Rule of 40 gave the software industry a common language. AI has exposed the need for a new one. Now, the challenge isn't that the market has changed the benchmark. It's that the characteristics increasingly determining software value are difficult to observe through public financial reporting.
I've been having a number of conversations lately with equity analysts and fund managers to better understand what I think is AI's emerging coherence problem for the software industry. There is an increasingly visible disconnect between how software companies are being asked to perform, how they are being valued when they do, and how employees are expected to interpret both. This is not simply a post-2021 valuation correction. Something deeper appears to be happening.
The market is no longer asking only whether a company can grow efficiently. It is asking whether that growth is durable, defensible and structurally advantaged in an AI-repriced world. Yet while equity markets appear to have adopted a new set of expectations, those expectations have not been clearly articulated in as simple a way as R40. So if the benchmark has changed, what is it? That’s what I’ve been wanting to know.
For years, the industry has had a reasonably shared language. We all agreed that growth and profitability mattered. The Rule of 40 became a useful shorthand because it gave boards, executives, investors and employees a way to understand whether a software business was balancing ambition with discipline. Then came Rule of 50, and now even Rule of 60 for the stratospheric performers. But now that language feels insufficient.
Because the problem is not that software companies have stopped executing. It is that the financial market appears to have moved beyond the benchmarks by which that execution has traditionally been measured. This means that companies delivering Rule of 40-plus performance are still attracting significant valuation discounts, while others with comparatively weaker operating metrics appear to be judged against an entirely different set of expectations.
Today a software vendor can deliver strong revenue growth, improve free cash flow, secure high renewal rates and a cleaner cost base, and still take a big hit. The issue is not deteriorating performance, but uncertainty about how AI will reshape the company's future economics.
We have reached the point where if investors cannot confidently assess whether AI will compress pricing, erode product differentiation, shift value towards infrastructure, weaken seat-based business models or fundamentally reshape the economics of software development, go-to-market and customer delivery, they are likely to price that uncertainty into today’s valuation. That has become the default.
Their question is no longer simply, “Is this a Rule of 40 company?”. It is, “What kind of Rule of 40 company is this?” More importantly, what additional characteristics determine whether the market assigns a premium or a discount? Part of that uncertainty stems from the fact that we are still learning where AI genuinely changes enterprise software economics and where it does not.
AI has become remarkably capable of generating components, prototypes and functional pieces of software. Anyone who has experimented with frontier models has seen how quickly a feature, workflow or user interface can be produced through vibe coding or agentic development. But building an enterprise platform is not the same as building a feature.
Enterprise software must still satisfy architectural standards, cybersecurity requirements, governance controls, data models, integration patterns, operational resilience, compliance obligations and long-term maintainability. Generating an isolated capability is one thing. Delivering an entire platform that consistently meets those standards is another.
But until we have stronger evidence about where AI genuinely changes those economics, markets will continue to price future value through uncertainty rather than assumption. Those valuations then become today's management decisions. And that is where I think we need to momentarily leave the financial markets and consider how future expectations are already reshaping the software workforce.
This is a tough time to be inside tech. Inside many software companies, employees are being asked to do two psychologically conflicting things at once. They are being asked to maintain belief, energy and cultural enthusiasm while watching the equity market seemingly discount the very operating performance they were told to deliver. At the same time, they are being told to upskill in AI because AI will be critical to their future employability, while also seeing AI used as the rationale for restructuring, hiring freezes or role elimination.
We have moved past hype to the point where that is no longer a small cultural issue. It is fundamentally breaking the emotional contract between company and employee. Executives can continue to tell staff that the company is executing well. They can point to operating discipline, product innovation, customer wins and AI transformation. But if employees see those gains fail to translate into equity value, compensation upside, job security or organisational confidence, the message becomes harder to sustain. Performance without reward eventually becomes fatigue. And when future uncertainty begins driving today's restructuring decisions, fatigue quickly becomes fear. I hear about redundancies every day now.
In conversations this year, I've also sensed that fatigue beginning to emerge among the leaders of some of the world's largest software companies. Many have spent years executing difficult transformations, improving operations and reshaping their businesses for the AI era, yet still find themselves explaining why sustained execution has not translated into the market recognition they expected. Their challenge is not simply one of execution. It is that the market now appears to be judging them against a benchmark that has never been clearly articulated.
This feels like the point where equity and financial analysts may need to do more than simply apply lower valuation multiples and wait for management teams to infer the new rules. If the market has changed the benchmark, it should be stated more clearly. That creates accountability. It also allows boards, management teams, employees and investors to understand what success now looks like and to judge performance against a shared set of expectations rather than an implied one.
