Category Archives: Commentary

AI can remove typing. It cannot remove thinking.​

Students new to programming may wonder why they even need to learn to code any more. They can ask the reasonable question: “Why can’t we just use AI for all our programming?” It’s a fair question. The Genie is out of the bottle. We aren’t putting it back. It absolutely does have its uses. But it also can’t be trusted.

For a new programmer, the important skill is no longer remembering syntax, nor is it learning how to make an AI produce code. It’s learning how to solve problems in a systematic way that they understand well enough to trust.

When getting started with AI, use it to help think through the problem, break the work into manageable pieces, elaborate how the pieces interact and only then start to code.

There’s the old adage “Garbage in, garbage out”. If you aren’t able to articulate your problem in an AI-friendly way, we’re well into garbage out territory. The other part of the problem is whilst AI will certainly generate you some code, if you don’t understand what it’s giving back, you can’t trust it to be correct. If you carry on just accepting what it’s saying, it’s like going on a date in a foreign country and putting all your faith in your translation app. Sooner or later you’re unwittingly going to say something that will earn you a slap.

So, instead of just telling it what you want, try:

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One Year Down: Wisdom from a Former OPIG Newbie

With a new cohort of PhD students arriving imminently and my first year coming to a close, I am about to achieve an important milestone: I will no longer be the newest full-time OPIGlet in the group!

Naturally, this makes me extremely qualified to impart wisdom.

In all seriousness, the first year of a PhD has been a strange and wonderful adjustment. I have learned a lot about nanobodies, of course, but I have also learned quite a bit about how to actually do a PhD. So, for the incoming OPIGlets—or anyone else embarking on their first year—here are a few things I wish I had fully appreciated when I started.

Give some structure to all that freedom

One of the strangest things about starting a PhD is suddenly having an almost completely self-guided schedule. It is not something most of us have had to deal with before. Undergrad comes with lectures, tutorials, deadlines, and exams. Most jobs come with meetings, working hours, and someone telling you what needs to be done.

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Is the race for AGI a scam? Are the tech bros con men or visionaries?

To find out, we formally introduce the Stinkometer

In research, it can be tough even for experts to tell fact from fiction. This is especially true for more speculative fields like AI. It can feel like every model is “State-of-the-Art” and that AGI (whatever that means) will be “6 months away” for at least the next 6 months. How do we know we can trust these people?

What is a visionary? What is a con man?

Visionaries sell a fringe idea of the future, usually with strong self-belief and charisma. Con men are visionaries who don’t believe what they’re saying. It’s hard to tell the difference because we can’t read minds and can’t see the future.

I solved this by adapting the insightful “New Political Compass” from Harper O’Connor, an American political YouTuber. Harper found a similar problem. He’s active in local politics and has to decide who he should build alliances with to tackle certain issues. Interestingly, the “left-right” axis wasn’t a helpful guide. Most people he canvassed were reasonable but didn’t follow politics closely enough to have a robust ideology. He realised that someone’s psychology can be more important than their stated ideology.

Therefore, it was more useful to ask: Am I talking to a reasonable person? Here, I expand his framework into the Belief Compass (or the “Stinkometer”). We can answer our question by answering three simpler ones:

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Project Cybersyn: Bayesian Socialist Algocracy in 1970s Chile

Palantir and Peter Thiel have found themselves in the headlines a lot this year, very often in the same sentence as the word “surveillance”. The idea behind Palantir is a simple one: give one company, or one control room, a real-time algorithmic view of how a government or an economy is actually functioning, and let them help run things better.

That idea is not new. In 1971, Salvador Allende’s socialist government in Chile built almost exactly that system, out of telex machines and a room full of fibreglass chairs, on practically no budget. There’s evidence it worked, until a coup destroyed it before anyone found out whether it would have worked at scale. This was known as “Project Cybersyn”.

A modern reconstruction of Project Cybersyn’s hexagonal operations room, with seven chairs arranged in a ring and display screens on the walls.
The space-age themed Opsroom, where decisions were made in response to Bayesian forecasting.
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Work Hard, Play Hard: Balancing Sport and Studies at Oxford

I have always been involved in sport, having played football for Bristol Rovers and at county level when I was younger. That continued after arriving at Oxford, although in several different forms. In my first year, I ran the London Marathon for Alzheimer’s Research UK. In my second, I rowed with my college, and more recently, I took up boxing, competing against Cambridge to pick up the illusive ‘Blue’ and accompanying blazer.

