What are the most and least promising categories to invest in, within AI software and prosumer tools?

The most promising investment categories in AI software are those targeting prosumers and specialized verticals with high-value monetization models. A paradigm shift is underway from low-cost or ad-supported software to high-margin, utility-focused products that "do work" for users, justifying premium prices of **$200-$250/month** [1, 18]. This is evident in the divergence between the most-used and highest-grossing AI applications, indicating that highly profitable businesses can be built by serving niche, high-intent verticals rather than pursuing mass-market scale alone [6, 12]. Promising areas include specialized, best-of-breed tools for modalities like video, music, and voice that offer unique capabilities or aesthetics not easily replicated by general foundation models [13, 16, 27]. Additionally, tools that empower non-technical domain experts and developers to create software represent a significant and growing market, blurring the line between prosumer and consumer categories [3, 24]. The common thread is a focus on clear, measurable ROI, such as saving expensive engineering hours, which is becoming a prerequisite for enterprise adoption as the market matures beyond experimentation .

A central tension exists between targeting greenfield opportunities and disrupting established incumbents. Some experts advocate for creating entirely new software categories in verticals with unstructured workflows and no dominant players, such as legal, healthcare, and wealth management [2, 10]. This approach avoids direct competition with software giants. Conversely, others see a significant opportunity for AI-native challengers to directly disrupt large, incumbent-dominated markets like sales and marketing intelligence . This strategy faces headwinds, as experts like Ben Horowitz caution that displacing companies like Salesforce and SAP is **extremely difficult** . This debate occurs amid a broader negative market sentiment toward traditional SaaS, with fears that AI's ability to write code could commoditize existing subscription services [5, 9, 14, 28]. The most vulnerable incumbents are those with no proprietary data, weak moats, and no regulatory overlay .

Investing in AI infrastructure—the "picks and shovels" of the ecosystem—is another promising thesis, as these companies provide essential building blocks that are less likely to be commoditized by foundational models . This category is considered bullish because its constituents are seen as prime acquisition targets for tech monopolies and their work often aligns with national security priorities . This optimism for AI-native infrastructure, however, does not extend to traditional infrastructure software companies, which are reportedly struggling to raise capital . The value of the infrastructure layer is reinforced by market dynamics at the application layer, where general-purpose LLM assistants are trending toward a winner-take-most outcome and broad image generators are being commoditized . This bifurcation pushes value capture to the foundational infrastructure providers and the highly specialized, defensible applications built on top of them.

The overall AI market is characterized by explosive, non-zero-sum growth, with value being created and captured at every layer of the stack . The consumer landscape is highly volatile, with new entrants like DeepSeek rapidly challenging established leaders, proving the market is far from mature and assumptions are constantly being broken [6, 30]. Despite this flux, a core group of **16 companies** has demonstrated durable product-market fit by consistently remaining on top GenAI product lists . While opportunities abound, some investors warn that the odds of achieving a 100x return on new investments are now very low, as the most successful bets were placed before the current hype cycle . Emerging modalities like AI video and voice represent the next frontier, but investors should be cautious of categories like AI-native social platforms, which have struggled to build consumption habits even when their creation tools are successful [6, 18, 26].