a16z: The Endgame of AI Business Models Is Not Selling 'Seats' but Taking 'Cuts'

Author: David Haber, Partner at a16z

Compiled by: Shenchao TechFlow

Senchao Insight: This analysis from a16z points directly to the most critical turning point in AI commercialization: shifting from hourly cost savings to revenue sharing based on results. If you are a SaaS practitioner or investor, this article explains why the next batch of AI unicorns will no longer sell 'seat fees' but will directly take a portion of customer revenue growth.

Traditionally, calling a telecom operator to report an internet outage goes something like this: a robotic voice first addresses you as a "valued customer," then makes you navigate through a phone menu for several minutes: "Press 3 for technical support, press 2 for broadband, and press 4 for other issues." Then you are left on hold with waiting music, feeling forgotten, until finally, a customer service representative picks up and asks for your name, account number, and a description of the problem. If rebooting the router doesn’t solve the issue, you have to wait again and explain the entire situation to another representative before you can schedule a technician visit.

AI is already changing these customer service scenarios. You no longer have to deal with robotic voices and multiple representatives; an AI agent can now pick up the call immediately. It can check your account, see if other users in your area are affected, either directly resolve simple issues or help you schedule a repair technician. There are no more "all customer service representatives are currently busy" messages, and no more waiting music. Until recently, this was essentially the technological limit.

But now, new capabilities are enabling AI agents to track leads and execute tasks over longer time spans. We believe this will allow AI companies to move beyond single customer interactions and rethink how to manage complex customer journeys that last weeks or even months. AI will guide insurance customers from the first call to final payouts, mortgage borrowers from pre-approval to closing, and enterprise sales leads from the first demo to signing. This will not only provide better outcomes for customers but also allow AI providers to be compensated based on revenue growth rather than just cost savings.

Value Only Appears at the Finish Line

There are many types of businesses where creating value for customers requires many steps, spanning weeks or even months. Take the traditional process of preparing a case in a plaintiff law firm as an example. Potential cases usually come in as phone leads, and the intake specialist collects key information to pass on to a lawyer for evaluation on whether to take the client. Once the law firm signs the case, the investigation and evidence collection begin: paralegals retrieve police reports, attempt to contact witnesses, and send evidence preservation letters to the defendant to retain evidence, while continuously updating the client on progress. The firm then has to wait for the client to finish treatment, draft a medical timeline, and submit the materials to the lawyer to complete the claim letter.

This process is what we used to call a "relay race": a long journey where each handoff carries the risk of loss or value degradation. Potential clients often call several personal injury law firms, usually signing with the one that responds the fastest. So if a client only hears voicemail or if the qualification review is too slow, the case will leak out of the funnel. After signing, evidence collection involves multiple parallel leads, and the firm may not have the patience to make a fifth call after four unanswered attempts. Additionally, human limitations may prevent the firm from thoroughly reviewing every piece of evidence, especially in cases that seem routine and involve smaller stakes. If a careful reading of the doctor's records could reveal signs of brain injury, significantly increasing the settlement amount, the firm might have accepted the opposing party's first offer outright. Ultimately, the case could drag on for months, involving numerous participants, with information scattered across documents, personnel, and systems. This makes it nearly impossible for the firm to maintain a holistic view of each client. The result is that important bills may not be included in the claim letter, treatment gaps go unexplained, and ultimately the settlement amount is depressed.

Opportunities for Long-Term Agents

Until recently, AI agents mostly handled bounded tasks: "send this follow-up email," "summarize that call," "create this document," and other narrowly defined interactions. For example, customer service became an excellent early application for AI because the problem space is clearly defined, allowing for high reliability, while also enabling customization based on specific company policies. Interaction times are short, results are verifiable, and if an AI agent encounters an edge case it cannot handle, a ready customer service team is usually available to escalate the issue.

Now, long-term agents can pursue goals across dozens of steps, multiple modalities, and longer time spans, allowing them to complete the relay race from start to finish. In the case of a plaintiff law firm, from the initial intake call, a long-term agent can be assigned to the case with the goal of maximizing the client's compensation amount. The agent will save and retrieve case background information as needed. It automatically wakes up based on events or deadlines, proactively dispatching sub-agents to contact witnesses, update clients, and communicate with lawyers via phone, email, or text. It then integrates findings, plans the next steps, drafts documents for review, and drives the case toward the best possible outcome. Across clients, it continuously learns, with data generated from each case helping it handle the next one better than the current.

The agent addresses the major points of value degradation observed in today’s relay races. At the journey's start, clients no longer leak out due to slow case evaluations, as the agent is online at any time, any day of the week, and can almost instantaneously complete case assessments. Evidence collection is no longer limited by human resources, as the case agent can always pull in an additional sub-agent to make the fifth, tenth, or even twentieth call. Since the agent can scale almost infinitely, even seemingly routine cases will be thoroughly handled, with every piece of evidence carefully reviewed, exposing potentially overlooked signs of brain injury. Finally, the agent retains a complete record of the case, from witness call recordings to police reports to medical records, ensuring that no information is lost.

