Rippling

Magic AI

Rippling

Rippling uses modular pricing — companies buy the modules they need and pay per employee per month. The core platform (employee directory) is the foundation, with add-on modules for payroll ($8/month per employee), benefits, time and attendance, learning management, IT device management, app management, corporate cards, and expense management.

This modular approach means Rippling can land with one module and expand to many. A company might start with just payroll, then add device management when they realize it's available, then corporate cards.

Average revenue per customer grows as companies add modules.

The compound effect is the strategy. Each individual module might not be the best standalone product, but the integration between modules creates value that no combination of point solutions can match.

When your payroll system, IT system, and expense system all share the same employee database, automation becomes trivial.

Magic AI

Magic is building an AI that works like a software engineer, not a chatbot. The goal is a model you can hand a whole code base and a task, and it writes, reviews, and debugs like a teammate.

Their edge is memory. In 2024 they showed off LTM-2-mini, a model with a 100 million token context window.

To put that in perspective, that is about 10 million lines of code the model can look at in one shot, by far the biggest of any model at the time. The plan is to sell this as an AI coworker to companies that write a lot of software.

As of the 2024 rounds, the product was still not for sale.

Rippling

Parker Conrad's origin story at Rippling is inseparable from his spectacular flameout at Zenefits. Conrad co-founded Zenefits in 2013 as an HR platform for small businesses and grew it to a $4.5 billion valuation in two years.

Then it imploded. Regulators discovered Zenefits employees had used software to cheat on insurance licensing exams.

Conrad was forced to resign as CEO in February 2016. The company he'd built was toxic, and his reputation was in ruins.

Most founders would have retreated. Conrad started Rippling in August 2016 — six months after being pushed out of Zenefits.

His co-founder Prasanna Sankar was a former Zenefits engineer. The insight behind Rippling came directly from the Zenefits experience: companies use dozens of disconnected systems for HR, IT, payroll, and finance.

When you hire someone, you set them up in the HR system, the payroll system, the benefits system, the laptop provisioning system, the software access system — all separately. When they leave, you have to remove them from each one individually.

It's a mess.

Rippling's premise was radical: build one unified platform with the employee record at the center. When you hire someone in Rippling, it automatically sets up their payroll, enrolls them in benefits, ships them a laptop, provisions their software accounts, issues a corporate card, and adds them to the right Slack channels.

One action triggers everything. When they leave, one click revokes it all.

Magic AI

Eric Steinberger and Sebastian De Ro started Magic in 2022. Steinberger is an AI researcher who became convinced that the missing piece for a true AI software engineer was memory.

Chatbots forget what you told them a few thousand words ago. He wanted a model that could read your entire code base and remember all of it.

They raised a small seed in 2022, then a $23 million Series A in early 2023 led by Alphabet's growth fund CapitalG. From day one the pitch was not a chatbot.

It was an AI colleague that could handle real engineering work.

Rippling

Rippling's growth strategy is "compound startup" — building many products simultaneously instead of one at a time. Most SaaS companies pick a niche and dominate it before expanding.

Rippling launches new product modules aggressively, banking on the thesis that integration is the killer feature.

The land-and-expand motion works because every module sells every other module. An HR team that uses Rippling for payroll sees that IT device management is available.

The IT team that uses device management discovers corporate cards. Each module is a door to the entire platform.

Mid-market focus (50-2,000 employees) hits the sweet spot — these companies are big enough to need multiple systems but small enough that a single platform is appealing. Enterprise companies have entrenched vendors.

Tiny startups don't need the full suite. The mid-market wants consolidation and Rippling delivers it.

Magic AI

Magic's strategy is research first, product later, which is both unusual and expensive. Instead of shipping an app and iterating in public, they raised huge sums to chase a technical breakthrough in model memory.

The headline move was the 100 million token context claim, which got the whole AI world talking. They also cut a deal with Google Cloud in 2024 to build two supercomputers, called Magic-G4 and Magic-G5, using NVIDIA chips, to train bigger models.

Star backers like Eric Schmidt served as validation. The bet is that if the tech is far enough ahead, the product and the customers follow.

Rippling

Building many products simultaneously means none of them is best-in-class individually. Gusto has better payroll for small businesses.

Jamf has better device management. Brex has better corporate cards.

Rippling's bet is that "good enough across ten categories" beats "best in one." That bet is unproven at scale.

The Parker Conrad factor cuts both ways. His Zenefits implosion is public knowledge, and some investors and customers remain wary.

Conrad has been open about the experience but the baggage is real. On the flip side, the "I failed and came back stronger" narrative resonates with many founders.

International expansion is complex. Rippling's Global product offers employer-of-record services in 100+ countries, but managing local labor laws, tax regulations, and benefits across dozens of jurisdictions is extraordinarily complicated.

Deel and Remote.com are dedicated international employment platforms that may execute better in global markets.

Magic AI

Magic has raised close to half a billion dollars and, years in, still has no product the public can buy. That is the whole problem in one sentence.

A giant context window is a great demo, but rivals like Cursor, GitHub Copilot, and Cognition's Devin already have real users and revenue. Magic has to prove that more memory actually makes a smarter engineer, not just a bigger number on a slide.

And it has to do that while burning enormous sums on compute against competitors who are already shipping. The clock and the cash both keep running.

Rippling

Rippling Unity — the core employee data platform that connects all modules through a unified employee graph. Every system shares the same data, eliminating manual syncing.

Rippling Payroll — full-service payroll processing for US and international employees with automated tax filing. Rippling IT — device management (ship, configure, secure, and wipe laptops), software provisioning (manage employee access to hundreds of SaaS apps), and identity management.

Rippling Spend — corporate cards and expense management with policy enforcement built into the card itself. Rippling Global — international payroll and employer-of-record services covering 100+ countries.

Magic AI

The centerpiece is Magic's LTM, or Long-Term Memory, model family. In 2024 they unveiled LTM-2-mini, a model with a 100 million token context window, pitched as the key to an AI that can understand an entire code base at once.

The broader product is an AI software engineer, or coworker, that can write and change code across a whole project. The company also announced Magic-G4 and Magic-G5, two supercomputers built with Google Cloud and NVIDIA to train these models.

Much of it is still research rather than a shipping product.

Rippling

Founders Fund led the Series A — Peter Thiel betting on Conrad's comeback. Kleiner Perkins and Bedrock invested in growth rounds.

Greenoaks Capital, Coatue Management, and Y Combinator participated. The 2024 round valued Rippling at $13.5 billion, led by Coatue, and a 2025 round pushed it to $16.8 billion.

Notable for being founded by someone investors initially wouldn't touch after the Zenefits scandal.

Magic AI

Magic's backers are a murderers' row of AI insiders. It started with a $5 million seed round in 2022 and a $23 million Series A in early 2023 led by Alphabet's CapitalG.

Then in February 2024 came a $117 million round led by Nat Friedman and Daniel Gross, two of the most connected investors in AI. The blowout was August 2024.

Magic raised $320 million with checks from former Google CEO Eric Schmidt, CapitalG again, Atlassian, Jane Street, Sequoia, and Elad Gil. That pushed total funding to around $465 million, all for a company still building toward its first sellable product.