Deep Dive: Why Hedge Funds Are Splitting Into Two Very Different Worlds

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Today we’re talking all things hedge funds. More specifically, why hedge funds are splitting into two very different worlds.

I like this one a lot.

Because when most students think about hedge funds, they picture one thing: a brilliant trader sitting at a Bloomberg terminal, making big calls on markets, and generating huge returns.

That image isn't entirely wrong.

But it's describing only half of the industry.

The reality in 2026 is that the hedge fund world has fractured into two very distinct camps. One is driven by human judgement, conviction, and deep research. The other is driven by algorithms, data, and computing power.

And these two worlds are moving further apart every year.

Most students have no idea this split is happening, let alone what it means for careers, compensation, or what these firms are actually looking for.

By the end of today's issue, you should understand:

Let's get into it.

Before getting into the split, it helps to understand what hedge funds actually are.

At a basic level, a hedge fund is an investment fund that pools capital from sophisticated investors — typically institutions like pension funds, endowments, and sovereign wealth funds, or very high net worth individuals — and deploys it with the goal of generating returns regardless of market conditions.

That last part matters.

Unlike a traditional asset manager, which might simply aim to track or beat a benchmark index, hedge funds are supposed to make money in both rising and falling markets.

That's the theory, anyway.

Hedge funds charge fees that reflect this ambition. The classic structure is known as "2 and 20" — a 2% annual management fee on assets under management, plus 20% of any profits generated.

In practice, fee structures have compressed significantly over the years as competition has increased. But the basic principle remains: hedge funds are paid to generate absolute returns, not just to keep pace with a benchmark.

The strategies they use to do this vary enormously — from betting on macroeconomic trends, to exploiting tiny price differences between securities, to taking long or short positions in individual companies.

And that variety is exactly where the split begins.

If you zoom out and look at the hedge fund industry today, you'll notice something interesting.

Funds are increasingly falling into one of two categories.

Discretionary funds are run by portfolio managers who make investment decisions based on their own analysis, judgement, and conviction. They read markets, form views, develop theses, and take positions accordingly.

Quantitative funds — often called quant funds — are run primarily by algorithms and mathematical models. Human researchers build the models, but the day-to-day investment decisions are made by machines.

These two types of fund approach markets in fundamentally different ways.

One relies on human intelligence.

The other relies on computational power.

And right now, both are competing for the same pool of capital.

A discretionary fund is the more traditional version of a hedge fund.

At its core, it's built around the idea that skilled investors — through superior research, insight, and judgement — can identify opportunities that markets have mispriced.

A discretionary portfolio manager might:

The key word is conviction.

Discretionary investing is about having a view and being willing to back it.

Famous examples of discretionary hedge funds include names like Bridgewater Associates (in its macro strategy) and Pershing Square. Legendary investors like George Soros and Stanley Druckenmiller built their reputations on this style of investing.

When it works, it can generate extraordinary returns. A single well-timed macro trade can make an entire year.

But it also comes with significant risk.

Because discretionary investing is ultimately a human endeavour — and humans are wrong regularly.

Being wrong in discretionary investing has real consequences. Positions lose money. Track records take a hit. Investors withdraw capital. And that pressure never fully goes away.

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Quantitative funds operate on an entirely different logic.

Instead of forming views based on judgement and research, quant funds build mathematical models designed to identify statistical patterns in market data.

These models might look for:

The key insight behind quant investing is this:

Markets are not perfectly efficient. Patterns exist. And if you can find those patterns before others do, and execute trades fast enough to exploit them, you can generate returns consistently.

The most famous example of this approach is Renaissance Technologies, whose Medallion Fund is widely considered the most successful investment fund in history.

Renaissance doesn't employ economists or traditional analysts. It hires mathematicians, physicists, and computer scientists. The fund is built on decades of proprietary models and data processing.

Other major quant funds include firms like Two Sigma, D.E. Shaw, and Citadel's quantitative strategies.

What makes quant funds so powerful is their scalability and their speed.

A human investor can only analyse so many companies. An algorithm can process millions of data points in milliseconds.

And that advantage has been growing.

The rise of quant funds isn't just about clever mathematics.

It's about infrastructure.

Three things have converged to give quant funds an enormous structural advantage:

Computing power has become dramatically cheaper. Processing vast datasets that would have been impossible twenty years ago is now routine.

Data availability has exploded. Quant funds today analyse satellite data, credit card transaction flows, web traffic patterns, earnings call transcripts, and hundreds of alternative datasets that didn't exist a decade ago.

Machine learning has improved model sophistication. Quant models today are far more complex than the linear statistical models of the past. They can detect patterns that are genuinely invisible to human analysts.

This combination has driven significant capital flows toward quant funds.

Over the past decade, assets managed by quantitative strategies have grown substantially while many traditional discretionary funds have faced pressure.

The data supports this: quant funds have attracted a larger share of institutional allocations because their returns have been more consistent and less correlated with broader markets.

In other words: investors love what quant funds can do.

And that's created a structural tailwind that shows no sign of reversing.

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Given everything above, you might assume discretionary funds are a dying breed.

They're not.

