The first wave of AI innovation is over. Here’s what comes next

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In this newsletter, we’ll explore how businesses can unlock the potential of generative AI while minimizing its risks. AI has made significant advancements, but it faces challenges due to a lack of quality training data. We'll discuss how businesses can harness high-quality data, engage experts, leverage latent data, capture data in context, and secure proprietary information to drive the next wave of AI innovation.

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The AI Plateau: Understanding the Challenge

Generative AI models have shown remarkable capabilities, but they are reaching a plateau due to a shortage of quality training data. AI models, like the one that advised using glue for pizza or struggled with basic arithmetic, highlight the limitations of current AI technologies.

Quote: "AI should be based on human discovery and knowledge and crafted with human-centric attributes of privacy and quality in mind." – Industry Expert

The S-Curve of Innovation

Innovation follows an S-Curve pattern, where initial slow progress is followed by rapid advancements and eventually stabilization. Examples include:

• TCP/IP: Originated in the 1960s, saw significant acceleration in 1974, and stabilized with version 4 in 1981.

• The Browser Wars: Rapid evolution in the late 1990s, followed by incremental improvements.

• Mobile Apps: Surge after the iPhone App Store launch in 2008, with fewer novel apps today.

Quote: "The first wave of AI innovation is over. Here’s what comes next." – Fast Company

AI's Current Plateau

AI's growth surged with the 2017 paper "Attention Is All You Need," leading to the development of ChatGPT. However, recent improvements have been incremental, highlighting the need for new data sources to jump to the next S-Curve.

The Next Frontier: Business Data

Business data, such as product specifications, sales presentations, and customer support interactions, holds the key to the next wave of AI innovation. This data is of far higher quality than publicly available internet data.

Quote: "Startups that unlock and harness business data will create significant value and tools that enterprises actually want to adopt." – Fast Company

Four Opportunities for Startups

1. Engage Experts

High-quality training data comes from field experts, not crowdsourced labelers. Startups should embed themselves in expert communities and use novel incentive structures like gamification to source data.

Example: Companies like Centaur Labs and Turing leverage networks of professionals to improve data quality.

Outcome: Enhanced AI models with accurate and relevant training data.

2. Leverage Latent Data

Valuable data exists within business apps like Salesforce and Slack. Startups can help enterprises prepare this data for AI use, enabling more effective AI training and deployment.

Example: Companies like Unstructured and Reducto assist enterprises in ingesting complex documents for use with AI models.

Outcome: Improved AI performance through better data utilization.

3. Capture in Context

Businesses generate new data daily. Capturing this data without disrupting workflows is crucial for building effective AI models.

Example: Apps like Zoominfo and Textio guide workers to perform their tasks more efficiently and capture valuable data in the process.

Outcome: Continuous improvement in AI capabilities with real-time data.

4. Secure the Secret Sauce

Enterprises should build and deploy custom models to stay in control and protect their intellectual property. Techniques like federated learning allow model training without sensitive data leaving the user's device.

Example: Startups such as Flower and FedML help organizations utilize federated learning techniques.

Outcome: Enhanced data privacy and security, with proprietary AI models tailored to specific business needs.

Learn AI in 5 Minutes a Day

AI Tool Report is one of the fastest-growing and most respected newsletters in the world, with over 550,000 readers from companies like OpenAI, Nvidia, Meta, Microsoft, and more.

Our research team spends hundreds of hours a week summarizing the latest news, and finding you the best opportunities to save time and earn more using AI.

Sign up with 1-Click

The first wave of AI innovation has plateaued, but the next wave is on the horizon. By harnessing high-quality business data, engaging experts, leveraging latent data, capturing data in context, and securing proprietary information, businesses can unlock AI's full potential.

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