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M&A Process Differences: AI vs. Generative AI


M&A process differences: AI versus Generative AI

About three years ago we got involved in selling and buying the first business that leveraged AI or were build around an AI application. At the time the experts who developed these artificial intelligence applications didn't call it AI, they called it machine learning. Nowadays Generative AI opens up a totally new spectrum of opportunities, with its implications for the M&A process.


Generative AI and traditional AI (or machine learning) represent different paradigms or applications of artificial intelligence. Let's first understand the difference between AI (or traditional Machine Learning) and Generative AI. Here's a breakdown of the main differences:


Purpose

  • Generative AI: As the name suggests, the primary aim of generative AI is to generate data. This could be in the form of images, text, music, etc. Models like GANs (Generative Adversarial Networks) and certain variants of autoencoders are classic examples. Generative models learn the underlying distribution of the input data to produce new, previously unseen data.

  • Traditional AI/Machine Learning: The purpose of traditional machine learning is typically to make predictions or classifications based on input data. For example, a supervised learning algorithm might predict housing prices based on features of a house, or it might classify emails as spam or not spam.

Training Approach

  • Generative AI: Generative models often require specialized training mechanisms. For example, GANs involve two networks: a generator and a discriminator, which are trained together in a sort of "game" where the generator tries to produce fake data that the discriminator can't distinguish from real data.

  • Traditional AI/Machine Learning: Training often involves feeding labeled data into an algorithm, adjusting the model's parameters to minimize prediction error. Algorithms like linear regression, decision trees, and neural networks fall into this category.

Output

  • Generative AI: Produces new, previously unseen data samples.

  • Traditional AI/Machine Learning: Typically outputs a prediction, classification, or some other form of structured information.

Use Cases

  • Generative AI: Art creation (e.g., music, paintings, etc.), drug discovery (by generating molecular structures), data augmentation, creating virtual environments, etc.

  • Traditional AI/Machine Learning**: Fraud detection, recommendation systems, image classification, natural language processing tasks like translation and sentiment analysis, among countless others.

Data Dependencies

  • Generative AI: Many generative models, especially deep generative models, require substantial amounts of data to generate high-quality outputs.

  • Traditional AI/Machine Learning: The amount of data required varies based on the task and complexity of the model. Some algorithms can work well with small datasets, while others, especially deep learning models, may require large datasets.

Model Examples

  • Generative AI: Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), certain types of Reinforcement Learning agents, etc.

  • Traditional AI/Machine Learning**: Linear Regression, Decision Trees, Support Vector Machines, Convolutional Neural Networks (for tasks like image classification), etc.


While both generative AI and traditional AI operate under the umbrella of artificial intelligence and machine learning, they serve different purposes, utilize different training techniques, and produce different types of outputs. AI and Generative AI are different animals in the same jungle requiring

These nuances between AI and Generative AI can influence the M&A process, particularly when it comes to selling businesses that operate in these domains. Let's delve into the distinct differences in these scenarios:


1. Technical Understanding

  • AI Business: M&A advisors would focus on the breadth of AI applications, which can range from pattern recognition to predictive analytics, natural language processing, and more.

  • Generative AI Business: The focus would be on the depth and specificity of generating new content, ideas, or data. Understanding the technical workings of models like GANs (generative adversarial networks) becomes critical.

2. IP and Asset Valuation

  • AI Business: The valuation may center on proprietary algorithms, data collection and processing capabilities, and integration with diverse business applications.

  • Generative AI Business: Emphasis might be on the uniqueness of generated outputs, the quality and diversity of generated content, and potential applications in areas like content creation, design, and simulation.

3. Use-case Demonstrations

  • AI Business: Demonstrations may showcase accurate predictions, automation efficiencies, or enhanced decision-making capabilities.

  • Generative AI Business: Showcasing the quality, diversity, and utility of generated outputs (like images, sounds, or text) becomes paramount.

4. Regulation and Ethics

  • AI Business: Concerns might include data privacy, biased decision-making, and algorithmic transparency.

  • Generative AI Business: Additional concerns arise around the creation of synthetic or fake content, potential misuse in misinformation or deepfakes, and the implications of creating content that wasn't previously existent. Besides the upcoming EU AI Act there will be many more laws ruling when looking at Generative AI.

Typical implications for M&A processes looking at these two different technologies could relate to following topics.


A. Target Buyers

AI Business: Potential buyers could be companies looking to enhance their predictive analytics, data processing, or automation capabilities across various sectors.

Generative AI Business: Interested parties might be in sectors like entertainment, biotech, design, gaming, or research, where content or data generation can be a game-changer.


B. Due Diligence

AI Business: Evaluation of the breadth of AI applications, integration capabilities, and scalability.

Generative AI Business: Deep dive into the quality of generated outputs, robustness against adversarial attacks, and potential for diversifying generated content.


C. Future Potential

AI Business: Evaluation based on the AI's ability to scale, adapt to different industries, and refine its predictions or automations.

Generative AI Business: Consideration of the potential expansion into new content areas, realism of outputs, and possible new applications or markets.


D. Risk Assessment

AI Business: Risks may center around data breaches, algorithmic biases, or malfunctions in decision-making processes.

Generative AI Business: Emphasis on potential misuse, ability to control generated outputs, and the challenges of maintaining ethical boundaries in content creation.


E. Valuation Considerations

AI Business: Based on existing customer base, breadth of applications, data assets, and revenue streams.

Generative AI Business: Valuation might heavily rely on the uniqueness and quality of generated content, potential markets for synthetic data or content, and proprietary techniques that enhance generation.


In summary, while both AI and Generative AI businesses present exciting opportunities in the M&A landscape, they come with distinct technical, ethical, and market considerations. M&A advisors must be well-versed in these nuances to effectively position, evaluate, and negotiate deals in these domains.


In case you want to know more about the M&A processes related to (Generative) AI business, or are in the process of buying or selling an AI company, we are happy to discuss how we can support in the best way. Please reach out to us on info@crossings-advisory.com or +31 (0)85 2006244.

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