Software Firms Have Wanted "Outcomes-Based" Pricing for Decades: AI Means They Might Finally Get It

Private equity firms have poured hundreds of billions of dollars into enterprise software firms over the last few decades, on the assumption that terminal values and growth rates were sizable. 

But artificial intelligence has raised existential questions about enterprise software valuations because it raises the issue of positive feedback loops, which are inherently stability disrupting. 

A positive feedback loop is a process where the output of a system amplifies or intensifies the initial stimulus, driving the system further away from its original state. A negative feedback loop, on the other hand, allows the system to adjust. 

source: Citrini Research 

Unlike negative feedback that maintains stability, positive loops often cause exponential change in a single direction, without the corrective feedback that allows the system to stabilize itself.

In the context of what AI might do, it could, in principle, reduce employment. Reduced employment might lead to less consumer spending. Less consumer spending should put pressure on gross revenues and profit margins.

That might increase reliance on AI to reduce operating costs, which in turn further reduces employment and spending, and therefore firm revenues and profits. 

If AI agents can write code and execute complex workflows autonomously, what happens to the software companies built on charging per human "seat" or user? 

What happens to cash flows, and the valuation models built on those cash flows?

How much enterprise software value creation will happen in the future, compared to other segments of the market? And what does that imply for asset holding periods and exits?

Area of PE Operation	The Historical SaaS Era	The Current AI-Disrupted Reality
Valuations & Multiples	Software enjoyed near-automatic, premium revenue multiples (often 20x+ ARR) due to reliable recurring revenue and low customer churn.	A fundamental re-rating is underway. Investors are questioning long-term defensibility, dragging exit expectations down (e.g., closer to 15x EBITDA). PE firms are facing potential markdowns on legacy SaaS portfolios.
Due Diligence	Focus was heavily weighted on Net Revenue Retention (NRR), customer acquisition costs (CAC), and the "stickiness" of workflows.	Firms are now commissioning aggressive "AI vulnerability audits." The core question is whether a target's product is an easily replaceable interface, or if it possesses proprietary, structured data that AI agents need to function safely.
Value Creation (Operations)	Growth relied on expanding sales/marketing teams and steadily increasing the number of licensed human users per client account.	Operating teams are forcing portfolio companies to pivot away from seat-based pricing toward outcome-based or usage-based models. Genuine AI integration is now required for survival, not just for marketing.
Leverage & Private Credit	Software LBOs were built on highly secure, predictable cash flows, allowing PE firms to utilize significant leverage.	Private credit markets are showing stress (evidenced by the recent gating of tech-heavy credit funds in early 2026). Lenders fear that if AI shrinks a software company's user base, cash flows will contract, increasing default risks.
Exit Environments	Shorter hold periods (3–5 years) with reliable exits via public markets (IPOs) or sales to strategic buyers/larger PE funds.	Hold periods are extending. Exits are incredibly difficult to execute without hard proof that a portfolio company has successfully evolved into an AI-augmented platform rather than a legacy SaaS tool facing obsolescence.

The sort of interesting issue is that lots of observers have wanted to shift pricing from a more-commodity-like “seats” model to a “success-based” or “outcomes” pattern, the thinking being that this approach provides higher margins and perceived product value. 

Such an approach, in principle, makes more revenue sources available to suppliers: not just a share of the information technology budget but a share of saved labor costs; revenue shrinkage; customer acquisition; churn; marketing or other operations costs. 

