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Predictions on the Evolution of AI (Jan 2025)

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artificial intelligence, machine learning, technology, future predictions

Predictions on the Evolution of AI (Updated to January 20, 2025)

This is the first version of my predictions on AI, dated January 20, 2025. The projection is based on in-depth research of trends from economic, technological, cultural, and situational perspectives. Foresight was applied, and all accumulated professional experience was leveraged to outline these possibilities. Additionally, it was absolutely necessary to get hands-on and build autonomous agents supported by LLMs, perform RAG, fine-tuning, analyze the entire landscape from the ground up to understand where the AI revolution is heading, etc.

Below are historical reviews of what has already happened to provide context, followed by predictions in a temporal horizon format.

1. 2017-2019: Birth of Transformers and Initial Consolidation of LLMs

  • 2017: The paper “Attention Is All You Need” is published, introducing the Transformer architecture.
  • 2018-2019: The first Large Language Models (LLMs) based on Transformers, such as GPT-2 and BERT, emerge, demonstrating remarkable abilities in text generation and comprehension.

2. 2020-2022: Expansion of Capabilities and Popularization of LLMs

  • GPT-3 makes a significant impact with its ability to produce coherent and detailed text with minimal input.
  • Companies and developers discover multiple applications for LLMs, such as virtual assistants, content generation, and machine translation.
  • Foundations are laid for the development of more advanced reasoning techniques and integration with various workflows.

3. 2023: First Improved Versions of LLMs and Early Multimodal Models

  • Context length is extended, and learning capabilities from diverse data sources, such as text, images, and audio, are enhanced.
  • The multimodal approach gains strength, allowing models to start processing more diverse information and perform complex tasks like image description or video analysis.

4. 2024: Emergence of Chain of Thought (CoT)

  • The Chain of Thought (CoT) technique is integrated into LLMs, enabling the exposition and utilization of “step-by-step reasoning.”
  • Explainability improves, facilitating adoption in critical sectors such as education, medicine, and scientific research, where understanding how the model arrives at its conclusions is vital.

5. 2025: Advances in Interpretability and First Forms of Functional Agents

  • Explainability is reinforced: inferences made by models can now be traced more clearly.
  • Agents begin to emerge, though still basic, that use CoT to automate simple tasks such as calendar planning and data analysis with minimal human supervision.

6. 2026: Emergence of More Advanced Agents and Their Integration into Business Environments

  • More sophisticated intelligent agents emerge, capable of analyzing multiple information sources in real-time and operating autonomously in complex processes, such as logistics management, customer service, and assisted scientific research.
  • CoT technology drives these agents to solve problems with greater depth and autonomy, gaining popularity in high-value sectors like finance, industry, and healthcare.
  • Consistent video generation will achieve superlative quality, supported by increased computing capacity, making professional video creation accessible to individuals and initiating the open video industry.

7. 2027-2028: Birth of AI OS (AI Operating System) for End Users

  • The first version of an AI Operating System (AI OS) optimized to efficiently manage and coordinate multiple models is launched for personal computers and smart devices.
  • Key features of AI OS:
    • Data Source Standardization: Allows integration and processing of any type of file (text, PDF, image, video, etc.), including a powerful multimodal OCR technology that analyzes and extracts information from any source, as well as direct internet scraping capabilities.
    • Continuous Learning and Relearning: The system adjusts various internal models, personalizing performance based on each user’s usage.
    • Management of Local and Remote Models: AI OS can utilize its own models or external models, choosing the optimal option based on resources and needs.
    • Distributed AI and Privacy: Focused on not relying solely on the cloud, offering local solutions with enhanced security and privacy.
    • Deep Personalization of AI: Supports physical robotics, diverse interfaces (voice, gestures, etc.), and replaces the traditional concept of apps with specialized “agents.”
    • Standards for AI OS: Standards and protocols are established to ensure interoperability, compatibility, and consistency among managed agents and models.
    • Vector Database Management: Manages vector databases of various dimensions, facilitating efficient storage, retrieval, and analysis of high-dimensional data.

8. 2029-2031: Deepening Human-AI Collaboration, Standardization of AI OS, and First Experimental AGI

  • AI OS is adopted as the center of computational activity, delegating file organization, intelligent search, and daily task automation.
  • Standards are created to exchange models and data between different AI OS, promoting interoperability within the AI ecosystem.
  • By 2031, large computing centers integrate advanced, even quantum, architectures and networks of agents to achieve the first experimental version of AGI. Although it requires enormous resources and has rudimentary functionalities, it serves as a proof of concept.
  • Concurrently, a corporate bubble occurs due to the great boom and expectations, where numerous companies fail as the real technology outpaces initial promises.

9. 2032-2035: Cross-Pollination with Quantum Computing, High-Level Agents, and BCI

  • Quantum computing techniques are adopted to accelerate the training and inference of AI models.
  • Agents reach a new level of stratified reasoning, evaluating and reconfiguring their own decision-making processes.
  • AI OS coordinates several specialized models, such as language processing, vision, mathematical reasoning, and financial analysis, integrating their results to provide complex and efficient solutions.
  • Brain-Computer Interfaces (BCI) begin to popularize, allowing direct interaction with AI OS and its agents, ranging from basic control to experiments in clinical and educational environments.

10. 2036-2040: Towards the Democratization of AGI in Every Computer

  • The convergence of advanced models, collaborative agents, and hyperconnected AI operating systems makes AGI accessible on personal computers and smart devices.
  • Systems not only understand a user’s context but also anticipate their needs, offer creative solutions, and foster joint innovation.
  • Society reorganizes around these cognitive assistants, driving advances in key sectors such as education, medicine, transportation, energy, and communications, marking the transition towards a new technological and human paradigm with truly universal AGI.
  • In this scenario, BCIs evolve to offer a wide range of interaction options with AI, while AI OS continues to prioritize distributed, secure, and personalized AI, with “agents” completely replacing traditional software.