AI: The Next Era of Application-Driven Business Growth
AI's impact on business is shifting from models to applications, distribution, and new market creation. This analysis explores key investment themes.
Key Insights
-
Insight
The AI story is shifting from foundational models to applications, distribution, and new business models, driving unprecedented growth faster than previous tech platform shifts.
Impact
This highlights a critical pivot for investors and entrepreneurs, emphasizing the need to focus on how AI is integrated into user-facing products and reaches broad markets, rather than just raw model development.
-
Insight
AI adoption in enterprises is accelerating rapidly, primarily driven by the desire for increased economic value and reduced labor, making businesses 'richer and lazier'.
Impact
Companies that can clearly demonstrate AI's ability to significantly cut costs or boost revenue will see rapid adoption and market pull, leading to substantial valuation increases.
-
Insight
Three major AI application themes are emerging: traditional software going AI-native, software replacing or augmenting labor, and 'walled gardens' built on proprietary data.
Impact
These themes provide a strategic framework for identifying high-potential AI investments and entrepreneurial opportunities, guiding resource allocation and market entry strategies.
-
Insight
Defensibility ("moats") in AI applications is crucial and often comes from becoming a 'system of record' or leveraging unique, proprietary data.
Impact
Entrepreneurs must prioritize building sticky solutions with embedded data advantages or end-to-end workflow ownership to sustain long-term competitive advantage against rapid replication.
-
Insight
The 'software eating labor' category represents a larger market opportunity than traditional software, enabling automation of tasks previously requiring human effort.
Impact
This opens up vast new markets for AI solutions, particularly in sectors with high labor costs or chronic skills shortages, leading to significant market capitalization creation.
-
Insight
Aggregators of AI models are gaining traction in consumer AI, offering a single interface to access diverse, specialized models, similar to how Kayak functions for flights.
Impact
This suggests a winning strategy for consumer-facing AI products: focus on providing comprehensive access and comparative utility across different AI models rather than relying on a single underlying model.
-
Insight
Incumbents are increasingly leveraging AI to enhance their existing offerings and find new monetization strategies, indicating a dual battleground with startups.
Impact
While greenfield opportunities abound for startups, incumbents with deep customer bases and proprietary data can also become formidable AI players, necessitating distinct strategies for both challengers and established firms.
Key Quotes
"A lot of people think the AI story is about models. This episode argues the real story is about apps, distribution, and modes."
"Everybody wants two things. They want to be richer and lazier. So they want to do less work and get more economic value. And this is really what Gen AI unlocks, and it's really starting to happen right now."
"The source of defensibility for Eve is in owning the end-to-end workflow, right? It is actually in building a product that is contextual to you know, all the work that that attorney has to do. And then I think, you know, not unique to Eve, but one of the kind of uh X factors is that the data that that business is generating, which Alex will get into a bit in this sort of walled garden, it has a bit of these characteristics of this sort of walled garden, um, is not public, and it sort of creates a source of compounding competitive advantage, you know, for the product itself, right?"
Summary
AI: The Next Era of Application-Driven Business Growth
The narrative around Artificial Intelligence is rapidly evolving, moving beyond foundational models to the critical role of applications, distribution, and novel business models. This shift marks a new phase of technological acceleration, outstripping prior platform shifts like the PC, Internet, Cloud, and Mobile eras.
The Product Cycle Drives Growth
History shows that product cycles are the true drivers of market growth. Just as the PC, Internet, Cloud, and Mobile eras spawned enduring infrastructure and application companies, AI is now creating unprecedented value. The adoption rate of AI technology is phenomenal, with a significant portion of net new revenue in software stemming from AI at both infrastructure and application layers. This rapid advancement, even in just two years, demonstrates AI's ability to unlock substantial economic value by making users "richer and lazier" – reducing effort while increasing output.
Three Core Themes in AI Applications
Leading venture capital firms are focusing their investments on three defensible AI application themes:
1. Traditional Software Going AI-Native
Similar to the cloud-native revolution, existing software categories are being reimagined with AI at their core. Incumbents are adopting AI, enhancing their offerings and charging for new features. However, significant greenfield opportunities exist for new companies to build AI-native systems of record from scratch, particularly when businesses hit inflection points that demand more advanced solutions than legacy systems can provide.
2. Software Eating Labor
This theme represents arguably the largest market opportunity: AI applications performing tasks traditionally done by humans. This isn't just about cost savings; it's about unlocking new value by automating processes, performing tasks that humans can't (e.g., 24/7 multilingual support), or achieving significantly better outcomes (e.g., 50% higher collection rates). These solutions often augment, rather than eliminate, human labor, freeing up individuals for higher-value activities. The key for success here lies in building sticky, vertical operating systems that become indispensable.