Company, Product	Function	Outcome Metric	Pricing Mechanism	Strategic Benefit
Intercom Fin	AI customer support	Tickets resolved	~$0.99 per successful resolution	Aligns price with support deflection value; drives product improvement (Pragmatic Institute - Corporate)
Zendesk AI agents	Customer service	Successful resolutions	Charge per resolved ticket	Hybrid model linking AI value to outcomes (L.E.K. Consulting)
Chargeflow	Payments / fintech	Chargebacks recovered	% of recovered revenue	Captures share of direct financial benefit (rezoomex)
Riskified	E-commerce fraud	Approved fraud-free transactions	Charge per approved transaction	Direct link between trust and revenue protection (L.E.K. Consulting)
Vendr	SaaS procurement	Savings negotiated	% of savings	Monetizes cost reduction achieved (tanayj.com)
ServiceNow pilots	Workflow automation	Efficiency improvements	Full payment only if targets met	Demonstrates ROI in enterprise automation (Monetizely)
InsideSales / XANT	Sales acceleration	Conversion & velocity improvements	Performance-linked pricing	Aligns cost with revenue impact (Monetizely)
HubSpot performance tiers	Marketing automation	Campaign performance metrics	Discounts tied to results	Incentivizes effective usage (Monetizely)
Hitachi Rail “trains-as-a-service”	Industrial IoT	On-time performance	Payment linked to punctuality	Transfers reliability risk to vendor (Pragmatic Institute - Corporate)
Identity verification (iDenfy)	Fraud / KYC	Verified users	Charge per approved identity	Clear, measurable success metric (L.E.K. Consulting)

To use a simple example, instead of selling shovels, one sells “holes.” In some ways, it is similar to the shift from “product” pricing to “services” pricing. Instead of selling a car, one sells transportation services, with recurring fees rather than one upfront purchase. 

Business Function	The "Outcome" Being Sold	Real-World/Current Examples	Pricing Mechanism
Customer Support	A successfully resolved customer inquiry.	Intercom (Fin AI), Zendesk	No charge for "chats"; you only pay (e.g., $0.99) when the AI solves the issue without human intervention.
Legal & Compliance	A completed, filing-ready legal document.	EvenUp, Harvey	Instead of a monthly license for paralegals, firms pay per AI-generated demand letter or contract audit completed.
Sales & Marketing	A "Sales Qualified Lead" (SQL) or booked meeting.	11x.ai, Salesforce (Agentforce)	Companies pay for the meeting booked by the AI agent, rather than paying for a seat for a human Sales Development Rep (SDR).
Fintech / Payments	A recovered chargeback or fraud-free transaction.	Chargeflow, Riskified	The vendor takes a percentage of the money recovered or a fee only for transactions that are proven to be legitimate.
Cloud Operations	Hard-dollar savings on infrastructure bills.	Viewnear, ProsperOps	The software is free or low-cost; the vendor takes a 20-30% "success fee" of the actual savings generated on the client’s AWS/Snowflake bill.
Supply Chain	A successfully renegotiated vendor contract.	Pactum, Luminous	The AI agent autonomously negotiates with thousands of "long-tail" vendors; the fee is a share of the cost-reduction achieved.

Up to this point, such outcomes-based pricing has been quite difficult to implement. AI might make it imperative. 

In Ice Hockey Gold Medal round at Olympics, It was a Shame Any of the Teams Had to Lose

Years from now, I believe I'll only remember two things about the Winter Olympics of 2026: first, the USA men's ice hockey team getting a gold medal, the first since 1980.

source: USA Hockey

The other is the USA women's hockey team also winning a gold medal. 

source: Associated Press

So far, this is the only time one country has won both men's and women's ice hockey titles, since women's ice hockey was added as an Olympic sport in 1998. 

In both cases, the pairing was United States and Canada, and I'll have to say it was hard to watch two of my favorite hockey players on the men's Canadian team lose, as otherwise I always root for them. 

The matches were hard fought, and unfortunately one team had to lose. But huge respect for Canada and U.S. players on men's and women's hockey teams this year. 

We salute you.  

NBER Study Finds "No Productivity Impact" from AI So Far (And Nobody Should be Surprised)

Maybe we should not be surprised that studies of AI productivity often show few results so far. A recent study published by the National Bureau of Economic Research, for example, found:

• around 70 percent of firms actively use AI

• More than 66 percent of top executives regularly use AI, their average use is only 1.5 hours a week, with one quarter reporting no AI use

• firms report little impact of AI over the last three years, with over 80 percent of firms reporting no impact on either employment or productivity. 

source: NBER 

None of that should come as a surprise. Sure, AI adoption is widespread among survey respondents. across the four countries (U.S.; U.K.; Germany; Australia) studied:

• 69 percent of businesses currently use some AI technology

• “text generation” is a top use case

• “visual content creation” follows

• “data processing” is a top-three use case as well.

source: NBER 

But none of those use cases can easily be tied to bottom-line quantitative results very easily, if at all. They should be time savers, but faster text or image creation or some data manipulation, at modest usage rates, in the context of existing business processes, are probably reasonably described as relatively trivial contributors to measurable productivity. 

