President and Chief Product Officer at Rippling
San Francisco, CA
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And then we push outputs to their existing and familiar work environment, like Slack or Teams.
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Automations make it easy to put a skill to use, regularly. We provide a familiar UI to concepts like a Weekly Operations Brief that can run on a schedule for a team leader.
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X users understand why these are useful, but they might not understand why “normal” businesses aren’t using them to do everything already. Normie businesses have to deal with compliance, data theft, and IP misappropriation (like spies!).
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Today, we’re shipping updates to @Rippling AI that make it easier to deploy data connections, skills, and automations safely across your org. They’re available to every employee, but they’re managed centrally and they’re auditable.
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Former Board Director of @SFBART says that new taxes won't fix what's broken, and she's right. Starve a flu!
This is real people! No funding without fixes to the poor way this system is operated. Another $17B won’t change anything without reform first. A NO vote on RTM ensures fixes first. #FixesBeforeFunding.
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Matt MacInnis retweeted
I personally net out somewhere in between (though I suspect that @zoink and Eric Schmidt) are not that far apart.
I think every application has some Pareto (there's ~20% of the functionality that ~80% of the people care about). The problem today is that any time you add functionality, you have to figure out a way to both expose it and then give persistent control over it. This leads – especially for complex enterprise applications – with just a ton of confusing navs, pages, workflows and worst – baroque settings that are both overwhelming and often not expressive enough.
I do think once we relax the concept that "all functionality needs to be exposed via the UI," we give software the chance to be dramatically simpler. E.g., one of the ways I use Rippling is for the FSA flow. The flow is fine but the overall UI is taxed by having to put it somewhere and then accommodate all the form-filling necessary to complete the request. If I could just forward the receipt to a chat flow and have it ask me for the missing data, it both becomes a simpler flow and relieves so much tension on the UI.
Maybe this will eventually lead to fixing one of my biggest nits of all time, which is that you can only use @WhatsApp on four devices. I realize this is probably some scalability trade-off someone made and "what normal user would ever need more than four devices," but one can hope. :)
User interfaces are not going away.
Yes, humans will use computers alongside agents. When working with agents, we will need the ability to audit what agents do in a human-readable way.
Yes, some interfaces (ex: dashboards, in-chat UI for ephemeral tasks, etc) will be generated. Most important interfaces will be intentionally designed. We will want both deterministic and non-deterministic interfaces in different situations. We're thinking a lot about the best ways to help designers shape non-deterministic systems at Figma.
Yes, new input modalities like voice, VR/AR and BCI will change the way we use computers and introduce lots of new interaction paradigms. This is all very exciting :)
Overall, the surface area of design will continue to grow much broader. Design is the new code and the key differentiator for software moving forward. Expect more and more energy to go into design / craft in the years ahead... it will be how you win or lose.
Eric is right about plenty of things, but the market will prove him wrong on this one...
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ನಮ್ಮ ಬೆಂಗಳೂರು ತಂಡ ಸೂಪರ್! 🔥
Rippling AI Lab is now live in Bengaluru!
Our new AI engineering hub is focused on building trusted AI for businesses, from agents and models to the infrastructure, evaluations, and sandboxing.
Bengaluru has been central to Rippling’s engineering story from the beginning. Rippling AI Lab will help shape the next generation of AI-powered business software.
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This is going to make AI far more useful for real-world business software.
We tested JEV in GrowthOS, Rippling’s agent harness for GTM, using anonymized real-world data.
E.g. a marketer or RevOps user could ask, “Classify these campaign emails by strategy,” then analyze which messaging patterns perform best.
JEV achieved 90% accuracy versus 50% for the LLM baseline and ran nearly 8x faster.
It was not a universal win. JEV performed poorly as a control mechanism inside our text-to-SQL agent loop because it lacked the semantic context needed to interpret internal business definitions.
JEV could see that a query returned rows, but not know whether those rows represented Rippling’s canonical definition of “qualified pipeline.” The LLM could inspect the relevant models, filters, and business definitions before deciding whether the analysis was complete.
The takeaway: JEV looks extremely promising for repeatable 'classification' like workloads, while LLMs remain better suited to context-heavy reasoning. But pairing them is pretty freakin awesome.
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Matt MacInnis retweeted
This is just not true. Zero rippling payments run through airwallex. And never for cos in the US. It’s an oppo smear by ramp but it’s not accurate.
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starts reminding me of the wonderful accomplishments of the Chinese Communist Party at the bottom (great development, broad support of the Chinese people, this is 'objective fact')
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I know I'm old fashioned, but I believe valuations should be an output, not an input.
There seem to be too many founder conversations right now that go something like, “What do we need to do to raise our next round?” And not enough about, “What do we need to do to build the foundation of a wonderful business, one worthy of spending my scarce time on?”
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