7 Predictions for AI in 2025: A CTO's, Rogerio Chaves Perspective
Rogerio
Jan 1, 2025
As we enter 2025, the AI landscape is poised for significant evolution. Reflecting on the trajectory of large language models (LLMs) and multimodal systems, I can't help but feel that some of these predictions may sound like hallucinations today. But by the end of the year, I expect them to be obvious. Let’s dive into the seven trends shaping the future of AI.
1. Agents will continue to evolve and dominate
AI agents are here to stay. While we won’t fully solve the challenge of autonomous agents in 2025, expect steady progress and improved tooling. Think about the evolution of JavaScript frameworks before React became dominant, or how object-oriented programming transitioned to modern paradigms. The same maturation is happening with AI agents.
LLMOps will become even more critical as enterprises strive to deploy and monitor increasingly complex agents. Expect an ecosystem of tools to emerge, improving practices and enabling more scalable, reliable agent-based systems.
2. Video and multimodal data will unlock new frontiers
As Ilya Sutskever suggested, AI may soon hit a bottleneck with textual data, but we’re not there yet. Video, audio, and other rich data sources still hold untapped potential. Multimodal models are advancing rapidly, and video remains a goldmine of untapped relationships and insights.
Look for breakthroughs in multimodal learning that harness video beyond simple transcription. This shift will allow foundational models to extract deeper meaning from non-textual sources, pushing the boundaries of what's possible in AI.
3. Google and China leading the AI Race?
The momentum at Google is palpable. After resolving internal conflicts, they’re accelerating progress by leveraging their vast ecosystem, including YouTube. Chinese companies are also advancing rapidly. Releases like Qwen 2.5 and DeepSeek v3 illustrate the surge of innovation coming from China, backed by data inaccessible to Western organizations.
While OpenAI will remain a major player, it may struggle to stay at the forefront of foundational research. However, OpenAI’s consumer dominance with ChatGPT will likely persist throughout 2025, continuing to shape public perception of AI.
4. The rise of small, powerful, and affordable models
The trend of smaller, more efficient models outperforming their larger predecessors will accelerate. We’ve seen this with LLaMA 3.2 and DeepSeek v3's Mixture of Experts (MoE) architecture.
Costs are dropping, and model portability is increasing. This shift, combined with hardware advancements, could make 2025 the year where running models locally or embedding them directly into applications becomes standard practice, driven by the principles of a data flywheel.
5. Heavy models for research, distilled models for production
Large models like Claude 3.5 Opus may never see the light of day outside research labs. Instead, they’ll be used to distill knowledge into smaller, more efficient versions. Models such as the OpenAI o1-family exemplify this trend.
Expect to see a clear delineation between high-compute research models that push AI boundaries and lean production models designed for real-world applications. This will streamline the development of AI applications, optimizing compute resources.
6. Practitioners vs. hobbyists: Two distinct AI camps
AI development is currently split between cutting-edge prototypes and production-ready applications. In 2025, this divide will become more apparent. Rapid prototyping, often dismissed as gimmicky, will remain valuable. As apps like Lovable demonstrate, speed can be a differentiator.
At the same time, robust testing and monitoring practices, such as Test-Driven Development (TDD) for LLMs, will mature, paving the way for enterprise-grade AI.. Expect to see more adoption of LLMOps tools that ensure reliability and scalability for AI-driven applications.
7. A transformers-level breakthrough will reshape AI
I firmly believe that 2025 will witness a breakthrough akin to the advent of transformers. Whether it comes in the form of a new network architecture, a hardware innovation, or a novel data structuring method, something fundamental will change.
While we won’t achieve AGI (Artificial General Intelligence) just yet, we may solve critical challenges like quadratic complexity or AI reasoning. This black swan moment will, in hindsight, seem inevitable.
Looking ahead
AI is evolving at breakneck speed, and the landscape in 2025 will be shaped by innovations across agents, multimodal data, and model efficiency. As practitioners, staying ahead of these trends will be crucial. Whether you're focused on monitoring / evaluating LLMs with tools like the LLM Optimization Studio or building AI agents, the year ahead promises to be transformative.Here’s to an exciting 2025!