Trying to pursue sport alongside a PhD has not always been easy, but it has been one of the most enjoyable parts of my time at Oxford. It has helped me step away from work, manage stress and maintain a competitive outlet outside academia. At the same time, Oxford sport can become extremely demanding, so finding a balance is sometimes challenging!

There is always more work to do

One of the difficulties of doing a PhD is that the work never feels completely finished. There is always another paper to read, experiment to run, result to analyse or paragraph to improve. If you wait until everything is done before exercising, you may never leave your desk (which is sometimes the case).

Sport creates boundaries that research rarely creates for itself. Training begins at a fixed time, teammates expect you to be there and competitions cannot be rearranged around your workload. I have often gone to training feeling that I should have stayed at my desk, only to return less stressed and able to concentrate more clearly. Taking a break can feel unproductive, but spending another hour staring at the same problem is not always the best way to solve it.

What sport adds to academic life

Sport develops many of the same qualities that academic work demands. Training consistently requires discipline, progress is rarely linear and poor performances teach you to keep going when things are not working as you hoped. It also provides a competitive outlet and a clearer sense of progress than research often does, which can be valuable when your PhD feels slow or uncertain.

Just as importantly, sport introduces you to people beyond your department and gives you something meaningful outside your work. During a PhD, it is easy to let academic progress determine how you feel about yourself. Having another community and another part of your life that matters helps keep a failed experiment or unproductive week in perspective.

Of course, sport does not need to be justified entirely by the lessons it teaches. At the end of the day, it is something I enjoy doing.

When play becomes more work

Although having another ambitious goal alongside your studies can also be extremely rewarding, the difficulty is that sport at Oxford can quickly become a serious commitment. Chasing a varsity place can demand a great deal of training, recovery and focus. Sometimes sport may genuinely matter more than your studies (in the lead-up to a competition, for instance), and that is fine, provided it is a deliberate choice and that things are still kept in perspective, rather than something you have been swept into without considering the trade-offs.

Finding the balance

Balance does not mean dividing every day equally between work and sport, nor does it mean always putting your studies first. There will be periods when training deserves more attention and others when academic work must take priority.

Moderation does not mean lacking ambition. You can train seriously, chase difficult goals and care about winning while still maintaining perspective. Equally, working hard does not require allowing your PhD to consume every other part of your life.

Sport has made my time at Oxford busier and occasionally harder to manage, but it has also made it far more enjoyable. It has given me friendships, challenges and experiences that I would never have found through academic work alone. It may even have made me better at my PhD, but that is not the only reason it was worth doing.

Peering Inside the Black Box: A Beginner’s Introduction to Mechanistic Interpretability

Over the last few years, large language models (LLMs) have gone from being curiosities tucked away in research labs to something most of us interact with on a daily basis; whether for drafting emails, debugging code, or simply pondering the meaning of life at 2am. And yet, for all our reliance on these systems, a rather inconvenient truth lingers in the background: nobody, not even the people who built them, can fully explain what is going on inside.

This is where mechanistic interpretability comes in.

In essence, mechanistic interpretability is the approach of explaining complex machine learning systems through the behaviour of their functional units (Kästner and Crook, 2024) by reverse-engineering them into their more elementary computations (Rai et al., 2025). The aim is not simply to know that a model gives the right answer, but to pull apart the underlying machinery and uncover the causal relationships between input and output. Think of it as neuroscience for neural networks, except we can read every neuron at any moment, rewind, replay, and intervene mid-thought.

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The Open Immune Window: Notes on Sweaty Workouts and Vanishing Immune Cells

Here is a question for you: is an intense, sweaty workout in the gym building up your immune health, or is it just opening a window of opportunity for a pathogen to ruin your week? To understand this, we first have to look at energy. The immune system is incredibly energy-hungry, constantly patrolling and repairing the body. When you exercise hard, your body is forced into a rapid game of resource allocation, diverting precious energy away from baseline functions to fuel your contracting muscles.