3. Walled Gardens with Proprietary Data
In a world where foundational AI models are increasingly commoditized, proprietary data becomes a crucial moat. Businesses that aggregate unique, often publicly available but disaggregated, data sources and then apply AI to deliver a finished product create immense value. Examples range from medical journal data for diagnostic tools to historical domain ownership records for cybersecurity. These "walled gardens" allow companies to charge for enriched, actionable insights rather than just raw data, leveraging AI to transform niche information into highly valuable solutions.
Building Enduring AI Companies
Defensibility in the AI era relies on more than just AI capabilities. Companies must build strong "moats" – whether through owning end-to-end workflows, becoming a system of record, or leveraging unique data sets. This ensures that their solutions are sticky and difficult to displace by competitors offering slightly cheaper alternatives. Furthermore, aggregators of models are proving successful in consumer AI, providing a single pane of glass for diverse specialized models, similar to how Kayak aggregates airline flights.
Conclusion
AI is not merely an incremental improvement; it's a transformative force creating vast new opportunities across technology and business. Investors and entrepreneurs alike must understand these shifting dynamics – from application-centric growth to the strategic importance of proprietary data and labor augmentation – to build and back the next generation of enduring companies.
Action Items
Invest in AI-native solutions that target greenfield opportunities or address underserved needs where incumbents are slow to adapt.
Impact: This strategy can capture significant market share early by providing superior, AI-powered alternatives to traditional, often complex, legacy systems.
Develop AI applications that either significantly reduce labor costs or dramatically increase revenue, aiming for outcomes-based pricing models.
Impact: By focusing on measurable value creation, businesses can unlock large, previously inaccessible markets and achieve explosive revenue growth, particularly in labor-intensive sectors.
Identify and digitize unique, often freely available but scattered, data sources to build 'walled garden' businesses enhanced by AI.
Impact: This creates a powerful competitive moat, allowing companies to deliver highly differentiated, finished AI products that are otherwise unavailable to general models, commanding premium pricing.
Prioritize building "systems of record" or end-to-end workflow solutions to ensure stickiness and defensibility against rapidly evolving AI competition.
Impact: Companies focusing on deep integration into core business processes will create higher switching costs and stronger competitive advantages, crucial in a fast-paced AI market.
For consumer AI, explore aggregator models that provide access to multiple specialized AI models rather than building solely on a single model.
Impact: This approach can offer superior utility and flexibility to end-users, catering to diverse needs and preferences, leading to broader adoption and stronger market position.
Mentioned Companies
A16Z
5The firm is discussing its successful investment strategy and approach to the AI market, showcasing expertise and positive outcomes.
Eve
5Presented as a highly compelling example of AI-driven legal tech, demonstrating end-to-end workflow ownership and proprietary data moats.
Salient
5Highlighted as an explosive growth company using AI in auto loan servicing, demonstrating significant value generation (50% more collections) and strong moats.
Chat GPT
4Described as a rapidly adopted tool used by a significant percentage of adults, showcasing its immediate and widespread impact.
Mercury
4Presented as a canonical example of a 'greenfield opportunity' in neobanking for startups, demonstrating successful market entry without displacing incumbents.
Rillet
4Highlighted as an AI-native ERP company, likened to a 'NetSuite but better' example of a greenfield investment.
Toast
4Highlighted as a highly successful vertical operating system for restaurants, demonstrating how adding financial services and deep integration creates a strong moat.
Presented as an AI-powered medical platform with exclusive licenses to medical journals, showcasing a 'walled garden' approach for superior results.
VLEX
4Highlighted as a 26-year-old legal data company that quintupled revenue by adding AI, demonstrating the transformative power of AI on proprietary data.
Ask Leo
4Cited as an AI-powered procurement product that leverages proprietary contract data, showcasing a new application of 'walled garden' principles.
Krea
4Presented as an AI-native Photoshop competitor, illustrating the "traditional software going AI-native" theme in the consumer space.
11 Labs
4Highlighted as a category creator in voice and audio models, demonstrating new market opportunities driven by AI innovation.
Presented as an AI therapist that collects proprietary data from existing therapists to train its models, showcasing a successful 'walled garden' strategy in consumer AI.
Ash
4The consumer product of Slingshot, an AI therapist leveraging proprietary data, demonstrating the direct application of AI to consumers.
Decagon
4Mentioned as a company where a team member introduced co-founders, implying a successful venture capital investment.
Ramp
3Cited as a forward-thinking company whose enterprise adoption of AI demonstrates the technology's immediate impact on saving time and money.
Cisco
3Highlighted as an 'enduring infrastructure company' from the internet era, serving as a benchmark for long-term success.