Ready to explore how LangWatch can support you in Monitoring and evaluating your LLM-features?
Book a call with us!
As we enter 2025, the AI landscape is poised for significant evolution. Reflecting on the trajectory of large language models (LLMs) and multimodal systems, I can't help but feel that some of these predictions may sound like hallucinations today. But by the end of the year, I expect them to be obvious. Let’s dive into the seven trends shaping the future of AI.
1. Agents will continue to evolve and dominate
AI agents are here to stay. While we won’t fully solve the challenge of autonomous agents in 2025, expect steady progress and improved tooling. Think about the evolution of JavaScript frameworks before React became dominant, or how object-oriented programming transitioned to modern paradigms. The same maturation is happening with AI agents.
LLMOps will become even more critical as enterprises strive to deploy and monitor increasingly complex agents. Expect an ecosystem of tools to emerge, improving practices and enabling more scalable, reliable agent-based systems.
2. Video and multimodal data will unlock new frontiers
As Ilya Sutskever suggested, AI may soon hit a bottleneck with textual data, but we’re not there yet. Video, audio, and other rich data sources still hold untapped potential. Multimodal models are advancing rapidly, and video remains a goldmine of untapped relationships and insights.
Look for breakthroughs in multimodal learning that harness video beyond simple transcription. This shift will allow foundational models to extract deeper meaning from non-textual sources, pushing the boundaries of what's possible in AI.
3. Google and China leading the AI Race?
The momentum at Google is palpable. After resolving internal conflicts, they’re accelerating progress by leveraging their vast ecosystem, including YouTube. Chinese companies are also advancing rapidly. Releases like Qwen 2.5 and DeepSeek v3 illustrate the surge of innovation coming from China, backed by data inaccessible to Western organizations.
While OpenAI will remain a major player, it may struggle to stay at the forefront of foundational research. However, OpenAI’s consumer dominance with ChatGPT will likely persist throughout 2025, continuing to shape public perception of AI.
4. The rise of small, powerful, and affordable models
The trend of smaller, more efficient models outperforming their larger predecessors will accelerate. We’ve seen this with LLaMA 3.2 and DeepSeek v3's Mixture of Experts (MoE) architecture.
Costs are dropping, and model portability is increasing. This shift, combined with hardware advancements, could make 2025 the year where running models locally or embedding them directly into applications becomes standard practice, driven by the principles of a data flywheel.
5. Heavy models for research, distilled models for production
Large models like Claude 3.5 Opus may never see the light of day outside research labs. Instead, they’ll be used to distill knowledge into smaller, more efficient versions. Models such as the OpenAI o1-family exemplify this trend.
Expect to see a clear delineation between high-compute research models that push AI boundaries and lean production models designed for real-world applications. This will streamline the development of AI applications, optimizing compute resources.
6. Practitioners vs. hobbyists: Two distinct AI camps
AI development is currently split between cutting-edge prototypes and production-ready applications. In 2025, this divide will become more apparent. Rapid prototyping, often dismissed as gimmicky, will remain valuable. As apps like Lovable demonstrate, speed can be a differentiator.
At the same time, robust testing and monitoring practices, such as Test-Driven Development (TDD) for LLMs, will mature, paving the way for enterprise-grade AI.. Expect to see more adoption of LLMOps tools that ensure reliability and scalability for AI-driven applications.
7. A transformers-level breakthrough will reshape AI
I firmly believe that 2025 will witness a breakthrough akin to the advent of transformers. Whether it comes in the form of a new network architecture, a hardware innovation, or a novel data structuring method, something fundamental will change.
While we won’t achieve AGI (Artificial General Intelligence) just yet, we may solve critical challenges like quadratic complexity or AI reasoning. This black swan moment will, in hindsight, seem inevitable.
Looking ahead
AI is evolving at breakneck speed, and the landscape in 2025 will be shaped by innovations across agents, multimodal data, and model efficiency. As practitioners, staying ahead of these trends will be crucial. Whether you're focused on monitoring / evaluating LLMs with tools like the LLM Optimization Studio or building AI agents, the year ahead promises to be transformative.Here’s to an exciting 2025!
Ready to explore how LangWatch can support you in Monitoring and evaluating your LLM-features?