Also, there is no reason to expect the J curve of technology adoption will fail to be seen here. 

source 

Amara's Law suggests we will overestimate the immediate impact of artificial intelligence but also underestimate the long-term impact. 

Economic historians such as Erik Brynjolfsson and Paul David have documented that transformative, general-purpose technologies tend to follow the J-curve pattern. 

Initial deployment generates negative or flat productivity returns relative to investment, often for a surprisingly long time. 

David's famous 1990 paper on the "dynamo paradox" showed that electrification of US industry began in earnest in the 1880s but didn't produce measurable aggregate productivity gains until the 1920s.

The reasons are structural: firms must reorganize workflows, retrain workers, build complementary infrastructure, and abandon legacy processes before the technology's benefits materialize. 

The productivity gains, when they finally arrive, are real and large, but they accrue after enormous sunk costs and a long gestation period.

And that is going to be a problem for financial analysts and observers who demand an immediate boost in observable firm earnings or revenue, as well as the firms deploying AI that will strive to demonstrate the benefit.

The Great Reversal

Many Christians now are in the liturgical season of Lent, (Feb 18, 2026) to just before Easter, a period for repentance and spiritual renewal. 

Consider, in that regard, the Beatitudes and the Litany of Humility, which both dramatize the great reversal of values called for, where what humans normally prize (status, power, recognition, self‑assertion) is inverted and replaced by poverty of spirit, meekness, and self‑forgetful love. 

source: Laura James 

"The Sermon On The Mount" (Mt 5:3-12) begins with "The Beatitudes," an astounding and jarring reversal of human values, where those who are “blessed” (happy!!) are those one would never assert should be happy: 

• Poor in spirit (humble)

• Those who mourn (their spiritual poverty)

• The meek (gentle out of awareness of their spiritual poverty)

• Who hunger and thirst for righteousness (fervently desire to rectify their spiritual poverty)

• The merciful (forgiving and compassionate)

• The pure in heart (no selfishness)

• The peacemakers (not the "absence of conflict” but the absolute fulfillment of goodness)

• Persecuted for righteousness (misunderstood by the powerful).

Rafael Cardinal Merry del Val’s Litany of Humility illustrates interior movements that illustrate the implications of the Beatitudes. 

source: Friarmusings 

O Jesus, meek and humble of heart, Make my heart like yours.

From self-will, deliver me, O Lord. 

From the desire of being esteemed, deliver me, O Lord.

From the desire of being loved, deliver me, O Lord. 

From the desire of being extolled, deliver me, O Lord. 

From the desire of being honoured, deliver me, O Lord. 

From the desire of being praised, deliver me, O Lord. 

From the desire of being preferred to others, deliver me, O Lord. 

From the desire of being consulted, deliver me, O Lord.

From the desire to be understood, deliver me, O Lord. 

From the desire to be visited, deliver me, O Lord. 

From the fear of being humiliated, deliver me, O Lord. 

From the fear of being despised, deliver me, O Lord. 

From the fear of suffering rebukes, deliver me, O Lord. 

From the fear of being calumniated, deliver me, O Lord. 

From the fear of being forgotten, deliver me, O Lord. 

From the fear of being ridiculed, deliver me, O Lord. 

From the fear of being suspected, deliver me, O Lord. 

From the fear of being wronged, deliver me, O Lord. 

From the fear of being abandoned, deliver me, O Lord. 

From the fear of being refused, deliver me, O Lord 

source: WJLA

 

That others may be loved more than I,  Lord, grant me the grace to desire it.

that others may be esteemed more than I, Lord, grant me the grace to desire it. 

That, in the opinion of the world, others may increase and I may decrease, Lord, grant me the grace to desire it. 

That others may be chosen and I set aside. Lord, grant me the grace to desire it. 

That others may be praised and I go unnoticed, Lord, grant me the grace to desire it. 

That others may be preferred to me in everything, Lord, grant me the grace to desire it. 

That others may become holier than I, provided that I may become as holy as I should, Lord, grant me the grace to desire it. 

