Tag: Khosla Ventures

Prince Mario-Max Schaumburg-Lippe: AI Startup DualEntry Raises $90M to Modernize ERP Systems

Nobody Demos ERP at Conferences. VCs Just Bet $90 Million on It.

On October 2, 2026, a startup most people had never heard of came out of stealth and closed a $90 million Series A. DualEntry, an AI-native ERP company headquartered in New York, raised at a $415 million post-money valuation. The round was co-led by Lightspeed Venture Partners and Khosla Ventures, with GV (Google Ventures), Contrary, and Vesey Ventures participating. Total funding now exceeds $100 million raised in roughly 15 to 18 months since founding.

ERP — enterprise resource planning — is the least glamorous corner of enterprise software. It's the general ledger, accounts payable, bank reconciliations. Nobody puts it on a keynote stage. But finance back-offices are where AI automation converts most directly into dollars, and DualEntry's pitch is aimed squarely at the industry's most painful ritual: the legacy ERP migration.

The 24-Hour Migration

Here's the number that makes CFOs sit up: DualEntry's "NextDay Migration" engine claims to map and transfer historical financial data — line items, subledgers, attachments — from legacy systems like NetSuite, Sage Intacct, SAP, Microsoft Dynamics, QuickBooks, or Xero in under 24 hours. Traditional ERP migrations typically take 6 to 12 months and cost millions in consulting fees.

Anyone who has lived through an ERP migration knows why this matters. These projects are infamous: years of consultants, broken integrations, finance teams living in spreadsheets for quarters at a time. The switching cost is so high that companies stay on systems they hate. A migration measured in hours instead of months isn't an incremental improvement — it's a different market. It turns the vendor relationship from hostage situation into subscription.

The product itself is built AI-native from the ground up: general ledger, AR/AP, live bank connections, FP&A, and audit controls, with 13,000+ third-party integrations, SOC 2 Type II certification, and a range designed to scale from $5 million-revenue startups to NYSE-listed companies. Co-founder and CEO Santiago Nestares launched the company about a year ago.

That SOC 2 certification deserves a sentence. Finance software is where enterprise buyers are most conservative — nobody lets an uncertified vendor near the general ledger. The 13,000 integrations matter for the same reason: an ERP that can't talk to a company's existing tools is a non-starter regardless of how good its AI is. DualEntry is pitching disruption, but it's doing it with the compliance checkboxes that get you through procurement. That's a savvy combination.

The Finance Team of One

The customer story DualEntry leads with is genuinely striking. Slash, a fintech with over $100 million in ARR, runs on DualEntry with a finance team of one person. One.

The company reports that its AI automates up to 90% of repetitive tasks — bank reconciliations, intercompany transfers, anomaly detection, fraud prevention — and says it has processed over $100 billion in journal entries through AI automation, serving thousands of global users.

A note of healthy skepticism, which the coverage also flags: the $100 billion in journal entries and the 90% automation figures are company-reported. DualEntry is early — it launched about a year ago. But the investor list suggests the diligence was real. Lightspeed and Khosla don't co-lead $90 million rounds on vibes, and a fintech doing nine figures of revenue running on a one-person finance team is a customer story that's hard to fake.

Why AI-Native Beats AI-Bolted-On

There's a broader pattern worth naming. The first wave of enterprise AI was about bolting copilots onto legacy systems — an AI assistant inside your old ERP. DualEntry represents the second wave: systems rebuilt from the ground up with AI as the architecture, not the add-on. It's the same shift playing out across enterprise software, from open-source AI models entering professional domains to new approaches in AI-designed biology.

The deeper thesis: AI-native systems don't just automate tasks, they change org math. A $100 million ARR fintech running on a finance team of one is a preview of what every CFO is now being asked to imagine. The headcount model of corporate finance — teams of analysts doing reconciliation by hand — is exactly the kind of work that disappears when the system itself does the reconciling. Whether that's exciting or unnerving depends on where you sit, but the direction is unmistakable.

For the broader startup market, DualEntry's round is another datapoint in the enterprise AI funding surge: top-tier firms writing large checks for vertical AI companies with real customer traction, not just model demos. The $415 million valuation on roughly a year of operating history is rich — but in this market, it's the price of admission to the AI-native enterprise stack.

The Takeaway

ERP migrations have been the enterprise world's most dreaded ritual for decades. DualEntry's bet is that AI can compress 6–12 months of consultant misery into 24 hours — and a $100M fintech running on a finance team of one suggests the pitch isn't just theory. The least glamorous corner of enterprise AI may be its most lucrative.

