The VC Analyst Role is Being Replaced by Software

The VC analyst role, as it has existed for the last 20 years, is quietly dying. Most of the industry has not noticed yet.

I am not saying humans will stop working in venture capital. I am saying the specific job description that has defined "analyst" for two decades (maintain market maps, build memos, track competitive landscapes, cold-call founders, summarize news into internal digests) is being absorbed into software at a rate that is faster than most firms are willing to admit publicly.

I say this as someone who has built intelligence infrastructure used by firms that employ analysts, and as someone who has watched what happens to the analyst workflow when the infrastructure is deployed. The honest version of the story is that most of what an associate-level analyst does for their first two years is now better done by software. That changes the role, the economics, and eventually the career path.

This is my take on what is actually happening, what it means, and what the new analyst job should become.

What Analysts Actually Do

To understand what is being replaced, it is worth being specific about what the current analyst workflow looks like inside most VC firms.

A typical first-year associate at an early-stage fund spends their time across roughly these categories:

• Market mapping (20 to 30%): Building structured views of specific sectors. Who are the players, what is the competitive landscape, what are the funding patterns.
• Deal screening (20 to 25%): Processing inbound decks, doing first-pass diligence, deciding which deals merit a partner's time.
• Company research (15 to 20%): Deep-dive research on specific companies the firm is evaluating. Public data gathering, competitive context, team background.
• Memo writing (10 to 15%): Writing the IC memos that document investment recommendations and ultimately live in the firm's knowledge base.
• Internal updates (10 to 15%): Weekly digests, pipeline updates, portfolio tracking, keeping the partnership informed about what is happening.
• Founder interaction (10 to 15%): Sourcing calls, first-meeting screens, relationship building with founders the fund is not yet ready to fund.

The honest assessment: of those six categories, four are almost entirely automatable with current technology, and the remaining two are changing in nature.

What Software Now Does Better

Let me be specific about where software has clearly surpassed the first-year analyst workflow, and why.

Market Mapping

Static market maps are dead. A market map built by an analyst takes two to four weeks, is outdated within a month of publication, and reflects the analyst's incomplete view of the sector.

A continuous, AI-powered market map refreshes as new companies appear, as funding rounds close, as competitive positions shift. It covers more companies than an analyst would ever find manually and maintains coverage indefinitely without additional labor. It also scales across sectors in a way that analyst-built maps never can.

This is not a marginal improvement. It is a category difference. The question is not "which is better," it is "why is anyone still doing this manually."

Deal Screening

First-pass screening of inbound decks is tedious, repetitive, and high-volume. It is exactly the kind of work that modern AI handles well: extract key facts, compare against thesis parameters, score for fit, flag the ones worth partner attention.

A junior analyst can screen maybe 30 to 50 decks a week at reasonable quality. Software can screen the same number in an hour, and do it more consistently because it does not have a bad Tuesday or get distracted by a particularly slick deck that does not actually fit the thesis.

Company Research

Structured research on a specific company (founding team background, funding history, competitive context, hiring patterns, product history) used to take an analyst a full day of browsing across 10 to 15 sources. Now the same output is generated in minutes from a platform that has continuously indexed those sources.

The delta is enormous. What used to be a day's work for an individual company now happens at the speed of a search query. That capacity unlocks an entirely different workflow.

Memo Writing

This one is more contested, but the reality is that first drafts of investment memos are now routinely generated by software, often good enough that the human work becomes editing rather than writing. The synthesis of diligence findings into a structured narrative is a pattern-matching task, and pattern-matching tasks are where modern AI excels.

The human contribution to a memo is the judgment embedded in the conclusion, the contextual knowledge of the partnership, and the specific insight that comes from having actually met the founders. That is real and still valuable. The paragraphs that describe market size and competitive landscape are not.

Internal Updates

Weekly digests of sector activity, portfolio company news, competitive movement, and pipeline status are now automatable end to end. The analyst time spent gathering and formatting this information is genuinely gone, not reduced.

What Software Still Cannot Do

I want to be fair. There are parts of the analyst role that software does not do well, and some that software should not do at all.

Judgment Calls

Deciding whether a specific founder is exceptional, whether a specific market is real, whether a specific competitive threat matters, is not a pattern-matching problem in any useful sense. These are judgment calls that require human conviction and accountability.

Relationship Work

Building trust with founders, maintaining long-term relationships with portfolio companies and co-investors, reading the emotional state of a founder in a hard moment, are all deeply human activities. Software does not replace them and should not try to.

Contrarian Conviction

The best investment outcomes often come from contrarian calls: seeing what the consensus misses. Software is structurally biased toward consensus because it is trained on consensus data. Genuine contrarian conviction comes from humans who can look at the same data and reach different conclusions.

Firm-Specific Context

What fits this particular fund, at this particular vintage, given the current portfolio, the LP base, and the stated thesis, is local knowledge that software cannot fully replicate. Humans inside the firm know things that no external tool captures.

What This Means for Firms

If the analyst job is changing, so is the shape of the firm.

The Analyst-to-Partner Ratio Will Compress

Most firms have historically employed multiple analysts per partner, based on the math of how much research work was needed per deal. As software absorbs more of the research, the required analyst-to-partner ratio drops. Some firms are already running with much leaner teams than they did five years ago.

