Every decade or so, a new playbook emerges that fundamentally reshapes how we think about scaling businesses. In the 2000s, Apple and Nvidia showed us how to separate IP from manufacturing. In the 2010s, Salesforce taught us how to turn software into a utility with the cloud. Now, in 2024, we’re watching perhaps the biggest transformation yet: the separation of intelligence from service delivery in the $25 trillion services sector. But this time, the playbook isn’t coming from Silicon Valley. It’s being written by a new breed of investors who understand that AI isn’t just about automation, it’s about operating leverage.
The playbook: separate the “intelligence layer” from service delivery. It’s not entirely uncommon now in late 2024. But there are a few interesting ways private equity is rolling into bed with this model.
2000s IP ↔ Manufacturing Apple/Nvidia2010s Software as Service Salesforce2024+ Intelligence ↔ Service AI-Powered Rollups
Model Rollups
Company A(leaving anonymous) builds proprietary SOTA models for a service where inputs and outputs are performed by humans. This way, they compete head-to-head with incumbents while offering services 5x cheaper and 3x faster. Selling this into contracts (think government, etc.) is slow and challenging but is sticky and often lasts for years. Instead of selling copilots, they acquire smaller agencies for 1-2x revenue—feasible in their niche because many owners are looking to retire and there’s little innovation in this sector. Each acquisition provides more data, and the labor is repurposed from service delivery to data labeling, fueling a virtuous cycle: better models, more market share, and a greater data advantage. This turns low-margin services into software economics while maintaining the revenue base. This year, they’ve scaled to over $10 million in revenue.
Growth Buyouts
Company B, Metropolis, started as a vertical SaaS for the parking industry, where owners were trying to retire, operators were making thin margins so had a hard time selling to them. Then they bought Premier Parking with 600 locations, integrating their vision and software IP to boost EBITDA. Then they strike a ~$1.5B deal with industry leader SP+ to repeat at scale, rather than waiting for organic SaaS adoption. Metropolis’s tech allows unmanned parking facilities with license plate-based access and billing, slashing labor costs. Their data flywheel spans consumer behavior, real estate optimization, dynamic pricing, and more.
Vertical Rollup
Company C (this is fictional) is venture-backed, raising $100 million—instead of a seed round it’s more like a special acquisition vehicle or “vertical roll-up.” They acquire senior living facilities and consolidate them like a PE firm would, handling patient intake, staff scheduling, and revenue cycle management through their platform. But they layer on voice-to-voice models for automated phone calling and text models for outpatient care coordination, improving EBITDA margins by 15-20% while enhancing care quality.
Their approach prioritizes:
- margin transformation first,
- revenue growth second,
- and multiple expansion third,
- with an intelligence layer that gets marginally better with each acquisition’s data.
The core business operates as a roll-up but with a capital structure that sits between PE and VC. This market, like many service industries ripe for consolidation, is fragmented with owners looking to retire. While traditional PE rollups focus on consolidation and implementing standard software stacks. This model does something additional—each acquisition not only increases market share but also enhances the operational dataset, which trains the models and drives continuous improvements in collections, NPS, and other metrics. The key difference is that the data flywheel becomes stronger with each acquisition, with increasing margins from 15-20% to 30-25% backing this value prop beyond what traditional consolidation alone could achieve.
Data collection
In all cases, data collection shifts from improving individual productivity to training proprietary service models and fine-tuning copilots. With Github copilot, developers are already 29% more productive, and big law firms adopting copilots like Harvey’s utilization rates surging from 33% to 69% – the reality of transforming service businesses trading at 2-5x revenue into data factories comes with operational struggle.
So AI just increases margins with copilots? Probably the wrong question to ask– yes and leads to creating new business models impossible before these bigger models were commoditized. For title insurance ($57B, growing 12% annually) or insurance TPAs ($399B, 8.3% growth) – while it makes sense to consolidate and streamline with software, the play is separating the intelligence layer (risk assessment, document analysis) from service delivery (local presence, relationship management). While Lemonade tried disrupting insurance from the outside with pure software automation and got crushed on CAC and loss ratios. The better approach starts by uprooting the service. Build tools for existing players, acquire, collect interaction data to improve models, and implement at scale. Just 5% of services is bigger than all enterprise software at $656B.
- 2010s; build software that solves a problem and sell it to other businesses.
- 2024; build software that buys businesses.
Whether you start with models and use acquisitions for data, compete with PE firms, or create a tech-enabled rollup – the most interesting part isn’t the technology. It’s how these deals get structured and funded.
The real opportunity isn’t just in building AI or buying companies. It’s in creating entirely new financial structures that can turn the $25T services market into tomorrow’s software businesses. That’s the intelligence arbitrage.
Source: SDAN.io
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