The standard pitch for ML-driven VC sourcing is correct in the aggregate and wrong at the margin where it matters. Hiring velocity, web traffic, GitHub commits, app store ratings, these signals outperform gut intuition across the full universe of startups. The problem is that the full universe is not your portfolio.

Stage-agnostic scoring models are systematically miscalibrated because the signals that predict success at seed have a different shelf life than the signals that predict success at Series A. Using a stale signal isn't neutral. It's noise dressed as insight.


THE DECAY CURVE IS REAL

Hiring velocity is the clearest example. At seed, a team that is aggressively hiring before revenue is a leading indicator of founder conviction, capital efficiency expectations, and organisational ambition. The signal is fresh and predictive, founders who build fast teams early tend to build fast companies.

By Series A, hiring velocity has largely decayed as a signal. The companies that are hiring fast at Series A are often the ones that raised a lot, not the ones that are executing well. Capital causes hiring, not the reverse. The signal that was predictive at seed is now confounded by the round itself.

The same logic applies in the other direction. Revenue retention like net dollar retention, expansion rate, churn cohorts is meaningless at seed because there is no revenue to retain. It's a powerful signal at Series A and a critical signal at growth. Stage-agnostic models that include revenue retention in the seed-stage scoring layer are not ignoring this fact, they are giving it zero weight implicitly, which is different from having thought about it explicitly.


WHAT STAGE-STRATIFIED STACKS LOOK LIKE

The funds winning the sourcing game are not running one scoring model. They're running signal stacks calibrated to stage, with deliberate choices about which inputs are predictive at which moments.

Seed signal stack: Team composition and background data (founders who have built before in the domain), early hiring decisions (which functions they hired first, how fast they hired after funding), product shipping cadence (GitHub activity, product launches, beta user counts), and network graph positioning (who is on the cap table, who is advising, who they have access to).

Series A signal stack: Revenue trajectory and retention shape (not just the number, but whether the curve is accelerating or flattening), sales cycle characteristics (ACV relative to sector, time-to-close relative to cohort), product engagement depth (not DAU, but whether the product is in the critical path of the customer's workflow), and competitive positioning relative to named alternatives.

The transition between these stacks matters as much as the stacks themselves. There is a window between seed and Series A, typically between $500K and $2M ARR where the seed signals are losing predictive power before the Series A signals have enough data to be reliable. This is the hardest scoring environment. The funds that perform here supplement quantitative signals with qualitative inputs they've structured: reference calls that follow a consistent schema, founder conversation recordings analyzed for linguistic patterns, board update language that gets embedded and monitored.


THE PRACTICAL IMPLICATION

If your sourcing model was trained on a corpus that includes seed, A, and B outcomes without stage stratification, it has learned signal weightings that are incoherent. It is applying the predictive logic of seed-stage signals to growth-stage companies and missing the predictive logic of growth-stage signals at seed. The model is not wrong — it is systematically miscalibrated in ways that are hard to see in aggregate and expensive in individual decisions.

The fix is not to discard the model. It is to stratify it. Train separate models for each stage, or apply stage as a conditioning variable that reshapes signal weights rather than treating it as one feature among many.

The funds that have done this will tell you it is a materially different output. The companies that score well on a seed-stage model look different from the companies that score well on a stage-stratified model at the same funding level. That difference is where the alpha is.


THE BOTTOM LINE

Alt-data sourcing works. The evidence is solid enough that this is no longer the argument. The argument is whether you are running a model that understands signal decay or one that treats all inputs as equally predictive regardless of when in the company lifecycle they're measured.

The signal decay curve, the half-life function of each alt-data input as a function of company stage is not a theoretical refinement. It is the difference between a sourcing model that finds the companies that already look like winners and one that finds the companies that will become winners. At seed stage, those are very different lists.

Your signal is fresh at seed. It's stale at Series A. Price them accordingly.

The distress signal arrives in the board update before it arrives in the financials. You are reading the financials.

Every month, founders send you a structured window into their psychological state: the board update. Revenue numbers, burn rate, headcount. You scan them. You check the metrics against the previous quarter. You note whether the company is on plan. What you are not doing, what almost no GP is doing is reading the language. And the language is already telling you something the numbers won't say for another two to four months.

This is not intuition. It is statistics. It is detectable, measurable, and actionable with tools that exist today. The funds that figure this out will have an early warning system. The funds that don't will continue to be surprised by distress events they technically had advance notice of.


WHAT THE UPDATE DRIFT SIGNAL LOOKS LIKE

Portfolio update language changes before financials do. The pattern is consistent enough across companies and market cycles that it has a name: the update drift signal. Here's what it looks like in practice.

