AI applications and market potential across various industries

From: redpointai

The artificial intelligence (AI) landscape is undergoing a significant transformation, with projections indicating a massive market expansion and a shift in how value is created and distributed. This shift is driven by strategic investments in hardware and the emergence of new AI applications that are redefining market dynamics.

Market Projections and Investment Landscape

The projected revenue from AI is staggering. The NVIDIA data center division, which produces the Graphics Processing Units (GPUs) essential for AI training and inference, is projected to generate $177 bi ll i o n, m ore t han f i v e t im es t h ere v e n u eo f I n t e l^{'} sc l i e n t co m p u t in gg ro u p, w hi c h p o w ers p erso na l co m p u t ers < a c l a ss = " y t - t im es t am p " d a t a - t = "00 : 01 : 47" > [00 : 01 : 47] < / a > . T hi ss u b s t an t ia l c a p i t a l e x p e n d i t u re b yco m p ani es in N V I D I A GP U ss ugg es t s anan t i c i p a t e d re v e n u e g e n er a t i o n o f a pp ro x ima t e l y$ 1.2 trillion by 2030 and $1.5 t r i ll i o nb y 2032 f ro m t h e [[A I a ppl i c a t i o n s in s p or t s ana l y t i cs ∣ A I l an d sc a p e]] < a c l a ss = " y t - t im es t am p " d a t a - t = "00 : 02 : 26" > [00 : 02 : 26] < / a > . T o p u tt hi s in p ers p ec t i v e, t h ee n t i re g l o ba l e n t er p r i seso f tw a re ma r k e t, w hi c hha s d e v e l o p e d o v er 50 ye a rs, i s v a l u e d a t ab o u t$ 1.1 trillion [00:03:00].

Many technology leaders, including Benoff, Gates, Bezos, and Eric Schmidt, express “incredible excitement and exuberance” for what AI can bring, suggesting that these market projections might even be “understated” [00:03:27]. Investment in AI is increasingly seen as a “strategic imperative” for companies, regardless of immediate Return on Investment (ROI), to avoid being left behind [00:04:16].

From “Software as a Service” to “Service as Software”

A significant shift is occurring from “software as a service” to “service as software,” where AI is not merely making humans incrementally more efficient but is “doing the job of a human” [00:04:36]. Labor budgets are often an order of magnitude larger than traditional software budgets. For example, the customer service software market is roughly $35 bi ll i o n, w hi l e t h e h u man cos t a ssoc ia t e d w i t h c u s t o m erser v i ce i s a pp ro x ima t e l y$ 450 billion [00:04:58]. This represents a massive untapped market for AI to unlock significantly larger budgets [00:05:11].

Furthermore, AI is penetrating markets historically “under-penetrated” by software due to their small size or the unsophisticated nature of their users. AI is expanding these markets and creating entirely new ones [00:05:37].

The AI Landscape: Models, Infrastructure, and Applications

The AI landscape can be conceptualized in three layers:

Model Layer: This layer includes foundational models like Large Language Models (LLMs) that serve as the “brains” for AI capabilities [00:06:10]. The value of these model companies lies in the products that can be built on top of them [00:06:52].
- Developments:
  - Companies like OpenAI are moving up the stack by shipping products like deep research and enterprise AI agents [00:06:58].
  - Building a cutting-edge foundation model allows for the creation of unique products requiring state-of-the-art models [00:07:03].
  - The cost of entering the state-of-the-art LLM game is prohibitively expensive [00:07:22].
  - Adjacent model categories beyond LLMs, such as those for robotics, biology, and material sciences, are of significant interest [00:07:29].
- Commoditization of Models: The DeepSeek announcement highlighted that models are becoming cheaper, with costs for inference and training dropping about 10x per year [00:08:13]. This is beneficial for application companies as it improves margin structures [00:08:27]. Scale is not an enduring moat for model companies; they will need to build moats through distribution (like OpenAI moving into apps and agents) or specialization (like robotics) [00:08:36]. Switching costs between models are very low [00:09:36].
Infrastructure Layer: This layer consists of the “picks and shovels” that bridge the gap between models and application vendors, providing tools for developers to build AI applications [00:06:19].
- Challenges: The rapid pace of change in the model layer means building patterns for developers also change quickly [00:10:59]. Early AI adoption focused on “use case discovery mode” using powerful, branded models like OpenAI and Anthropic [00:11:11].
- Future: This year (2024) is seen as promising for infrastructure, with the emergence of agents expected to create common patterns for web access and tool usage [00:11:37].
Application Tier: This tier comprises companies building horizontal and vertical Software as a Service (SaaS) solutions that leverage AI capabilities to replace services with software [00:06:28].
- Opportunity: The application space has seen an “explosion” of companies building AI-powered applications to deliver differentiated experiences [00:12:19].
- Disruption: The advent of AI brings a “business model change” where companies can charge for “work rather than for a seat” [00:13:38]. This creates a moment of disruption similar to the early days of SaaS [00:13:42].

