Are we in a AI Bubble?

As Scott Galloway wrote in his article the answer is yes and AI is here to stay. Artificial intelligence (AI) continues to dominate headlines, driving significant market capitalization and valuation speculation. A striking illustration of this is the recent AI boom from October 2022 to May 2024, which added a whopping $3 trillion to the market value of major tech companies like Amazon, Microsoft, and Google. However, the expected revenue from AI in 2024 is only projected to be around $20 billion, highlighting a significant gap between market valuation and actual revenue generation. Meaning there are significant disparities between market exuberance and tangible business outcomes (Bubble.ai, ProfGalloway 2024).

His article made me go deeper and explore the true value of AI solutions right now. I want to understand the actual impact of claims like a 30% productivity gain.

The Challenge of AI Valuations in Legacy SaaS Companies

This discrepancy between perceived and actual value raises important questions about the role and impact of AI in legacy SaaS companies like Salesforce, HubSpot, and Canva. These companies have integrated generative AI models into their platforms, yet their performance and utility often fall short compared to the original ChatGPT, at least from my experience. This brings me to a critical point: What is the true value of such integrations, and are customers genuinely willing to pay a premium for the integration or continue using ChatGPT?

Productivity Gains and Cultural Resistance

Another critical aspect of AI integration is the promise of increased productivity. Solutions like Microsoft's Copilot and other employee GPTs aim to boost productivity of your workforce by up to 30%, potentially saving countless hours. However, achieving these productivity gains is not just a matter of technology; it's also about culture and adaptability within organizations.

Adapting to new technology can be challenging. Employees need to be trained, workflows need to be adjusted, and there is often resistance to change. Even when organizations successfully implement these solutions, the question remains: What will they do with the productivity gains? Will they produce 30% more, will they reduce labor by 30%?

Real-World Use Cases & GenAI first companies

Michael Hunkeler wrote a great newsletter article about AI that works. Let me follow up on his #3 in the list - Customer Support through the use of LLMs. Klarna (the Swedish buy-now-pay-later company) integrated OpenAI's LLM models into Customer Support and claimed it handled 2/3 of customer service chats in the first month of existence. From my experience, this numbers seem a bit steep and can easily be inflated by just not giving another option. Aka there is no email or phone to contact - making your customer requests magically disappear.

With Connect AI, we launched our first AI-Assistant in the same month as Klarna. From what I can tell, GenAI makes a lot of sense in customer support. 24/7 availability, immediate answers, multilingual support, and high scalability are just the starting benefits. Furthermore, we see a consistent 15% reduction in total ticket volume over the last 5 months while offering all the other customer support options (email, phone, ticketing tool). You basically create customer value on day one, it fits right into your existing customer support environment and it helps you become more efficient and cost effective.

Now the challenge is to find more of these real-world use cases with clear benefits.

Key Takeaways

1. Valuation vs. Revenue: There is a significant gap between AI's market valuation and its actual revenue generation, indicating potential overvaluation.
2. Integration Challenges: Despite the massive valuation increases of legacy SaaS companies claiming AI integration, the added revenue and customer willingness to pay more remains to be seen.
3. Productivity Gains: While AI promises increased productivity, the actual benefits depend heavily on organizational adaptability and the strategic use of saved time and resources.

As we navigate the AI landscape, it’s crucial to critically assess the true value of AI technologies beyond the market hype, focusing on tangible benefits and real-world applications.