Akamai
3Highlighted as an 'enduring infrastructure company' from the internet era, serving as a benchmark for long-term success.
eBay
3Mentioned as an 'enduring company in the application space' built on the internet, showcasing successful application-layer businesses.
Amazon
3Mentioned for its enduring application (eBay) and its AWS division accounting for a vast majority of its market cap, illustrating platform growth.
Workday
3Cited as a successful application layer company from the cloud era and discussed as an incumbent that will leverage AI for new monetization.
Shopify
3Cited as a successful application layer company from the cloud era, exemplifying growth from prior tech shifts.
Viva
3Cited as a successful application layer company from the cloud era, exemplifying growth from prior tech shifts.
Cited as an example of a successful vertical software company, demonstrating that specialized solutions can become very large businesses.
Cited as an example of a successful vertical software company, demonstrating that specialized solutions can become very large businesses.
Used as an example of a company that builds a 'walled garden' by aggregating publicly available but niche data (ADS B transponder data) and adding value.
Cited as an example of a company with proprietary data (funding rounds) that can be made more valuable with AI, transforming raw data into finished products.
Cited as an example of a company with proprietary data (legal information) that becomes more valuable with AI, offering finished products over raw data.
CoStar
3Cited as an example of a company with proprietary real estate data, illustrating the power of specialized data assets.
Cited as an example of a company with proprietary financial data, demonstrating the value of exclusive information.
Used as an example of a data moat built by acquiring genealogical records, illustrating the value of unique, digitized information.
Cited as an example of a company with proprietary historical data (whois queries) that becomes tremendously more valuable with AI integration.
Uber
3Used as an example of a 'why now' company, whose success was enabled by new mobile technology like iPhones and GPS, providing context for current AI-driven opportunities.
Airbnb
3Used as an example of a 'why now' company, whose success was enabled by new mobile technology, similar to Uber.
Kayak
3Used as an analogy for AI model aggregators, demonstrating the value of a single interface for multiple options over single-source solutions.
Intuit
3Described as having a 'gold mine' for AI monetization through its existing QuickBooks customer base, highlighting an incumbent's AI opportunity.
OpenAI
2Acknowledged as an infrastructure layer and leading consumer app, but also noted as a potential competitor to application builders, creating a nuanced view.
NetSuite
2Cited as an incumbent ERP system, illustrating the difficulty of switching but also the opportunity for AI-native alternatives in greenfield scenarios.
Mentioned as a widely used accounting software that struggles with complexity, highlighting a market opportunity for AI-native solutions, and as an incumbent with a 'gold mine' for AI monetization.
Bill.com
2Mentioned as an incumbent that will become stronger with AI adoption, showcasing the potential for AI to enhance existing businesses.
SAP
2Mentioned as an incumbent ERP system that will become stronger with AI adoption, indicating its role in the evolving landscape.
Adobe
2Mentioned as an incumbent that will become stronger with AI adoption, showcasing how AI enhances creative software.
UiPath
2Cited as an existing RPA company, illustrating an incumbent in a category being transformed by AI.
Harvey
2Mentioned as an existing legal AI company, providing context for the market and contrasting with Eve's unique approach.
Mentioned in the context of historical market dynamics (Netscape) and its current meaningful entry into the AI space, indicating a significant player.
Zendesk
1Mentioned as an existing customer support company whose business model is challenged by AI, driving a shift to outcomes-based pricing.
Cited as an incumbent CRM, representing a 'hostage' business with an opportunity for AI-native greenfield disruption due to user dissatisfaction.
Mentioned neutrally as an incumbent in email marketing to illustrate the challenge of displacing existing brownfield solutions.
Mentioned neutrally as a traditional payment processor, contrasting with Toast's integrated software solution.
Mentioned neutrally as a traditional payment processor, contrasting with Toast's integrated software solution.
Delta
0Used neutrally in the Kayak analogy to represent a single airline, contrasting with the value of an aggregator.
United
0Used neutrally in the Kayak analogy to represent a single airline, contrasting with the value of an aggregator.
Eliza
-1Used as a contrast to modern AI, illustrating an early, less effective AI attempt, providing historical context.
Netscape
-2Used as a historical example of a company that became 'roadkill' due to a larger competitor, illustrating the challenges of building enduring companies.
WordPerfect
-2Used as a historical example of a once-dominant software company that ultimately failed due to shifts in distribution and product cycles.
VisaCalc
-2Used as a historical example of a once-dominant software company that ultimately failed due to shifts in distribution and product cycles.
Lotus 123
-2Used as a historical example of a once-dominant software company that ultimately failed due to shifts in distribution and product cycles.