No Priors Ep. 83 | With Rippling COO Matt MacInnis

In this episode of No Priors, Sarah and Elad sit down with Matt MacInnis, COO of Rippling, to discuss the company’s unique product strategy and the advantages of being a compound startup. Matt introduces Talent Signal, Rippling’s AI-powered employee performance tool, and explains how early adopters are using it to gain a competitive edge. They explore Rippling’s approach to choosing which AI products to build and how they plan to leverage their rich data sources. The conversation also delves into how AI shapes real-world decision-making and how to realistically integrate these tools into organizational workflows. Sign up for new podcasts every week. Email feedback to show@no-priors.com Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Stanine Show Notes: 0:00 Introduction 0:32 Rippling’s mission and product offerings 2:13 Compound startups 3:53 Evaluating human performance with Talent Signal 13:19 Incorporating AI evaluations into decision-making at Rippling 14:56 Leveraging work outputs as inputs for models 18:23 How Rippling chose which AI product to build first 20:53 Building out bundled products 23:26 Merging and scaling diverse data sources 25:16 Early adopters and integrating AI into decision-making processes

Sarah GuohostMatt MacInnisguestElad Gilhost

Sep 24, 202431mWatch on YouTube ↗

WHAT IT’S REALLY ABOUT

Rippling’s Talent Signal Uses AI To Grade Employee Work, Not Vibes

Rippling COO Matt MacInnis discusses the company’s compound-startup strategy and unveils Talent Signal, an AI product that evaluates employees based on their actual work output rather than managerial impressions. The system ingests work product data from tools like GitHub and Salesforce, combines it with HRIS data such as role and level, and produces a calibrated signal on new hires after 90 days. MacInnis argues this can surface overlooked high performers, flag struggling employees earlier, and reduce biased, vibe-based performance reviews. He also emphasizes cautious rollout, strict internal policies against AI-only employment decisions, and openness to scrutiny around bias and ethical use.

IDEAS WORTH REMEMBERING

5 ideas

Evaluate performance from work product, not manager ‘vibes’.

Talent Signal focuses solely on concrete outputs—code, sales interactions, support tickets—rather than demographics or subjective impressions, aiming to reduce bias and make reviews more fact-based.

Use AI as a signal, never as the sole decision-maker.

Rippling’s internal policies forbid relying exclusively on Talent Signal for promotions, terminations, or other employment decisions; managers must still perform holistic, human judgment and review underlying examples.

Start with constrained, low-scope deployments to build trust.

The system currently generates a single signal at the 90-day mark for new hires, allowing companies to back-test, assess accuracy, and expand usage gradually without overstepping the organizational ‘Overton window’.

Leverage unique platform data for defensible AI products.

Rippling’s advantage comes from unifying HRIS data with work systems (e.g., GitHub, Salesforce) so it uniquely knows both the quality of work and the eventual human outcomes (promotions, performance exits), enabling powerful models others can’t easily replicate.

Target roles and cultures already oriented around coaching and metrics.

Early versions focus on IC engineers, salespeople, and support agents—functions where output is traceable, coaching-heavy cultures exist, and leaders are hungry for competitive performance insights.

WORDS WORTH SAVING

5 quotes

Where the magic really comes is where there's something common underneath all of these different applications that provides you with what I like to call your vibranium advantage.

— Matt MacInnis

If you wanna know if someone's a good engineer, look at their contributions. Like, look at their source code.

— Matt MacInnis

The motivating factor here, honestly, it's the bad manager… Talent Signal walks into that environment and slams your work product down on the table and says, 'What about this?'

— Matt MacInnis

Talent Signal is not making employment decisions. It's just giving this independent signal to the manager about how the employee is doing.

— Matt MacInnis

I'm thankful for the pitchforkers… when someone comes at us and asks hard questions about bias or unintended consequences, we're just gonna listen and we're gonna learn.

— Matt MacInnis

Rippling’s compound startup model and multi-product bundling strategyThe concept and design of Talent Signal, Rippling’s new AI productUsing work product data to assess employee performance and potentialCalibration, fairness, and bias mitigation in AI-driven performance toolsCultural and organizational traits of early adopters of Talent SignalInternal dogfooding, safeguards, and policy design around AI in HR decisionsBroader reflections on AI roadmaps, corporate finance, and platform ‘vibranium’ advantages

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