This brings us to a rather scary observation in sports science that I stumbled on one day reading random headlines. If you draw blood one to two hours after a hard run or heavy exertion, your immune cell count (specifically lymphocytes) absolutely plummets. Apparently for decades, scientists looked at this massive drop in the blood and concluded that our immune system temporarily crashed after exercise, leaving an “open window” of 3 to 72 hours where we were highly vulnerable to infections. Which leads us back to the main question – is a hard workout actually making you sick?

Thankfully, no. It turns out those missing immune cells didn’t just die off. Driven by the acute spike in adrenaline from your workout, those cells rapidly exit your bloodstream and migrate directly into peripheral tissues, specifically mucosal barriers like your lungs and gut. Think about it: during a hard workout, you are hyperventilating and exposing your airway to massive amounts of external air. Your body isn’t suppressing its defenses; it’s actively deploying its best troops exactly where a pathogen is most likely to enter. It is a state of heightened immune surveillance, not suppression.

So why do athletes often get the sniffles after a big race? Often, it is just non-infectious airway inflammation from heavy breathing, combined with the psychological stress and lack of sleep that accompany big events. Your workout actually acts as a natural immune adjuvant, making you more resilient. If you want to dive deeper into this topic, I highly recommend checking out the paper Debunking the Myth of Exercise-Induced Immune Suppression by Campbell and Turner (Frontiers in Immunology, 2018).

What I wish I knew before applying and moving to Oxford from the US

The first time I ever visited the UK was when I moved to Oxford for my PhD (or DPhil in Oxford speak). I was nervous, excited, and thought I could assimilate easily after growing up watching Sherlock, Midsomer Murders, and Doc Martin. After all, my native language is English, how different really is the UK? Oh, how wrong I was.

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Curiosity might not kill the cat

Unlike most members of OPIG, I don’t work on small molecules, antibodies, or protein structure; I use hypergraph representations of protein complexes to predict gene essentiality and drug targets. I have also had an unconventional route to get here, and on the way, discovered my love for learning and research.

Friends and family had noticed I jumped around with my interests, so much so that when we used to meet up, they took great delight in teasing me about what my current adventure was – ‘you don’t settle do you!’, ‘when are you going to find what you’re looking for?’, ‘why can’t you just stick to something’. Looking back, there was a pattern, I just couldn’t see it yet.

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Pitfalls of AI-Generated Reviews: Case Study of a Frontiers in Microbiology Review on Anti-Influenza A bnAbs

In the last five or so years, large language models (LLMs) have transformed from a novel regurgitator of haphazardly stitched together sentences to an almost ‘human’ personality standing by our side as we tackle life. Whilst the perceived humanity of these models is the topic for perhaps a future blogpost, it is almost undeniable to understate the impact of LLMs in our daily lives. Do you need someone to proofread your essay you’ve spent hours drafting? GPT (or one of its many counterparts) has you covered. Need help drafting an email from scratch? No problem. Want to write and/or heavily edit an entire academic article which would typically require days, if not weeks, of research? Surely just needs a push of a button… right?

Despite tremendous advances in LLMs, key issues mean they are not yet a fully dependable addition to our writing endeavours. They are known to fail when asked to generate new content with only a basic prompt. Some of these failures have made headlines 1. Some of the scariest instances are those of hallucinated information 2–4 . This refers to the phenomenon where AI tools generate convincing information which is factually inaccurate or simply fabricated 2 . In Belgium, the Ghent university rector came under fire for citing quotes, supposedly from influential thinkers, which were later found to be AI-hallucinations 1.
Whilst there are numerous examples of the poorly cited and often AI-hallucinated papers falling through the cracks of the peer-review process, today we focus on a Frontiers in Microbiology review titled ‘Broadly neutralizing monoclonal antibodies against influenza A viruses: current insights and future directions’ 5. This paper attempts to provide an overview of the current landscape of monoclonal antibodies (mAbs) which are being developed to confer protection against influenza A, highlighting ‘technological advances, clinical performance, and scalability’. This paper contains many of the hallmarks of text that has been created or edited with generative AI, despite the generative AI statement stating ‘The author(s) declared that Generative AI was not used in the creation of this manuscript.’

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