Charging by Results

In addition to plugging these gaps, having a complete customer journey can also change the business model for agent providers. When agents can deliver results with higher quality and faster speed, they can generate revenue growth for clients, not just cost reductions. More importantly, since the impact is observable and attributable to the agent, providers can share the incremental value they create by pricing based on work products or results. In the case of plaintiff law firms, individual cases are natural work units, and the value of the final outcome, i.e., the settlement amount, is clearly identifiable. Moreover, as agents occupy an increasingly large share of the final work product, the results become easier to associate with the agent. Providers can therefore share in the revenue growth, achieving contract amounts that were previously unattainable through mere cost reductions.

Admittedly, transitioning to results-based pricing is not without friction. In the plaintiff attorney industry, most states prohibit non-lawyers from sharing legal fees, so pricing needs to align with settlement amounts but cannot directly share in them. In other verticals, providers may face pushback from procurement teams because they lack precedents for results-based contracts. Even when agents come closer to fully owning the results, attribution disputes may still arise in the early stages. Therefore, successfully deploying long-term operating agents is both a business challenge and a technical challenge.

However, charging by results in high-value customer journeys is not a new idea.

A Case for an AI-Native Red Ventures

Red Ventures (RV) is a great historical case. Founded by Ric Elias and Dan Feldstein, RV pioneered end-to-end outsourced sales operations for long-term consumer contracts. In the case of insurance, RV collects customer information while they search for these products (through owning websites like homeinsurance.com), proactively contacts customers, guides them through coverage options, and ultimately closes policies. By fully owning this journey, RV earns a commission from the insurance company for every policy sold. This model has been extremely effective: according to S&P Global data, RV generated approximately $3.7 billion in EBITDA on about $1.4 billion in sales in 2022.

Today, this model seems to face structural threats. At the top of the funnel, the way consumers discover products is changing. They are no longer relying on Google searches or comparison sites to explore and compare products but can directly ask personal agents like Meta's Muse or Instinct for product recommendations, diminishing the value of previously owned exploratory sites. Looking further downstream, it is no longer hundreds or thousands of people operating a linear funnel; long-term operating agents can handle journeys that are far more complex than in the past. They can work across products, across communication methods, operate in parallel, support any language, scale massively, and have infinite persistence. RV's EBITDA is projected to drop to about $160 million by 2025, due to customer churn and softening ad spend, which has already indicated the model's fragility, and AI will further change the entire industry.

Three Market Entry Paths

There are different ways to bring long-term operating agents to market:

(1) Selling Platforms: In this approach, providers sell long-term operating agents as software, enabling businesses to use them within their existing operations. The plaintiff law firm case is a good example where the agent integrates with existing teams and workflows. Selling platforms can bring revenue upside because there are usually no comparable software or outsourcing service contracts to anchor pricing. However, this also extends the sales cycle, as new budget line items need to be created, and when an agent interacts with multiple departments of a business, it becomes difficult to clearly attribute results to it.

(2) Selling Services: Here, providers take over a service that has already been outsourced to deliver the expected results more effectively, just like Red Ventures owns the entire customer acquisition journey for insurance companies. This approach shortens the sales cycle because the buyer's ROI calculation is straightforward, and the BPO budget line items already exist. However, the existing pricing models before the emergence of current AI may not reflect the true value that long-term operating agents can deliver, and many high-value journeys today are operated internally by businesses, limiting the coverage of this approach.

(3) Full-Stack Entry: Full-stack means vertical integration, becoming an AI-native player. For example, rather than selling long-term operating agents to a debt collection company, it might be better to buy a debt portfolio and collect it yourself, allowing you to fully capture any upside brought by AI and solve attribution issues. But this also means you have to take on service aspects like licensing or compliance, which can add interference and operational complexity outside of the actual customer journey.

It is still too early to judge which approach will prevail, but we are confident that long-term operating agents will win in large-scale relay races and expand the AI-addressable market from a portion of labor budgets to a portion of revenue. The preparation of plaintiff cases is just one example, and we expect agents to drive results in other high-value journeys, such as mortgage issuance, insurance claims processing, complex enterprise sales, and countless other scenarios. If you are capturing this opportunity, we would love to hear from you!

This content is provided for general informational purposes only and doesn't constitute financial, investment, legal, or tax advice. Any events, rewards, online promotions, or related information mentioned herein should not be considered a recommendation, solicitation, or invitation to purchase, sell, trade, or otherwise deal in any crypto assets. Crypto assets are highly volatile and may result in loss. The availability of WEEX services, products, and related events may vary by region. You are responsible for ensuring that your participation is in accordance with applicable local laws and regulations.