Prince Mario-Max Schaumburg-Lippe: General Intuition Raises $220M to Teach AI the Real World

The next frontier of AI isn’t a smarter chatbot. It’s AI that can see, move, and act: pick up a box, navigate a warehouse, climb a set of stairs. And on September 30, one of the biggest bets on that future got a lot bigger: New York–based General Intuition raised $220 million at a $6.2 billion valuation.

The backers are a who’s who of venture capital: Valor Equity Partners, Atreides Management, 776, Point72 Ventures, Khosla Ventures, and General Catalyst. One of the largest physical-AI raises of the month, and a clear signal that serious money is following the agentic-AI wave into the real world.

The capital is earmarked for GPU cluster acquisitions, accelerated foundation-model training, expanded machine learning and reinforcement learning research teams in New York, and commercial infrastructure spanning both virtual gaming systems and physical robotics platforms.

Why Games Are the Gym for Robots

The company’s core idea is elegant. Games were the original training ground for modern AI: think DeepMind learning Atari, AlphaGo conquering Go. General Intuition is betting that millions of hours of gameplay telemetry is the bridge to robots that function in messy reality.

Here’s the clever bit. Instead of hand-labeling the physical world, an expensive, slow, painstaking process, learn intent from players who already demonstrate it. Every game session is a human showing, moment by moment, what they meant to do: navigate this space, grab that object, avoid that obstacle.

General Intuition’s tech is a multi-modal “action foundation model” trained on massive proprietary datasets of multi-angle gameplay video combined with player input telemetry and real-time execution matrices. The model learns spatial navigation, physics interactions, and operational intent, the same skills a robot needs, and drives both autonomous agents in simulations and humanoid robots in the real world.

In other words: the training data is hiding in play. Humans already generate exquisitely detailed demonstrations of physical intent every time they game. General Intuition is just harvesting it.

The Embodied AI Wave Is Building

This raise doesn’t exist in isolation. Physical AI, the industry term for AI that acts in the physical world, is having its moment. Humanoid robots like Agility’s Digit are getting stronger, safer, and more capable. Driverless trucks are on public roads. Robotaxi fleets are scaling fast.

Each of those machines needs a brain that understands physics, not just language. A chatbot can be wrong and it’s a joke; a 200-pound humanoid can be wrong and it’s a lawsuit. The bar for “good enough” in physical AI is brutally higher than in text, which is why the training approach matters so much.

General Intuition’s angle, learning from demonstrated intent at massive scale, sidesteps the biggest bottleneck in robotics: labeled real-world data is scarce and expensive. Gameplay telemetry is abundant and rich. If the transfer from virtual to physical works, it’s a shortcut around years of slow data collection.

What $6.2 Billion Says About the Moment

Valuations this size say investors believe embodied AI is following the same arc as language AI: a period of expensive foundational work, then a sudden unlock. The GPU clusters, the expanded research teams, the commercial infrastructure across gaming and robotics: this is a company building the full stack, not a demo.

The New York angle is nice too. The company is expanding its ML and reinforcement learning research teams in the city, planting a flag for physical AI on the East Coast in an industry that tends to default to the Bay Area.

Where the Robots Go First

The commercial infrastructure piece of the raise deserves attention. General Intuition isn’t just training models — it’s building the deployment pipeline across virtual gaming systems and physical robotics platforms. The near-term beachhead is likely the warehouse: structured enough to be tractable, labor-hungry enough to pay for automation. Longer term, the same action models that navigate a game level could navigate a disaster site or a factory floor.

That’s the bet the investors are making with $220 million: that “action” becomes a foundation-model category the way language did, and that the company holding the best action model holds a position worth far more than $6.2 billion. It’s early. But every major AI platform started with someone training an expensive model on data nobody else thought to collect.

The Optimist’s View

Picture where this leads. Robots that learn movement the way humans do, by watching and doing at scale, could take on the dull, dirty, and dangerous work that’s hard to staff: warehouse shifts, disaster cleanup, elder care assistance. The path from a game controller to a helpful humanoid is longer than a press release makes it sound, but the direction is right, and $220 million is a serious down payment.

General Intuition’s bet is simple and, in retrospect, may look obvious: the internet taught AI to think; play will teach it to move. The funding announced today suggests a lot of very smart investors agree.