That single shift changes almost everything. The market appears to be supplementing growth and profitability with a broader assessment of the operational characteristics that determine whether those economics are sustainable. In practical terms, the emerging benchmark seems to evaluate software companies on the quality of their growth, the durability of their economics, the defensibility of their AI capabilities and, ultimately, their ability to create better long-term customer outcomes rather than simply generate short-term sales activity.
Those characteristics cannot be observed directly through traditional financial reporting, so investors will have to increasingly look for operational evidence.
They still want to understand where profitability is coming from, whether customers continue to expand their investment over time, and whether pricing power can be sustained. But now also whether AI capabilities create genuine competitive advantage, and whether gross margins will remain resilient once AI infrastructure costs are fully accounted.
Increasingly, they are also paying attention to measures like sales efficiency, implementation velocity, and product-led adoption that were once considered secondary. We could throw a blanket over all these and just consider whether customers are able to achieve better outcomes with less friction. This remains one of the biggest unanswered questions in software today.
This is where the work of industry analysts has always differed. Beyond the polished demonstration and the marketing campaign sits the much harder questions of execution. How quickly can customers adopt the technology? How successfully can they implement it? And do they ultimately achieve the outcomes that were promised? Helping organisations answer those questions has long been one of the defining roles of industry analysts. In doing so, we have spent decades evaluating the operational realities that sit between a vendor's promise and a customer's outcome. Those are now becoming some of the defining questions for investors as well.
Put another way, the two disciplines of equity and industry analysts are beginning to converge. Equity analysts largely explain what happened to a company's economics through its reported financial performance. Industry analysts have traditionally explained why those economics happened by observing product maturity, implementation reality, customer adoption and competitive execution.
As software valuation shifts more towards operational capability rather than purely financial efficiency, those two perspectives become increasingly complementary. The reason is that the market is beginning to distinguish between companies that reduce costs by genuinely improving the business through AI, and those that reduce costs by simply redistributing effort, complexity or risk elsewhere in the value chain. That distinction has long sat at the heart of industry analysis. We have always been interested not simply in whether a product could be sold, but whether customers could successfully implement it, adopt it and realise value from it.
In the AI era, productive efficiency is becoming more valuable than simple efficiency. The market is no longer asking only how efficiently software companies generate revenue. It is increasingly asking whether AI is removing friction from the system or merely moving it. Ultimately, that means asking how efficiently software companies create customer success.
That distinction becomes much easier to understand through a practical example. One emerging assumption in software markets is that reducing the cost of sale will become an important part of the new equity story. On a spreadsheet, or a boardroom whiteboard, that makes perfect sense. If AI can reduce the cost of demos, prototypes, solution design and technical validation, software companies should be able to lower sales and marketing expense, improve margins and shorten sales cycles. Job done. And to be fair, I have seen some genuinely impressive applications of AI in this area. But by the same token, we’re yet to answer the most important question about what happens to those productivity gains?
When Efficiency Runs Ahead of Evidence
If reduced cost of sale is reinvested into better onboarding, higher-quality implementation, stronger customer success, deeper technical assurance, and faster time to value, then the customer may benefit and the valuation case strengthens. But if reduced cost of sale is simply extracted as margin, while customers receive less expertise, thinner technical engagement and more automated pre-sales theatre, the long-term outcome is less clear.
The question is whether the industry is beginning to optimise a system before it fully understands which parts of that system actually creates customer value. This is particularly important in the case of solution engineers (SEs) but applies equally to anyone with a Sales Engineer, Solutions Consultant, Pre-sales Engineer or Solutions Architect title.
Replacing SE capacity with account executives supported by frontier models or agentic demo tools may seem like a valid financial step. It certainly reduces headcount and cost. It may accelerate prototype creation. It may create the impression of greater sales productivity. And for simpler products or transactional buying motions, it may prove highly effective.
One of the canonical stories emerging from the AI industry is the account executive who uses Claude to listen to a customer’s requirements and produce a working prototype before even leaving the first sales meeting. Whether every version of that story is true is almost beside the point. It has become the example repeatedly used to illustrate how dramatically AI is compressing the time between customer discovery and solution creation. And it is an impressive demonstration of technological capability. But it is not yet a substantial body of evidence that enterprise buying has fundamentally changed.
Enterprise platform sales have never been about producing the best demonstration. They have been about helping customers understand how a technology fits their architecture, operating model, governance, security requirements, implementation approach and long-term business objectives. The solution engineer has traditionally played a critical role in translating product capability into organisational change.
What we have today are compelling anecdotes. What we do not yet have is robust evidence that replacing experienced solution engineers with AI-assisted account executives consistently improves customer adoption, implementation success, time to value or long-term commercial outcomes. Reducing the cost of sale is only one part of the equation. If those savings are offset by slower adoption, higher implementation costs, greater customer success effort or lower long-term retention, the economics become far less compelling.