This is not primarily about cost savings. It is about what the firm can do with the same headcount. A partner augmented with intelligence infrastructure can do the work that previously required a partner plus two associates, plus produce higher-quality output.

The Remaining Analyst Roles Will Be More Senior

The analysts who remain will not be entry-level. They will be operator-turned-investors, domain experts, or researchers whose judgment is valuable independent of the rote analytical work. Their function will be more about direction-setting than work execution.

The entry-level associate role, as a generic first step in a VC career, is going to become rare. There simply will not be enough remaining work at that level to justify the slot.

Firm Output Per Partner Will Increase

Intelligence-augmented partners can cover more companies, maintain more thesis depth, respond faster to live deals, and make better-informed decisions. The capacity per partner is materially higher than it was five years ago, and it is still growing.

That has follow-on effects: firms can be smaller relative to their capital deployment, can maintain better portfolio oversight, and can compete with larger firms in ways that were not possible before.

What This Means for Careers

This is the uncomfortable part. If you are an analyst, associate, or aspiring analyst in the current VC job market, the playbook you grew up on is breaking.

The Learning Curve Is Different

Historically, you learned VC by doing a lot of analytical work over two or three years: hundreds of memos, dozens of market maps, years of company diligence. The volume built pattern recognition and investment instincts.

That apprenticeship model does not work when the apprentice work is done by software. The question for new entrants becomes: how do you build investment judgment if you never did the grunt work that used to produce it.

My answer is: deliberately. You cannot passively absorb pattern recognition from automated output the way you could from manually produced memos. You have to actively engage with the intelligence, question it, disagree with it, look for where it is wrong, and build your own views on top of it. The firms that train the next generation of investors well are going to be the ones that formalize this process.

Domain Knowledge Matters More

In a world where commodity analytical work is automated, what differentiates one investor from another is specific domain knowledge. An investor with deep expertise in a particular vertical, regulatory environment, or technology has something software does not: the contextual knowledge to know when the data is wrong or incomplete.

This is why the most interesting new VC hires are often operators, researchers, or domain specialists, not generic MBA graduates. The skills that matter are shifting toward depth rather than breadth.

Relationships Become the Moat

If software does the research, what makes one investor worth having on the cap table rather than another? The answer increasingly is the relationship: how they show up for founders, what network they bring, how they help in hard moments. Software does not replace that, and it never will.

Analysts who build real founder relationships, real sector depth, and real opinions over their early years will be fine. Analysts who define themselves by their ability to write tidy memos will not.

The Contrarian Take

There is a contrarian view I want to acknowledge. Some people argue that better research tools will mean firms hire more analysts, not fewer, because the research capacity becomes so much greater.

I do not buy it. That argument assumes the constraint on analyst hiring was always "we need more output." But the actual constraint for most firms is fund economics: analyst costs have to be justified by the deals they contribute to closing. If software delivers the same deal contribution without the analyst, the slot goes away regardless of output capacity.

There will be firms that thoughtfully expand their teams with intelligence-augmented humans who cover more ground than traditional analysts. But those will be the exception. The norm will be smaller teams, higher leverage per person, and a redefined role at the senior level.

What Firms Should Do Now

A few practical suggestions, if you are running a firm and thinking about this transition.

Be honest about the work. Actually audit what your current analysts spend their time on. Estimate the percentage that is now automatable. The number is usually higher than the partnership thinks.

Deploy intelligence infrastructure intentionally. Not as a side tool that the analysts happen to use, but as the new default research layer for the whole firm. Redesign the workflow around it, do not just bolt it on.

Redefine the analyst role upward. If software does the commodity work, what does the human analyst do that adds value? Write it down. Hire and train against that, not against the old job description.

Invest in the training gap. New investors have fewer opportunities to build pattern recognition through volume. Firms that deliberately train judgment (through deal reviews, portfolio post-mortems, structured sector immersion) will produce better investors than firms that assume the old apprenticeship still works.

Be clear with candidates. Hiring analysts into a role that is changing under them, without telling them, is not fair. The best new entrants will take jobs at firms that are honest about the shift and thoughtful about what the career path actually looks like.

The research stack inside most firms → is not going to survive the next five years in its current form. The analyst role is the most visible part of that, but the deeper change is that the entire information architecture of venture capital is being rebuilt.

Where This Ends

Here is my prediction, stated clearly so it can be judged in a few years.

Within five years, the majority of early-stage VC firms will operate with significantly smaller analyst teams than they did in 2020. The remaining analyst roles will be more senior, more domain-specific, and more focused on judgment than on production. Intelligence infrastructure will be table stakes for any firm that wants to be competitive.

The firms that adapt thoughtfully will produce better returns with leaner teams. The firms that resist will spend another decade employing people to do work that software does better, while competitors eat their lunch on speed, coverage, and quality.

This is not an anti-analyst post. I respect the work. I have worked alongside excellent analysts whose judgment I would trust more than many partners I have met. The point is that the job is changing, and pretending it is not is the wrong response.

The VC analyst is not dead. The VC analyst of 2015 is.

If you want to see what intelligence infrastructure looks like when it is built to augment senior investors rather than replace junior ones, that is what Brevoir → is for. The research stack of the next decade, built for the investors doing the thinking.