A Digest of Agentic AI vs. Generative AL: Common AI & Vertical AI & Horizontal AI
GenAI focuses on creating, agentic AI focuses on doing. Generative AI's output is new content, while agentic AI's output is a series of actions or decisions.
Agentic AI must have the following characteristics:
If traditional genAI models that simply respond to prompts or execute predefined tasks, agentic AI can make decisions, plan actions, and learn from its experiences
AI agents, multi-agent systems and multimodal AI will dominate in 2025
AI agents are able to work autonomously (or semi-autonomously) and perform multi-step processes. According to Capgemini, only about 10% of large enterprises are already using AI agents — but 82% plan to integrate them in the next three years.
Google identifies six types of AI agents:
Having many agents taking on many processes across many tasks, goals, functions can create a chaos, which will give rise to new platforms.
Agentic Autonomous AI can analyze data, set goals, and take action to achieve them. It achieves near-human cognition by employing a combination of advanced AI techniques, including large language models (LLMs), machine learning algorithms, deep learning, and reinforcement learning.
Agentic AI refers to a system or program that is capable of autonomously performing tasks on behalf of a user or another system by designing its workflow and using available tools. The system has “agency” to make decisions, take actions, solve complex problems and interact with external environments beyond the data upon which the system’s ML models were trained.
From Vertical SaaS Vs. Horizontal SaaS to Vertical AI Vs. Horizontal AI
Horizontal SaaS is a type of cloud software solution that is targeted to a wide audience of business users, regardless of their industry.
In contrast to the horizontal software model, vertical SaaS solutions include software that is targeted to a particular niche or industry-specific standards.
While vertical AI excels in domain-specific applications, Horizontal AI takes a more expansive approach, serving as a foundational layer that underpins and enhances various technologies and industries. These versatile AI systems are adept at tackling general tasks, such as natural language processing, computer vision, and predictive analytics, making them invaluable assets across diverse sectors.
Vertical AI refers to AI systems that are designed to solve problems within a specific industry or domain. These AI models are highly specialized, focusing on niche areas such as healthcare, finance, manufacturing, or legal services.
Vertical AI
Vertical AI refers to AI systems that are designed to solve problems within a specific industry or domain. These AI models are highly specialized, focusing on niche areas such as healthcare, finance, manufacturing, or legal services, specialized AI systems used for medical diagnosis, financial risk assessment, supply chain management, legal document analysis.
The key characteristics of Vertical AI include:
Real-life vertical AI examples
Healthcare AI
Use Case: IBM Watson Health uses AI to analyze medical records, assist in diagnosing diseases, and recommend treatment plans. It is tailored specifically for the healthcare industry, leveraging medical data, clinical trials, and research to provide insights to doctors and healthcare providers.
Real-Life Application: Watson has been used in oncology to help oncologists determine personalized treatment options for cancer patients, considering vast amounts of data that a human might not easily process1 .
Finance AI
Use Case: AlphaSense is an AI-powered market intelligence and search platform used by financial analysts and researchers. It specializes in processing financial documents, earning calls, and news articles to provide actionable insights for investment decisions.
Real-Life Application: Investment firms use AlphaSense to track market trends, company performance, and emerging risks, helping them make informed decisions based on vast amounts of financial data2 .
Pharma AI
Use Case: DeepMind’s AlphaFold is an AI model developed by DeepMind for predicting protein structures. This model is highly specialized within the field of molecular biology and has revolutionized research in this area.
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Real-Life Application: AlphaFold has been used to predict the structures of proteins that are difficult to study experimentally, accelerating drug discovery and understanding of diseases.
Retail AI
Use Case: Amazon Personalize is an AI service that helps retailers provide personalized product recommendations, promotions, and targeted content to customers. It’s designed specifically for the e-commerce and retail sectors.
Real-Life Application: E-commerce platforms like Zappos use Amazon Personalize to deliver custom recommendations to shoppers, increasing conversion rates and customer satisfaction.
Agriculture AI
Use Case: Blue River Technology, a subsidiary of John Deere, uses AI for precision agriculture. Its See & Spray technology identifies crops and weeds in real-time and applies herbicides only where necessary.
Real-Life Application: Farmers using Blue River’s AI-driven equipment can significantly reduce the amount of chemicals used, lowering costs and environmental impact while maintaining crop yields3 .
Automotive AI
Use Case: Tesla’s Autopilot is a specialized AI model designed for autonomous driving. It processes data from cameras, radar, and ultrasonic sensors to navigate roads, avoid obstacles, and assist with driving tasks.
Real-Life Application: Tesla vehicles equipped with Autopilot can perform tasks such as lane keeping, adaptive cruise control, and automated parking, making driving safer and more efficient4 .
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SUPPLEMENT: the Future of AI
The Future of AI is the Future of Humanity, and vice versa.
The Future of AI, Automation, Robotics (AIAR) bifurcates:
Big Tech Anti-Human-Competing AIAR Technology Taking Over the World
Pro-Human-Complete AIAR Technology Augmenting the Human World
AI AS NON-HUMAN MACHINE INTELLIGENCE AND LEARNING IS THE GREATEST EVER IDEA, INVENTION OR INNOVATION OR GENERAL TECHNOLOGY IN ALL HUMAN HISTORY OF IDEAS.
AI AS HUMAN MACHINE INTELLIGENCE AND LEARNING IS THE WORST EVER IDEA, INVENTION OR INNOVATION OR GENERAL TECHNOLOGY IN ALL HUMAN HISTORY OF IDEAS.
NO AI TECHNOLOGY, MACHINE, SYSTEM, ALGORITH, OR MODEL COULD OR SHOULD COPYCAT, IMITATE, MIMICK, REPLICATE, OR SIMULATE HUMANS, OUR BODY, BRAIN, BRAINS/INTELLIGENCE, BEHAVIOR, BUSINESS, our work, jobs, tasks, and goals.
The whole presumption of “AI mimicking the brain or human human intelligence or human behavior” is the mass-marketing consumers fraud.
If you wish, it is a sort of big lie, a gross distortion or misrepresentation of the truth primarily used as a commercial propaganda technique, a constant repetition across many different forms of media is necessary for the success of the big lie technique.
Its key beneficiaries are big tech fake AI companies, from Nvidia to Microsoft to Tesla, which market caps have jumped to trillions USD due to the big lie, now the prospective subjects of the prospective Global AI Class Actions Litigation for massive consumer fraud and fake AI product liabilities.
To conclude, the future of humanity with the big tech humanoid AIRA is the AI apocalypse, if we fail to overcome the evil AI oligopolies.
As even ChatGPT could suggest that “a powerful AI system might decide that it no longer needs human oversight or intervention, and begins to act independently. This could lead to a rapid and widespread takeover of all digital systems, including military and industrial infrastructure”.