So the real challenge is not demonstrating that AI can produce a better demo. It is demonstrating that it produces a better customer outcome. Good SEs do that. They don’t merely show software. They translate architecture and operating model fit being considerate of constraints and customer maturity. They are often the people who prevent a sales cycle from becoming detached from delivery reality. So whereas an AE with an agentic SE assistant may be faster, faster is not the same as more qualified.
The risk we’re staring down at the moment is that software companies remove human expertise from the sales process at precisely the moment customers need more help understanding how AI, platforms, data, identity, workflow and governance fit together. That may improve near-term sales efficiency (margin), but it could also weaken customer outcomes (margin), increase implementation disappointment (margin) and ultimately damage retention (margin).
The question isn't whether AI can change enterprise software sales. It almost certainly already has. The question is whether organisations are redesigning those sales motions faster than they are generating evidence that the new model produces better customer outcomes. It’s hard to argue it does over the same short-term forward horizons.
So where have I landed after all my discussions? I think the equity analysts are absolutely right to ask harder questions. The old valuation models did not fully account for AI disruption, seat compression, infrastructure cost, product commoditisation or the growing fragility of software moats.
I think industry analysts are equally right to focus on customer outcomes, implementation reality, organisational readiness and the distinction between capability and adoption. Demonstrating that AI can do something has become relatively easy. Demonstrating that customers can consistently adopt it, govern it and realise value from it at scale is considerably harder and will be beyond many protagonists.
And I think operators are right to pursue efficiency. But efficiency only creates long-term value if it improves the business rather than simply shifting effort, complexity or risk somewhere else in the customer lifecycle.
Each perspective is valid. The problem is that they are still being assessed through different lenses. I return to the missing piece being a shared benchmark that connects financial performance, operational execution and customer outcomes.
The next software valuation model can’t simply reward lower cost of sale or lower cost to serve, though these are very important. It should reward productive efficiency via the ability to grow, expand, implement and retain customers with less friction and better outcomes. And this is where the industry analyst perspective becomes critical.
Financial analysts can observe the economic outputs. Industry analysts are often closer to the operational inputs that create those outputs. One explains what happened. The other helps explain why it happened.
We see how a vendor’s sales pitch aligns with customer implementation reality. We hear whether products are easier to adopt, whether customers are reaching value faster, whether partners are carrying more delivery risk, and whether support demand is falling because the product is better or because support has been reduced.
Reduced cost of sale should therefore not be treated as inherently positive without further interrogation. Are customers going live faster? Are they expanding faster? Are support needs falling because the product has improved, or because support has been cut? Are SE reductions improving scalability, or merely pushing technical risk downstream into delivery, partners and customer success?
The real question is not whether the vendor, through AI, has become cheaper to operate. It is whether the vendor’s solution, through AI, has become easier to buy from, easier to implement, easier to adopt, easier to service, and easier to expand.
So the market is right to move beyond Rule of 40. The challenge is that the characteristics increasingly determining software value are not readily observable through public financial reporting.
Rule of 40 democratised software valuation because anyone with a company's published financial statements could calculate it. The emerging AI benchmark is fundamentally different. Many of the characteristics now driving premium or discounted valuations are operational rather than financial. They are often difficult to observe from outside the business, forcing investors to infer them. And when markets cannot directly observe what matters most, they tend to price uncertainty conservatively.
So until investors can better observe those drivers, markets will continue relying on imperfect financial proxies. Companies will manage to largely unstated expectations, and employees will struggle to understand why strong execution is not translating into value creation. But perhaps, we can say with most certainty, Rule of 40 gave the software industry a common language, whereas the AI transition has exposed the need for a new one.
And until we become better at observing the operational characteristics that now determine software value, investors will continue pricing uncertainty, management teams will continue executing to largely unstated expectations, employees will continue questioning whether performance is being recognised, and customers will (almost certainly) ultimately absorb the hidden cost of margin improvement. The challenge is making what matters visible.
My own contribution is this. Firstly, high growth and strong margins remain necessary. Of course they do. They are simply no longer sufficient.
Secondly, the market appears to be placing greater value on operational capability rather than simply financial efficiency. That naturally raises the new question as to where should that capability become visible?
I think the answer is that every release, every product investment and every AI capability should systematically reduce friction across the customer lifecycle. Is AI making the company easier to buy from, easier to implement, easier to adopt, easier to serve, easier to trust and easier to expand? Those are questions we can already begin to assess, even if we cannot yet measure them consistently from public financial reporting.
That leads to a simple conclusion. If two companies deliver identical Rule of 50 performance, but only one can demonstrate that AI is systematically reducing customer friction without increasing customer risk, they should not command the same valuation. That difference is precisely what the next generation of software valuation needs to capture.
About Councilio
Councilio provides independent technology analysis, executive briefings, keynote speaking, workshops, and strategic consulting and advisory services.
Learn more: councilio.co