Hedging increases. The quarterly update that used to say "we closed three enterprise deals" starts saying "we're seeing encouraging signals in our enterprise pipeline." The claims are softer. The tense shifts from past to present to future. Founders who were reporting outcomes start reporting process. This is not spin, it is an unconscious linguistic response to a deteriorating information environment. They know less than they knew six months ago about whether the next quarter will deliver.

Specificity falls. Early-stage updates from confident, executing founders are dense with proper nouns: customer names, contract values, named competitors displaced, specific hiring decisions and why. As execution degrades, the proper nouns thin out. "We had great conversations with several Fortune 500 prospects" is a sentence that contains no information. It is what a founder writes when they cannot write what they know.

The product-to-fundraising ratio inverts. In a healthy company at a growth stage, board updates are mostly about the product and the customers. When things start going wrong, the fundraising section expands. Founders who are worried about runway start reporting on investor meetings they've had, warm introductions they've received, the strategic conversations they're having with potential acquirers. None of this is explicitly alarming. But the ratio shift is: when the fundraising section exceeds the product section in word count, you are reading a company that has shifted its attention from building to surviving.

Response latency lengthens. This one does not require NLP. When a founder who has always responded to GP requests within 24 hours starts taking three or four days, something has changed. Either they're overwhelmed, distracted, or managing a conflict they haven't disclosed. Latency is behavioural, not linguistic, but it belongs to the same signal class: the founder's communication behaviour is changing because their internal state is changing. The update is lagging the operational reality.


THE TWO-TO-FOUR MONTH LEAD TIME

Why does the linguistic signal precede the financial signal by two to four months? Because the update reflects the founder's current knowledge, not the reported financial state.

Financial reporting, even in startups, is backward-looking by design. The Q3 board update reports on Q3, which ended last month. The metrics were aggregated last week. The analysis reflects decisions made over the past quarter. By the time the numbers show distress, the distress has been underway for months.

The founder's language, by contrast, reflects their current psychological state including the information they're holding but haven't yet structured into reportable metrics. A founder who has just lost a key account knows in the update they send before that loss shows up in churn numbers. A founder who is struggling to close the round knows in the language they use about "runway" before the cash balance shows a problem. The update is a real-time signal. The financials are a lagging indicator.

A fund that embeds monthly update text, monitors embedding-space movement over time, and flags anomalous drift in communication patterns has a materially different information environment than one that reads updates for the metrics and ignores the prose.


WHAT RUNNING THE MODEL REQUIRES

This is not a research project. The tooling exists. What it requires is operational discipline applied to data you are already receiving.

Step one: standardize the update corpus. If your portcos are sending updates in different formats, some in email, some in a shared doc, some in a Notion page, you need a consistent text extraction pipeline. This is solvable. The harder version is getting consistency in what founders report so the signal isn't confounded by format variation. A standard board update template that you enforce, kindly, across the portfolio is the prerequisite for the analytics layer.

Step two: embed and store. Every update goes through a text embedding model and the resulting vector is stored with metadata: company ID, period, date received, GP who read it, any manual flags. This is a few hundred lines of code and a vector database. It is not a data science project. It is data infrastructure.

Step three: monitor for drift. Semantic drift is measurable as distance between consecutive embedding vectors. A company whose monthly update sits close to the previous month's update is communicating consistently. A company whose update has moved significantly in embedding space is communicating differently — which is worth investigating. The drift metric does not tell you what changed. It tells you something changed.

Step four: build the flags. Specific signal patterns can be detected with lightweight classifiers or even well-crafted prompts over the update text. Hedge word frequency, future-tense ratio, product-to-fundraising word count ratio, named customer mentions per unit of text. These are countable. You can build a dashboard that flags updates above threshold on any of these metrics and routes them for GP review.

The goal is not to replace the qualitative relationship. It is to ensure that the GP who reviews the flagged update goes into the conversation with the founder having already noticed something, rather than discovering it after the fact.


THE COST OF NOT RUNNING IT

The funds that do not build this capability are not operating without information. They are operating with information they cannot use.

Every board update your portcos have ever sent is sitting in someone's email inbox or a shared drive. It is a timestamped, longitudinal record of how each founding team communicates under varying degrees of pressure. The fund that has never structured this data has paid for it through time, through relationship capital, through the management fee that funds the GP who reads the update and extracted only the surface layer.

The distress events that produce the hardest GP conversations, the ones where the fund is surprised, the runway is six weeks, the options are bad are almost always preceded by a detectable linguistic signal that no one was equipped to read. You were not paying attention to the diff.

The update is already telling you something. Start reading the whole document.