What is the future of B2B software applications in the post-AI era?
The current phase of AI, as evaluated by the Gartner Hype Cycle, is a useful first step in the analysis. Are we in the “Inflated Expectations” phase or the “Slope of Enlightenment” phase?
There are strong arguments on both sides.
Engineering teams are coding ~55% faster, doctors are spending ~40% less time on documentation, customer support teams have decreased average call-handling time by 35%, drug development teams have accelerated competitive-intelligence insights by 30%, and insurance teams have reduced claim-processing and underwriting time by 40% each. Impressive advancements in such a short amount of time.
Anthropic reported annualized revenue increasing 3× in five months. Cursor hit ~$500M ARR within three years of founding, and Lovable reached ~$100M ARR eight months after launch.
Better data, tighter workflow embedding, stronger distribution loops, and faster iteration from real-world usage allow first movers to skip the hype cycle and win in the long term.
When the price-to-earnings ratios of the top 10 companies separate significantly from the rest of the S&P, it’s often a signal that market gains are being driven by a very narrow group of stocks. Historically, a market correction follows shortly thereafter.
Of course, these companies are led by experienced founding teams with excellent track records, which largely drive the valuations. While serial successful founders often attract a premium valuation, these figures are at an exponentially new level.
Data, skills, and scaling barriers stand in the way of AI success, most AI projects stall at the pilot stage, and those projects that deploy to production fail to meet ROI expectations.
The headlines state:
However, looking under the hood finds:
The Application Layer appears particularly vulnerable. Industry analysts note that AI isn’t a single market. It’s a stack. And “bubble risk,” moat potential, and incumbent staying-power vary dramatically by layer, yielding different positions on the hype cycle.
Many of these segments face the traditional challenge of competing against well-established incumbents. However, the application layer presents two additional challenges:
Foundational models are moving up in the stack to the application layer, such as offering general agents for task completion and more specialized tools for coding. Expect much more in the year ahead.
CIOs are choosing a “build” over a “buy” strategy for their internal systems. These executives fostered a “bring your own device” and “bring your own app” culture over the last decade. This created SaaS sprawl, resulting in a disparate system of applications and data that is difficult to gain insights from. Because the “AI-our-organization” mandate has been delegated to the Office of the CIO, the Office is learning from past mistakes and prefers to run as much as possible on foundational models and to aggregate data into a centralized data lake. The hyperscalers are aligned and helping their customers with this transition.
The smaller nature of vertical AI total addressable markets (TAMs), in this case, provides a potential advantage toward building a sustainable business. Historically, the most successful vertical software businesses have achieved market valuations in the billions, and sometimes in the low tens of billions. However, vertical software is less attractive to foundational models than horizontal or functional software, with exponentially higher TAM opportunities. Furthering the case are the more nuanced product requirements seen in vertical software required to meet specific regulatory compliance requirements. Why pursue a smaller TAM with more complex, less transferable product requirements?
The same trend applies to venture investors. In recent years, most of the venture capital has been deployed by the top 20 firms, many of which are managing multi-billion-dollar funds for the first time. While a 10% ownership stake in a $5 billion exit can generate a 5x return for a $100 million fund, it doesn't significantly affect the returns for multi-billion-dollar funds. These larger funds need to concentrate on major horizontal or functional investments, which leads to heightened competition and inflated valuations for a limited number of deals.
CIOs at tech companies may possess the talent and tech stack required to support a "build" mentality. In contrast, CIOs in sectors such as financial services, manufacturing, and healthcare often prefer vendors equipped to meet their industry-specific requirements, including compliance and regulatory standards.
Many companies receive sizeable valuation multiples today due to first-mover advantage. The thinking is as follows:
This is logical thinking. However, many studies do not show that first movers win more often than fast followers. Anecdotally, reflecting on the early Internet era:
It's challenging to find a first mover that successfully secured long-term market dominance. First movers face several disadvantages compared to early followers.
If these dynamics apply to the AI era, the names that dominated the press and fundraising scene over the last two years will likely end up more like Pets.com, Webvan, and AOL, and not Google, Amazon, and Meta.[7]
It’s early, so who knows. However, combining observations around today’s context and historical behavior in past, similar situations yields the following:
1. Boulding, W. and Christen, M. (2008), “Disentangling Pioneering Cost Advantages and Disadvantages,” Marketing Science
2. Ozalp, H., Cennamo, C., and Gawer, A. (working paper, 2022), “First mover, Fast Second or Later Mover in Platform Industries and: Berger, J. et al. (2019), “Don’t be first! An empirical test of the first-mover disadvantage using a TV game show,” Journal of Behavioral and Experimental Economics.
3. Suarez, F. et al. (2022), “Early bird or early worm? First-mover (dis)advantages and the timing of entry in innovation contexts,” Technological Forecasting and Social Change.
4. Hartmann, P. et al. (2016), “Evolving user needs and late-mover advantage,” Industrial and Organizational Psychology / related work as indexed in PubMed Central
5. Chen, J., Chen, J. S., and Posen, H. (2023), “Uncertain Learning Curves: Implications for First-Mover Advantage and Knowledge Spillovers,” Academy of Management Review
6. Neosfera. (2025). Why your $10M valuation is actually a trap: How overvaluation quietly kills startups [Blog post]. Neosfera Insights.
7. Lieberman, M. (2007), “Did First-Mover Advantage Survive the Dot-Com Crash?” analyzing 46 Internet markets and 200+ public dot‑com firms.