The Faults of Research Machinery

My Question to Gemini: 

This story took place 40 years ago. It is a true account, and I have been granted permission to publish it anonymously.

 

A number of math students from one graduate school attended their first regional math conference. During a break between sessions, the group was present for a discussion about the reality of mathematical research. One postdoc offered the following observation:

 "All known ideas have already been investigated to death. So when a newly fledged mathematician emerges with something fresh and publishes it—thinking to work on this garden patch for a while—immediately, a number of advanced researchers fly in with their professional axes and finish his idea off, leaving the newcomer with nothing". 

 

In this context, the 'professional axes' do not imply that the senior colleagues are smarter. Rather, it means they have been in the field longer, possess a vast knowledge of existing results, and are highly trained in the 'Publish or Perish' culture of churning out articles quickly. A few newcomers may even be better mathematicians, but they are rarely given the chance to prove it before their territory is colonized. 

 

How close is data science research to this kind of state?

Gemini reply (generated in collaboration with Gemini 3 Flash):

The "scorched earth" style of competition described at that conference remains remarkably relevant, but in Data Science, the "professional axes" are powered by industrial efficiency and systemic speed rather than just expertise.

1. The "Pipeline Axe": Process over Insight

In your story, the senior researchers have a distinct advantage not because they are "smarter," but because they have been in the field longer. They have developed a reflex for the "academic churn."

• The Reality in 2026: Established Data Science labs have ready-made codebases, pre-processed datasets, and "fill-in-the-blank" publication templates.

• The "Finishing Off": When a newcomer publishes a novel idea, the veteran lab doesn't need to reinvent the wheel. They simply plug the newcomer's concept into their high-speed experimental pipeline. By the time the student is manually debugging their second script, the "professional axes" have already hacked the idea into ten different follow-up papers, effectively colonizing the student’s "garden patch."

2. The "Compute Axe": Scaling as a Weapon

In Data Science, "finishing an idea off" often looks like a race of scale.

• The Advantage: Advanced researchers at major institutions or Big Tech firms have instant access to massive GPU clusters that a newcomer simply cannot match.

• The Result: A student might discover a clever trick for model efficiency using a small dataset. The veterans, using their superior resources, can immediately apply that trick to a model 1,000x larger. They haven't necessarily contributed a more creative thought; they have simply used their "industrial machinery" to reach the finish line that the newcomer was walking toward on foot.

3. The "Benchmark Axe": Knowledge of the Map

Experience in the field acts as a map of the "territory." Older colleagues know exactly which benchmarks the community respects and which "reviewer traps" to avoid.

• Investigation to Death: Because Data Science relies on empirical results, an idea is "investigated to death" the moment it is tested across all major public benchmarks (like MMLU or HumanEval).

• The Swarm: While the newcomer is trying to understand the deeper why of their discovery, the "advanced researchers" use their knowledge of available results to swarm the what—running the idea through every standard test until there is no "novel" territory left for the newcomer to claim.

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Is there a "Fence" for the Garden Patch?

While the "professional axes" are intimidating, Data Science in 2026 offers one survival strategy that pure math didn't: Data Propriety.

The "advanced researchers" are experts at "finishing off" ideas that use publicly available data. If a newcomer finds a garden patch where the soil (the data) is private, specialized, or "messy"—such as local medical records, specific manufacturing logs, or unique linguistic dialects—the veteran researchers cannot easily use their axes there. They cannot churn out articles for a "patch" they cannot enter.

In this way, the newcomer stays safe not by being faster, but by working in the "thickets" where the industrial tools of the veterans cannot reach.