Software Is Dead. Long Live Software.

The market is pricing in an apocalypse. The reality is more nuanced.

SaaS software stocks have declined significantly since Oct 2025 amid broader concerns that software is in decline, disrupted, displaced, and replaced by AI. Companies will use AI to redesign and unbundle their workflows over time, and the markets are effectively pricing in a software apocalypse.

The selloff has been almost indiscriminate, and the market is overly pessimistic.

As expected, faster-growing companies tend to exhibit greater price volatility. This is because most of the present value lies in the future. Any change in future expectations by Mr. Market can lead to significant fluctuations between ecstasy and agony. In this case, a substantial downward revision of future expectations has led to a significant drawdown in stock prices.

Let us return to first-principles thinking. The internet significantly reduced the marginal costs of distribution. AI is considerably reducing the marginal costs of creation. For most of software history, code execution was scarce. Code development was slow, costly, and constrained by the use of expensive human code developers. AI is accelerating code production, making it cheaper and enabling a much larger volume. While software costs are inching down, they are not collapsing overnight (yet).

When the cost of writing and maintaining code drops, companies start evaluating whether specific internal workflows, edge cases, or domain-specific processes that were previously too expensive or fragile can be automated. Software is not a technology barrier, it is a business knowledge barrier.

Software is a digital tool. It does not make sense to keep reinventing tools (e.g., a calculator or a hammer). If there are new tasks that have not yet been automated and can now be automated with software, now is the best time. Software is a TAM accelerator, and companies can create new and more products in shorter time frames.

The future appears to be agentic, with agents constituting the new digital workforce for humans, working for us and with other agents on exploratory, low-value, and repetitive tasks, thereby allowing us to focus on higher-value creative and strategic tasks.

The fact that everyone has a pen or a keyboard does not mean that we will have a rush of great writers, authors, or coders. The best work will still be done by the select minority, not the vast majority. Writing code is easy. Shipping a basic V1 is just 1% of the work. 99% of building enterprise software is about writing code that actually works and keeps working, maintaining it, iterating on it, securing it, and scaling it, and that is where the real difficulties lie. Vibe coding might be incredible for prototypes, internal tools, and new products, but it is not replacing a proven tool.

It is the same with AI. It does not mean that, if one can code faster with AI assistants, one can write great code or develop a great product. It still requires deep understanding, intent, judgment, and taste. And that’s where the bottleneck lies. Try getting a first-year coder to “vibe-code” and build a massive CRM database, and you will soon realise that it is not as easy. Automation scales whatever structure already exists. Agents tend to work best when intent is explicit and stable, and struggle when it is implicit and judgment-intensive.

SaaS is heterogeneous, not homogeneous. One cannot simply be lazy and lump everything into a single category of thought. The idea that enterprises will dump all software to “vibe-code” their own software with AI agents is wildly optimistic. Larger, more complex SaaS platforms with substantial codebases, deep workflows, extensive API connectors/regulatory licenses, strong network effects, and extensive hardware infrastructure are likely to be more insulated.

Deterministic systems where precision is critical, non-negotiable, requiring it to be 100% all the time, are more likely to be more insulated, as “close enough” is simply unacceptable. Probabilistic systems, conversely, tend to tolerate some errors and accept good-enough performance, and are primarily focused on pattern recognition, content generation, basic automation, and simple decision-making. If an LLM can replicate your probabilistic product with 90% of the quality at 10% of the cost, you are likely not to have a sound business model any longer. Even having a great UI or UX won’t save you.

High-value, mission-critical, must-have software is likely to be more insulated than low-value, non-mission-critical, good-to-have software. Functions such as cybersecurity, payments, and infrastructure are likely to remain robust. Because when these go down, the business stops. Customers should continue to be willing to pay premium prices for quality and peace of mind, remain highly sticky, and rarely switch because the cost of failure is too high. They tend to have high gross retention (customers don’t leave), high net retention (customers spend more over time), and are willing to pay more as their business grows.

The problem of software sprawl continues. Enterprises are increasingly using too many SaaS applications. While they made sense when purchased individually, they have since created increasing chaos, fragmented data, security gaps, and ballooning costs. Companies are increasingly moving away from single “best of breed” to “best of suite” consolidators that offer a broad, growing product portfolio, and these should continue to outperform single legacy solutions.

Outcome/usage/value-based pricing will increasingly dominate seat-based pricing business models. Seat-based pricing assumes that value scales with headcount, revenue, and seats. But AI breaks this assumption completely. Employees using AI tools and agents are significantly more productive and can do more work, so companies no longer need as many seats as before. In addition, SaaS companies that continue to raise prices without delivering additional customer value will begin to struggle as customers consider switching or even attempting to replicate the service themselves.

Smaller startups and SMBs with low legacy data gravity, integration web, and switching costs are more likely to be early adopters than large enterprises when experimenting with building rather than buying. Larger enterprise customers seek greater insulation, preferring to buy over build, as they cannot risk failure and often require primary and backup providers. They would like strong, established, and proven vendors to handle these functions, allowing them to focus on their core business of producing products and services for sale.

Horizontal SaaS consolidators are safer than vertical SaaS specialists. Most AI builders are now focused on developing vertical AI products rather than horizontal AI products and platforms, and this trend has accelerated in recent quarters. It is far easier to go for the lower-hanging fruit in more niche, custom, specialized vertical SaaS with less competition than to go up against the top dogs in horizontal SaaS.

Many vertical-market software (VMS) companies, especially PE-backed ones or those acquired by serial acquirers, are optimized for extraction rather than innovation. Structurally low reinvestment rates are a feature, not a bug. Their playbook flywheel is simple: acquire VMS, reduce R&D, raise prices, harvest cash flows, and acquire more. Rinse and repeat until they grow large enough that acquisitions become more difficult and costly, and valuation multiples compress. VMS customers often stay because switching is painful, not because the product is great (old and legacy).

Disruption by AI should vary across industry verticals in vertical SaaS, depending on the complexity of regulatory knowledge, industry workflows, and compliance requirements. The domain expertise moat could begin to erode, as these AI builders can engage industry domain experts to develop a good-enough product that delivers 80-90% of the value at 10-20% of the cost. For many of these smaller companies, that might just be enough.

Dynamic over static. SaaS companies that continuously seek to improve their products/services and add more over time, and that embrace AI wholeheartedly and use it to enhance their offerings, are likely to be more resilient than those that are slower or more reluctant to do so.

Longer over shorter. SaaS companies with longer multi-year contracts, rather than shorter monthly/annual contracts, would have higher switching costs, greater visibility, and higher customer retention, allowing them to be more strategic about their product innovation cycles.

The market sees a software apocalypse. We see software Darwinism. The fittest will survive, consolidate, and thrive. SaaS is heterogeneous, not homogeneous.

Overall, most, if not all, of the SaaS companies we own exhibit the preferred traits we highlighted. The selloff could well persist, but we think the market is overreacting. We recently added to several of our existing positions during this selloff, which we believe was oversold. We would be happy to deploy any additional capital we receive into what we consider long-term, attractive opportunities.

The information contained in this article is for general informational purposes only. While every effort has been made to ensure the accuracy and reliability of the content, errors or omissions may occur. The author and publisher do not assume any responsibility or liability for any errors, inaccuracies, or omissions in the content, nor for any actions taken based on the information provided. Readers are encouraged to verify any information before relying on it and to seek professional advice as needed.

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05 Feb 2026 | Eugene Ng | Vision Capital Fund | eugene.ng@visioncapitalfund.co

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