AI Applications Across Industries

Horizontal Applications

AI-native solutions are emerging to compete with established horizontal SaaS giants. Examples include:

Adio, targeting HubSpot or Salesforce.com with an AI-native vertical CRM [00:13:56].
LevelPath, competing with Coupa using an AI-native approach [00:14:06].

While incumbents are strong, well-capitalized, and also embracing AI, startups can gain an advantage through “speed” and business model disruption. Large companies face challenges in abandoning existing SaaS pricing models and re-engineering legacy systems to incorporate AI effectively [00:14:32]. The more a workflow changes due to AI, the greater the opportunity for new entrants [00:33:30]. For instance, AI can entirely eliminate complex routing engines in customer service by making better, data-informed decisions [00:32:51].

Vertical Applications

There has been a “Cambrian explosion” of vertical AI SaaS businesses, with 500-600 companies started in recent years [00:15:19]. These companies target verticals that previously lacked compelling SaaS solutions [00:15:26].

When evaluating AI use cases with product market fit in vertical markets, key questions include:

Effective Wedge: Is there a meaningful and “flying off the shelves” product [00:16:15]?
Scalability: How much more can the company do beyond replacing one Full-Time Equivalent (FTE)? Focus is on large industries like healthcare, law, and finance, where the prize is significant [00:16:36].
Quality Imperative: Does quality matter significantly in the target industry? In regulated industries like healthcare or corporate law, a 100% quality bar is often preferred over an 80% good solution, preventing a “race to the bottom” [00:17:01].

AI can unlock labor budgets, making traditionally smaller markets more attractive [00:17:25]. This has led to larger Average Contract Values (ACVs) for some companies by replacing human labor [00:17:41]. However, many of these 500+ companies will likely see initial growth but “top out” as markets become crowded with similar offerings [00:18:13].

Historically, the SaaS market saw clear market winners accrue a disproportionate share of enterprise value [00:20:35]. It is unclear if the AI market will follow a “winner-take-most” scenario or remain highly fragmented, given that much of the value is created by underlying LLMs that many companies can leverage [00:20:50].

Challenges and Considerations for AI Companies

Product Depth and Moats

Successful AI companies demonstrate significant product depth that is challenging to replicate. This often means their solution is more heavily weighted towards workflow (e.g., 80% workflow, 20% model) rather than being primarily model-dependent [00:23:14]. The “moat” for AI apps often comes from “a thousand little things” like user experience (UX), product breadth, and overall user experience, similar to traditional SaaS products [00:24:41].

Velocity and First-Mover Advantage

“First mover advantage matters” significantly in AI, with companies quickly becoming synonymous with a category within six to nine months [00:23:44]. This rapid velocity allows them to become a default choice for customers, attracting partnerships and capital [00:24:06]. However, being first is not always the ultimate determinant of success, as seen with Google in search and Facebook in social networking [00:25:10].

Expertise and Founding Teams

While AI expertise (e.g., former DeepMind or OpenAI researchers) was initially sought after, it’s becoming less critical than understanding the pace of model development and being able to adapt quickly. The ability to integrate new reasoning models is more important than building them from scratch [00:26:00]. Domain expertise matters for understanding end-user problems, but the AI market is more “democratic,” allowing new entrants to engage with industry leaders more easily [00:26:44]. The ideal team often combines both strong building capabilities and founder-market fit [00:22:31].

Durable Revenue vs. Experimental Budget

Many AI companies have gained early success from “AI tourist” or “experimental budget,” as businesses are eager to explore AI integration [00:27:29]. However, the challenge lies in translating this into “durable revenue” by securing business line budgets [00:28:20]. High engagement and usage metrics among end-users are key indicators of genuine product value [00:28:50].

Valuation and Fund Construction

AI valuations are significantly higher, reflecting expectations of larger market outcomes and faster growth rates [00:35:55]. AI-native development can lead to companies achieving substantial revenue with very few employees (e.g., hundreds of millions of revenue with 20-40 employees), potentially requiring less future capital [00:36:40].

However, high valuations come with increased risk due to the rapidly changing market [00:37:44]. Many AI companies are achieving significant revenue (e.g., $3 mi ll i o n,$ 5 million, or even $10 million ARR) very quickly, but their organizational maturity (systems, people, processes) often lags behind their revenue maturity [00:39:14]. Investors must perform “first principle diligence” to differentiate between genuinely strong, enduring businesses and those with inflated early revenue [00:42:56]. The market is also seeing incumbents and startups eating into the budget of legacy Business Process Outsourcing (BPO) and services businesses [00:34:58].

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