if you are so hell bent on doing one that no one could convince you otherwise. There were many who tried to tell me it was a bad idea to do a PhD, but I was not to be convinced.

Don't get me wrong, a PhD can absolutely be worth it. You get to work on something that you (hopefully) love. You get to push the boundaries of human knowledge. It is literally one of the most impactful things to do.

It also grows you as a person - you become better at learning new things, at systematically breaking down a problem and chipping at it till it becomes manageable. You learn to communicate your work to a wider audience. You develop a confidence in yourself that you can take on gnarly, seemingly impossible problems and just figure it out.

However, a PhD is a long and painful journey. It can be quite lonely. So if you have even the smallest of doubts about whether you should do one or not, then don't. Especially if you think it might be a stepping stone in a career outside academia. More than likely it is one, but that is not motivation enough to actually stick with it during.

Having said that I really enjoyed mine, so if you are that focused on it then go for it! It's also not a one way street. People do go back to doing a PhD after a career in industry - my good friend satej is a great example! After a successful career at Palantir he decided to spend some time in academia, and seems to be enjoying it.

If you are someone who is trying to decide between a PhD and a career in the industry, hope this short writeup helped. If you have any questions or want to discuss further, do get in touch - I am happy to chat.