And understanding why is important.

First, not all markets are equally efficient.

Quant strategies tend to work best in liquid, data-rich markets — major equity indices, foreign exchange, and futures markets — where there is enough historical data to build robust models.

But in less efficient markets — certain emerging markets, distressed credit, complex special situations — human judgement and deep fundamental research can still generate a genuine edge.

Second, quant models have blind spots.

Models are built on historical data. And historical data doesn't always predict the future — particularly during unusual events.

In genuinely novel situations — a global pandemic, a sudden geopolitical shock, an unexpected policy change — quantitative models can struggle because they have no relevant historical reference point.

Experienced discretionary managers can sometimes navigate these moments more effectively precisely because they rely on judgement rather than pattern recognition.

Third, the best discretionary funds have adapted.

The binary of "pure quant" versus "pure discretionary" is increasingly blurred.

Many discretionary funds now use quantitative tools to assist their research. Portfolio managers might use data science to screen for opportunities before applying fundamental analysis. Risk management is often heavily quantitative even inside traditional discretionary shops.

The funds that are struggling are the ones that didn't evolve.

The funds that are thriving are the ones that figured out how to blend both approaches.

Whenever AI comes up in finance, it tends to generate either extreme excitement or extreme scepticism.

The reality, as usual, is more nuanced.

For quant funds, AI is an extension of what they were already doing.

Quantitative funds have used machine learning techniques for years. What has changed recently is the scale and sophistication of the models available.

Large language models are now being used to analyse earnings calls, news flow, regulatory filings, and management commentary in ways that were previously impossible to automate. Natural language processing tools can extract signals from vast amounts of text far faster than any human analyst.

For quant funds, AI is primarily a tools upgrade. It makes their existing processes faster and more powerful.

For discretionary funds, AI is more of a supplement than a replacement.

A portfolio manager building an investment thesis still needs to understand a business, assess management quality, and form a view about competitive dynamics. Those judgements remain fundamentally human.

But AI tools can help that manager process background research faster, screen for relevant data, and stress-test assumptions more rigorously.

The honest take is this:

AI is not making discretionary investors obsolete. But it is raising the baseline of what analytical work looks like. Investors who ignore it will increasingly look slow compared to those who embrace it.

The area where AI is having the most disruptive effect is not at the portfolio manager level — it's at the analyst level.

A lot of the research work that junior analysts traditionally did — screening, data gathering, summary writing — is being automated or significantly accelerated. That changes what funds need from their junior hires, which has direct implications for anyone thinking about a career in this space.

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This is where it gets really practical.

The split between quant and discretionary funds creates two very different career paths — and understanding which one fits you is becoming increasingly important.

If you want to work at a quant fund:

The skills profile has shifted dramatically. The most competitive candidates typically have strong backgrounds in mathematics, statistics, computer science, or physics. Coding skills — particularly Python — are increasingly expected even for research roles. Many of the top quant funds recruit heavily from top engineering and science programmes rather than traditional finance degrees.

This doesn't mean you need a PhD to get in. But it does mean that demonstrating technical skills alongside financial understanding is increasingly the expectation.

If you want to work at a discretionary fund:

The traditional route still exists — strong academic performance, intellectual curiosity, the ability to build investment theses and communicate them clearly. But there's an important shift happening here too.

Discretionary funds increasingly expect their analysts to be comfortable with data tools and to understand quantitative concepts even if they aren't building models themselves.

The idea that discretionary investing is a purely qualitative skill is becoming outdated.

The candidates who stand out in 2026 are those who can think like investors and engage with data rigorously.

The broader point:

Regardless of which path you're targeting, demonstrating awareness of this structural shift — and what it means for how these firms operate — puts you in a very strong position in interviews.

Most candidates talk about hedge funds as if they're one homogeneous thing.

The candidates who understand the industry well enough to discuss this split are the ones who immediately sound like they actually know what they're talking about.

If an interviewer asks:

A strong answer could be:

The hedge fund industry is one of the most intellectually interesting parts of finance.

But it's also one of the most misunderstood — particularly by students who are trying to break into it.

Most people picture hedge funds as a single thing. In reality, the industry is splitting into two worlds that operate on completely different principles, attract different types of people, and require different skills.

Quantitative funds are scaling. They are increasingly data-driven, technically sophisticated, and structurally advantaged in the kinds of markets where pattern recognition beats conviction.

Discretionary funds are evolving. The ones that are thriving are the ones that figured out how to use quantitative tools without abandoning the judgement-driven approach that defines great fundamental investing.

And AI sits in the middle of both — accelerating quant capabilities, supplementing discretionary research, and gradually changing what analytical work looks like at the junior level.

This is the kind of topic that separates candidates who have genuinely engaged with how the industry works from those who have just memorised surface-level facts about what hedge funds are.

If you can talk about this clearly in an interview — connecting the structural shift, the different skill sets required, and the implications of AI in a grounded way — you'll immediately stand out.

That's commercial awareness in practice.

Not memorising headlines.

Understanding what's actually happening underneath them.

That’s all for this deep dive. I’ll see you in the next commercial awareness update later this week.

Peace!

Afzal