Setting a one-year window for subsequent funding is an oversimplification; liquidity is too rare for that.
Actual funding cycles depend on many factors; time is a finite and valuable resource that cannot be standardized.
A year might be a luxury for some startups, while others, like those in biotech, require much longer to reach their milestones.
This single window does not account for the variable lifespan of resources available to these companies.
For example, a Swiss startup focused on deeptech might need 2 to 3 years to demonstrate the viability of its technology before attracting a second significant round, making the one-year limit totally unrealistic.

Follow the incentive: who benefits from the claim that the size of the venture capital portfolio reduces risk, verified by a power law alpha simulation?
This model verification does not account for unexpected macroeconomic shocks or sudden regulatory changes.
The reliability of a simulation entirely depends on its assumptions, which implies a latent conflict of interest.
For example, a global economic recession could invalidate the supposed risk reduction, regardless of portfolio size.

Imagine this: a single time frame of one year for subsequent funding of startups is like freezing a film after the first act.
This approach ignores the full narrative arc of a company's development, where tensions and turning points are unpredictable.
A startup may take longer to find its niche market or navigate economic cycles, without necessarily failing.
For example, a player in biotechnology might need more than a year between two funding rounds, to validate complex clinical trials, a delay that does not indicate a lack of potential success.

The idea that selective follow-ups are a fundamental pillar of venture capital fund performance, on par with decision quality, overly simplifies a factor that is, reliably, much more contingent.
History teaches us that the impact of such a policy heavily depends on the economic cycle and market liquidity, making its role sometimes marginal.
For example, during the dot-com bubble, aggressive follow-ups exacerbated losses, whereas prudence would have been preferable.
The record indicates that the relevance of this dynamic is subordinate to macro-economic conditions, not a constant.

Let's forget what we have invested. The term opposition for due diligence frameworks and venture capital prediction models is an exaggeration. A multi-agent analysis is thorough, while a performance measurement model is not mutually exclusive concepts. For example, a family office could possess a benchmark model to track overall performance, then use a multi-agent diligence framework to specifically evaluate governance risks in a new startup.

Let's forget for a moment the initial investment in the idea that a 2019 study was a prerequisite for Bento's thesis.
Would we still be convinced of the necessary convergence if we had not already spent time on this rapprochement?
Research areas like machine learning and venture capital often evolve in parallel, with teams working on similar ideas independently.
Bento's thesis could have drawn from many other sources or market-specific data; for example, access to startup data in Southeast Asia could have been a more direct driver than Arroyo's study.
It is more accurate to see this as a conditional influence, not as a direct causality.