At being unknown and poor, Lord, I want to rejoice. 

At being deprived of the natural perfections of body and mind, Lord, I want to rejoice.

When people do not think of me, Lord, I want to rejoice.

When they assign to me the meanest tasks, Lord, I want to rejoice

When they do not even deign to make use of me, Lord, I want to rejoice.

When they never ask my opinion, Lord, I want to rejoice. 

When they leave me at the lowest place, Lord, I want to rejoice. 

When they never compliment me, Lord, I want to rejoice.

When they blame me in season and out of season, Lord, I want to rejoice.

This is an astounding and almost unthinkable “ask,” yet it aligns with the “great reversal” of the Beatitudes, and illustrates the attitudes we might strive for. 

Compared to our typical human values, upside down humbleness and humility in every way.

Value Shifted as Media Morphed from Storytelling to Traffic Monetization

As someone who worked for 40 years in ad-supported media and experienced the harsh realities of the internet, the realities of today’s business are brutal. And that was true before generative artificial intelligence, which is accelerating the underlying economic trends. 

In a nutshell, here’s the business model problem: Many media businesses are no longer primarily “storytelling organizations” but traffic monetization systems. As writers we act as storytellers. But whether that is harnessed financially can often be unclear or unworkable. 

Since advertising “cost per thousand” impressions have collapsed over the last few decades, so have media entity revenues, bringing huge cost pressures to the forefront. 

Platforms such asGoogle, Meta and X have captured distribution and pricing power, driving many former independent or smaller entities out of the market. 

So revenue per article must be less than the cost of human labor to produce that content. So automation becomes a survival move. 

Machines can often produce content abundance, especially when the content has high structure. Humans ideally produce scarcity value when lots of insight, interpretation or “meaning” are required. But generative AI is making inroads there, as well. 

The economics of content production therefore favor using machines to produce mass content at scale, when possible, while humans have the edge only where scarce, specialized or trust-critical content is involved. 

Basically, it is all about marginal cost and associated revenue upside. Investigative reporting might, in some cases, have very-high revenue potential, though it is rare. Original analyses have high production cost, and might have moderate revenue lift. 

Breaking news, data-driven news or automated summaries invariably have low revenue upside. So the choices are fairly simple: automate what does not produce reasonable amounts of revenue, reserving human roles for the more-complicated content that will be a relatively-small part of total content production. 

Content Type	Marginal Cost	Revenue Potential	Economic Role
Investigative reporting	Very high	High but rare	Brand anchor
Original analysis	High	Medium	Subscriber retention
Breaking news rewrite	Medium	Low	Traffic defense
Data-driven updates	Near zero	Low	Volume filler
Automated summaries	~zero	Very low	Search capture

Most user-generated content business models follow a somewhat-similar, but “flipped” pattern. UGC platforms follow the same economic logic (abundance versus scarcity, automation versus humans), but the roles of humans, machines, and “content” are different. 

Abundant, low-value content is automated, while scarce, high-value content is human-produced. 

But users supply the labor for abundance, instead of journalists. The platforms automate selection, amplification, and monetization, so scarcity comes from attention, status, and trust. 

That flows from the differences in media models. Media companies pay to produce content and then monetize the content. 

UGC platforms get their content for free and monetize behavior around that content. In other words, the real product is engagement, not content, per se. So curation is more important than content supply, which is, for all intents and purposes, unlimited. Attention is the source of scarcity. 

Generative AI accelerates the content-creation processes, which becomes even more abundant and lower cost. But the algorithms still curate. So UGC platforms will optimize for watch time; shares; comments or return visits. 

So algorithms will favor emotionally activating content; identity-affirming content or controversial content. That might be likened to “commodity” news, the sort of stuff that, in a professional media context, is structured enough to be automated. 

Top UGC creators, in terms of revenue potential, often provide insights on what matters, what to ignore or how to think about something. They often focus on meaning, which is the same “scarce human” function in professional media.

Ironically, AI increases competition for attention, but raises the premium on human scarcity. That happened with journalism after the internet; music after streaming or photography after smartphones. 

Abundance also tends to make authentic human insight more valuable, not less, even if it remains rare, as surfaced by the algorithms.