Inflated ARR in AI startups: how to spot it

The context: why ARR has become the iconic AI metric

In the tech startup funding cycle, ARR — Annual Recurring Revenue — has been the main indicator of financial health for years. However, in the AI segment, this metric has taken on disproportionate weight. Investors use it to compare companies in different stages. Founders communicate it as proof of traction. The media picks it up to build growth narratives.

Therefore, ARR has become a positioning tool even before a measurement one. In particular, in AI startups, where sales cycles are still immature and contracts are often experimental, the pressure to show high numbers is very high. As a result, some players have started to interpret the definition of ARR in a creative way.

According to a TechCrunch investigation from May 2026 , founders and venture capitalists are fully aware of this practice. Therefore, these are not unintentional accounting errors, but conscious narrative choices.

The numbers that matter: how an ARR gets inflated

There are various techniques through which an AI startup can present an ARR higher than the real one. First of all, it is useful to understand them in detail in order to recognize them.

• Annualizing pilot contracts: a three-year contract for 30,000 euros is declared as 10,000 euros of ARR, even if renewal is not guaranteed.
• Including cloud credits: free credits offered by AWS, Google Cloud, or Azure are accounted for as actual revenue.
• One-off revenue reclassified: onboarding or initial consulting payments are spread out annually and included in the ARR.
• Conditional contracts: agreements subject to milestones or exit clauses are included in the total as if they were confirmed.
• Pipeline like ARR: in some cases, late-stage sales opportunities are lumped into the actual numbers.

Plus, some startups distinguish between committed ARR and run-rate ARR without making the difference explicit in public communications. Similarly, the term "ARR" is used interchangeably with "annualized revenue", which is a different concept.

According to research by Gartner on AI startup valuation metrics , the lack of shared standards in defining ARR is one of the main drivers of opacity in the industry. As a result, there is no body that certifies the consistency of these numbers before publication.

Strategic take: why investors let it slide

A key point highlighted by the investigation is that venture capitalists are not clueless victims of this dynamic. Instead, they often actively encourage or tolerate it. The reason is structural.

A fund that has invested in an AI startup is interested in that startup being perceived as a market leader. Therefore, a high ARR — even if built on shaky foundations — helps attract new investors in subsequent rounds, increases valuation, and facilitates more profitable exits. Consequently, the incentive to correct the narrative is weak.

Furthermore, in the 2026 AI market, perceived growth speed is often more important than the solidity of fundamentals. This creates an environment where those who communicate more conservative numbers risk being penalized in capital raising, even if their financial position is stronger. Thus, this is a systemic problem, not an individual one.

An analysis by Harvard Business Review on AI startup metrics had already highlighted last year how the competitive pressure on funding rounds is distorting the quality of information available to external stakeholders.

Operational implications for Italian B2B SMEs

For an Italian SME operating in the B2B or retail sector, the inflated ARR problem has direct practical consequences. In fact, many companies find themselves evaluating AI startups as potential tech suppliers, business partners, or emerging competitors. In all these cases, the reported ARR influences the perception of reliability and soundness.

However, choosing an AI provider based on inflated ARR means relying on a company that might have a much more fragile customer base than it appears. Consequently, the risks are multiple: service discontinuity, renegotiation of contractual terms, difficulties in post-sales support.

We at SHM Studio We notice that this topic is still not well covered in Italian SMEs. Often, technological purchasing decisions are made by evaluating brand recognition or numbers communicated in the media, without a critical analysis of the underlying metrics. Therefore, developing an internal capacity for critical reading of AI startups' financial data is a strategic competence today.

In particular, we suggest considering the following operational criteria when evaluating an AI partner:

• Ask for the explicit definition of ARR: a solid vendor can explain exactly what is included in their reported ARR.
• Check the number of active customers: a high ARR concentrated in just a few clients is much riskier than one spread across a broad base.
• Analyze the churn rate: the percentage of customers who do not renew is a more reliable indicator of business health than raw ARR.
• Request verifiable references: documented use cases with companies comparable in size and sector are more informative than any aggregated metric.
• Evaluate the contract structure: prepaid annual contracts show a different solidity compared to monthly or conditional contracts.

The work in progress: toward more transparent standards

The issue of inflated ARR is not likely to resolve itself in the short term. In fact, as long as the capital market rewards perceived growth over real solidity, the incentive to communicate optimistic numbers will remain structural. However, some signs indicate that the context is evolving.

On one hand, institutional investors and private equity funds are starting to demand greater granularity in revenue metrics during due diligence processes. On the other hand, some standardization initiatives — such as those promoted by SaaS industry organizations — are trying to define shared guidelines for ARR reporting.

Furthermore, with potential European regulations on financial transparency for startups approaching, it's likely that by 2027-2028 we'll see increased pressure towards more rigorous standards. Despite this, in the short term, critical reading skills remain the only tool available for those operating outside the scope of institutional investors.

To delve deeper into the critical evaluation of AI tools for SMEs, it is also useful to consult the analyses of Wired on the metric issues in AI companies .

How SHM Studio helps SMEs with tech assessment

We at SHM Studio we support Italian SMEs in building a solid digital strategy, which also includes the critical selection of tools and tech partners. Therefore, the topic of evaluating AI startups falls right within our scope AI consulting services .

In particular, when we support a client in choosing AI-based solutions, we adopt an approach that goes beyond evaluating functionalities. In fact, we analyze the supplier's solidity, the contractual structure, the product roadmap, and the consistency between market promises and operational reality. This approach integrates with our activities of Digital marketing and SEO , where the quality of the tools you use has a direct impact on results.

Additionally, for companies that are building or optimizing their digital presence, we offer support in defining content strategies through our services of SEO copywriting , in managing google ads campaigns and of LinkedIn campaigns . Finally, for those looking to upgrade their web infrastructure, our web services also include consulting on choosing the most suitable tech platforms.

For a direct discussion on your company's specific needs, you can contact us through our website. Also, to stay updated on digital sector analyses, we recommend following our Blog .