No Priors Ep. 83 | With Rippling COO Matt MacInnis

No Priors Ep. 83 | With Rippling COO Matt MacInnis

No PriorsSep 25, 202431m

Sarah Guo (host), Matt MacInnis (guest), Elad Gil (host), Narrator, Elad Gil (host)

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

In this episode of No Priors, featuring Sarah Guo and Matt MacInnis, No Priors Ep. 83 | With Rippling COO Matt MacInnis explores 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.

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.

Key Takeaways

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.

Get the full analysis with uListen AI

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.

Get the full analysis with uListen AI

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’.

Get the full analysis with uListen AI

Leverage unique platform data for defensible AI products.

Rippling’s advantage comes from unifying HRIS data with work systems (e. ...

Get the full analysis with uListen AI

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.

Get the full analysis with uListen AI

Use AI to surface hidden talent and support at-risk employees.

Examples from dogfooding show the model elevating under-the-radar high performers and flagging team members who need help ramping, enabling better coaching, fairer calibration, and stronger team performance.

Get the full analysis with uListen AI

Don’t chase generic AI features; build revenue-driving, unique products.

MacInnis criticizes me-too chatbots and copilots, arguing AI investments should go to capabilities that exploit a company’s ‘vibranium’—its distinctive data and platform strengths—to create new, monetizable SKUs.

Get the full analysis with uListen AI

Notable 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

Questions Answered in This Episode

How can companies validate that Talent Signal’s outputs are free of hidden bias, given that it’s trained on historical performance data that may itself be biased?

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. ...

Get the full analysis with uListen AI

What specific safeguards or audit mechanisms should be in place before expanding beyond a single 90-day signal to continuous performance monitoring?

Get the full analysis with uListen AI

How might employees’ behavior change once they know their work product is being systematically analyzed by AI, and could that create new kinds of gaming or pressure?

Get the full analysis with uListen AI

In what ways could Talent Signal misattribute performance in highly collaborative environments, and how should managers adjust their interpretation to account for that?

Get the full analysis with uListen AI

How should organizations communicate the use of AI in performance assessment to employees to maintain trust, transparency, and a sense of fairness?

Get the full analysis with uListen AI

Transcript Preview

Sarah Guo

Hi, listeners. Welcome back to No Priors. Today, Elad and I have a spicy one. We're here with Matt McInnis, the COO of Rippling, the juggernaut workforce management platform that unifies HR, IT, finance, and more. They're launching a new AI product that looks at the work output of employees and generates performance management signals. Sound terrifying? Let's discuss. It's so good to have you.

Matt MacInnis

Thank you for having me.

Sarah Guo

So I think a lot of our audience will know Rippling or use Rippling-

Matt MacInnis

Yep.

Sarah Guo

... but for anybody who's missing it, what does the company do?

Matt MacInnis

Yeah, it's an all-in-one platform for HR, IT, and finance. We do all the boring stuff, but the important stuff to help you run your company. So we wanna, like, adminis- uh, eliminate the administrative burden of running a company. That's all the, like, official language, but most people come to us and say they need payroll, and we have payroll. They need a device management solution, we have one of those too. So we do all that stuff.

Sarah Guo

The rumor is, you know, many hundreds of millions of dollars in revenue, growing fast. Anything else you can say about scale?

Matt MacInnis

Uh, it's going well. We got about 3,500 employees. We've got tens of thousands of customers using the platform. So I'd say we're doing something right.

Elad Gil

I guess also one of the things that you all have really pioneered is this, uh, notion of reintroducing compound startups or bundled products across a suite of different things. How many different products do you offer now, and how-

Matt MacInnis

Oh, man.

Elad Gil

... what's the velocity in terms of adding new ones?

Matt MacInnis

We have on the order of like 25 unique SKUs that, uh, a customer can buy from us. Products come in different shapes and sizes. And so like w- we ship small new things every quarter, and then we, we definitely do, like, big things every couple of quarters or so. We're about to ship, like, scheduling, which, you know, again, sounds unsexy but is actually really cool. We shipped an applicant tracking system for recruiting, you know, tacking these sorts of things under our HCM suite. We d- we do a lot of this partially because we have so many founders in the business. We have over 150 people who have started companies that now work at Rippling. It's like an explicit strategy to go out and try to either give talented entrepreneurs whose business ideas didn't quite work out, like, "Hey, hand raised, I've been there," uh, a safe place to land and continue either pursuing what they were interested in or do something new at Rippling, and so that's worked out really well for us on the velocity front for shipping new products. The compound startup thing obviously has been in the zeitgeist a little bit in the Valley. It's just obviously a huge tailwind for us that businesses generally wanna consolidate as much of their software onto a single platform as they can, so we're gonna keep pursuing this and keep recruiting awesome, talented entrepreneurs and ship new stuff all the time.

Install uListen to search the full transcript and get AI-powered insights

Get Full Transcript

Get more from every podcast

AI summaries, searchable transcripts, and fact-checking. Free forever.

Add to Chrome