In the early 2000s, Salesforce made waves in the tech world with its bold “No Software” marketing campaign, symbolized by the iconic image of the word “software” crossed out in a red circle. While it was a bit misleading—Salesforce still delivered software, just in the cloud—the campaign invited people to rethink software delivery. This marked the dawn of the cloud era, and businesses were ready for a change. Then, enter Salesforce and the AI Revolution.
Thank you for reading this post, don't forget to subscribe!Today, we’re witnessing a similar shift with AI. The word “SaaS” is the latest to be crossed out in red, as AI-native applications, where AI is the core rather than an add-on, promise to disrupt service delivery at an unprecedented speed—far faster than cloud displaced on-premise software.
Even Bessemer Venture Partners (BVP), a leader in identifying emerging AI trends, admits to being caught off guard by the rapid rise of AI. In its State of the Cloud 2024 report, which aptly declares “The Legacy Cloud is dead—long live AI Cloud!”, BVP highlights how even the most optimistic predictions couldn’t fully capture the pace and scale of AI’s impact.
The AI Revolution: Opportunities and Disruption
The AI market is evolving at breakneck speed, and entrepreneurs are scrambling to stake their claim in this quickly shifting landscape. In the early cloud era, companies like Box, Docusign, HubSpot, and Shopify found success by targeting specific business use cases with subscription-based, cloud-powered solutions. Similarly, today’s AI opportunity lies in industries where manual, repetitive tasks are still prevalent.
Major AI players like OpenAI, Anthropic, and Mistral are investing billions in building large-scale language models (LLMs), but there’s a gap in the market for entrepreneurs to focus on verticals where human labor is still largely manual—such as legal, accounting, and outsourcing services.
Traditionally, investors have shied away from these industries due to their reliance on manual labor, high costs, and low profit margins. But AI changes the game. Tasks once done manually can now be automated, transforming labor-intensive processes into scalable, high-margin operations. Services businesses that were once unattractive to investors will now attract attention as AI boosts profitability and efficiency.
The Shift to AI-Native Applications
The impact of AI-native applications will go beyond improving revenue models; they will fundamentally change how we interact with software. In the current SaaS model, users spend hours in applications, manually entering data and querying systems for answers. In contrast, AI-native B2B applications will solve problems end-to-end without requiring human input for every step. Software will work for users in the background, allowing them to focus on building relationships and making strategic decisions.
However, humans won’t be removed from the equation. AI trained on real human intelligence in specific verticals will perform better than purely machine-based intelligence. The combination of human expertise and AI-native applications will drive significant, tangible business results.
Avoid the “X of AI” Hype
With excitement around AI reaching fever pitch, many startups are branding themselves as the “X of AI”—for instance, the “Salesforce of AI.” These claims are often surface-level, wrapping an AI solution around an existing LLM without delivering true innovation. To identify genuine AI-native solutions, look for these key characteristics:
- Invisibility: The best AI-native software operates seamlessly in the background with minimal user interface.
- Data Magnets: The solution should efficiently capture relevant data and integrate multiple data sources, including real-time triggers.
- Self-Learning: The AI must be able to learn from data, tracking successes and failures to optimize its performance.
- Value-Based Pricing: Instead of traditional per-seat pricing, AI-native solutions should deliver value-driven pricing based on the results they produce.
- Machine-to-Machine Triggers: Efficient AI applications will have machine-driven actions, with human oversight when necessary, to increase speed and accuracy.
Spotting the Next AI Success Stories
The AI space is noisy and crowded, and as more AI-native startups emerge, it will become even harder to separate the winners from the hype. The true innovators will be those who bring untapped data into the digital fold and streamline workflows that have historically been manual.
To succeed, founders need deep knowledge of their vertical and a clear understanding of how to implement AI for real-world results. Above all, they must have the vision and drive to realize the full potential of AI-native applications, transforming industries and redefining service delivery.
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