Book a call with us!
As we enter 2025, the AI landscape is poised for significant evolution. Reflecting on the trajectory of large language models (LLMs) and multimodal systems, I can't help but feel that some of these predictions may sound like hallucinations today. But by the end of the year, I expect them to be obvious. Let’s dive into the seven trends shaping the future of AI.
1. Agents will continue to evolve and dominate
AI agents are here to stay. While we won’t fully solve the challenge of autonomous agents in 2025, expect steady progress and improved tooling. Think about the evolution of JavaScript frameworks before React became dominant, or how object-oriented programming transitioned to modern paradigms. The same maturation is happening with AI agents.
LLMOps will become even more critical as enterprises strive to deploy and monitor increasingly complex agents. Expect an ecosystem of tools to emerge, improving practices and enabling more scalable, reliable agent-based systems.
2. Video and multimodal data will unlock new frontiers
As Ilya Sutskever suggested, AI may soon hit a bottleneck with textual data, but we’re not there yet. Video, audio, and other rich data sources still hold untapped potential. Multimodal models are advancing rapidly, and video remains a goldmine of untapped relationships and insights.
Look for breakthroughs in multimodal learning that harness video beyond simple transcription. This shift will allow foundational models to extract deeper meaning from non-textual sources, pushing the boundaries of what's possible in AI.
3. Google and China leading the AI Race?
The momentum at Google is palpable. After resolving internal conflicts, they’re accelerating progress by leveraging their vast ecosystem, including YouTube. Chinese companies are also advancing rapidly. Releases like Qwen 2.5 and DeepSeek v3 illustrate the surge of innovation coming from China, backed by data inaccessible to Western organizations.
While OpenAI will remain a major player, it may struggle to stay at the forefront of foundational research. However, OpenAI’s consumer dominance with ChatGPT will likely persist throughout 2025, continuing to shape public perception of AI.
4. The rise of small, powerful, and affordable models
The trend of smaller, more efficient models outperforming their larger predecessors will accelerate. We’ve seen this with LLaMA 3.2 and DeepSeek v3's Mixture of Experts (MoE) architecture.
Costs are dropping, and model portability is increasing. This shift, combined with hardware advancements, could make 2025 the year where running models locally or embedding them directly into applications becomes standard practice, driven by the principles of a data flywheel.
5. Heavy models for research, distilled models for production
Large models like Claude 3.5 Opus may never see the light of day outside research labs. Instead, they’ll be used to distill knowledge into smaller, more efficient versions. Models such as the OpenAI o1-family exemplify this trend.
Expect to see a clear delineation between high-compute research models that push AI boundaries and lean production models designed for real-world applications. This will streamline the development of AI applications, optimizing compute resources.
6. Practitioners vs. hobbyists: Two distinct AI camps
AI development is currently split between cutting-edge prototypes and production-ready applications. In 2025, this divide will become more apparent. Rapid prototyping, often dismissed as gimmicky, will remain valuable. As apps like Lovable demonstrate, speed can be a differentiator.
At the same time, robust testing and monitoring practices, such as Test-Driven Development (TDD) for LLMs, will mature, paving the way for enterprise-grade AI.. Expect to see more adoption of LLMOps tools that ensure reliability and scalability for AI-driven applications.
7. A transformers-level breakthrough will reshape AI
I firmly believe that 2025 will witness a breakthrough akin to the advent of transformers. Whether it comes in the form of a new network architecture, a hardware innovation, or a novel data structuring method, something fundamental will change.
While we won’t achieve AGI (Artificial General Intelligence) just yet, we may solve critical challenges like quadratic complexity or AI reasoning. This black swan moment will, in hindsight, seem inevitable.
Looking ahead
AI is evolving at breakneck speed, and the landscape in 2025 will be shaped by innovations across agents, multimodal data, and model efficiency. As practitioners, staying ahead of these trends will be crucial. Whether you're focused on monitoring / evaluating LLMs with tools like the LLM Optimization Studio or building AI agents, the year ahead promises to be transformative.Here’s to an exciting 2025!
Ready to explore how LangWatch can support you in Monitoring and evaluating your LLM-features?