"Unveiling the AI Value Chain and the Metrics for Quantitative Product Market Fit"

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Jul 29, 2023
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"Unveiling the AI Value Chain and the Metrics for Quantitative Product Market Fit"
Introduction:
Artificial Intelligence (AI) has become a prominent force in technology, with its potential to revolutionize industries and reshape the way we live and work. Understanding the AI value chain and ensuring quantitative product market fit are essential for companies looking to capitalize on this transformative technology. In this article, we will explore the critical components of the AI value chain and the metrics that determine product market fit.
The AI Value Chain:
The AI value chain consists of three key components: the foundational model, fine-tuning, and end-user access points. The foundational model combines computing power and data with advanced algorithms to create a machine capable of going beyond rote commands. This model can be further fine-tuned to suit specific use cases or scenarios, enhancing its effectiveness. Finally, the end-user access points encompass the deployment of the model in various applications, allowing organizations to leverage AI's power.
Compute Power and Data:
The compute layer of the AI value chain is vital for running algorithms effectively. However, it is not as simple as using a specific chip for AI operations. Some AI algorithms require the simultaneous use of hundreds or thousands of GPUs to achieve optimal performance. Additionally, AI models are typically trained using data sets. In the past, labeled data sets were considered essential for training AI, but advancements have expanded the possibilities.
Fine-Tuning for Specific Use Cases:
Fine-tuning is the process of customizing a foundational model to fit a particular use case. This step in the value chain allows organizations to tailor AI capabilities to their unique requirements. For example, the use of the Stable Diffusion foundational model, fine-tuned with Google's Dreambooth, can yield specific and desired outcomes. Companies like OpenAI and OpenPhil have built on existing AI models, such as GPT-3, and fine-tuned them for their offerings.
Integrated AI and Infrastructure:
Integrated AI involves incorporating AI capabilities into existing products without displacing incumbents. Microsoft's integration of Dall-E into its Office suite exemplifies this approach. Furthermore, major consolidation is expected in all levels of the AI value chain, except for access points. Cloud providers like AWS, Oracle, and Azure, for instance, are poised to develop custom AI workload chips, networking software, and in-house models that users can leverage.
The Intelligence Layer and Invisible AI:
The intelligence layer of the AI value chain focuses on the improvement of fundamental models. As these models evolve rapidly, the importance of fine-tuning decreases, shifting towards output cost and speed optimization. The companies that compete in this layer will differentiate themselves based on their ability to attract top talent and achieve extraordinary feats. Additionally, companies like Bytedance, the parent company of TikTok, exemplify success by utilizing AI as an enabling technology to build breakthrough products from the ground up.
Quantitative Product Market Fit Metrics:
Product market fit (PMF) indicates when a product is deemed good enough to transition from product improvement to scalable distribution. Cohort retention rate emerges as a crucial metric for determining PMF. This rate represents the percentage of users who continue using a product over a specified period. A high cohort retention rate signifies that a product has achieved PMF and can be distributed through effective growth strategies.
Tracking Cohort Retention:
To monitor progress towards PMF, companies should employ a cohort retention "triangle" chart. This chart plots the retention rates of different cohorts over time. As product improvements are implemented, newer cohorts should exhibit higher retention rates. When multiple cohorts level off at a specific retention rate, PMF has been achieved. For benchmarking purposes, it is essential to analyze the retention rates of comparable products that have experienced significant growth within your industry.
The Power of Retention:
Good cohort retention amplifies acquisition efforts, as users acquired are more likely to stay with the product. It also indicates the level of satisfaction and loyalty among users, leading to positive word-of-mouth recommendations. Users who continue using a product over an extended period are more inclined to share it with friends, further fueling growth. Actively measuring cohort retention rate using a triangle cohort retention chart enables companies to optimize their product and acquisition strategies.
Actionable Advice:
- 1. Invest in computing power: To maximize the potential of AI algorithms, organizations should ensure they have the necessary computing power, including GPUs, to run them effectively.
- 2. Fine-tune for specific use cases: Tailoring AI capabilities to meet specific business requirements can yield better outcomes. Consider utilizing existing models and fine-tuning them to suit your organization's unique needs.
- 3. Prioritize cohort retention: Focus on improving cohort retention rates to achieve quantitative product market fit. Actively measure and track these rates using a cohort retention chart, allowing you to refine your product and acquisition strategies accordingly.
Conclusion:
Understanding the AI value chain and achieving quantitative product market fit are critical for organizations looking to harness the power of AI successfully. By recognizing the interconnectedness of compute power, data, fine-tuning, integrated AI, and the intelligence layer, companies can position themselves for success in the AI landscape. Additionally, prioritizing cohort retention as a metric for PMF enables organizations to refine their products and distribution strategies, driving growth and customer satisfaction.
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