How I See AI Evolving in 2026 (as an AI Engineer)

AI Landscape in 2026: A Detailed Analysis

Key Concepts:

• LLMs (Large Language Models): AI models trained on massive datasets of text, capable of generating human-like text, translating languages, and answering questions.
• Hallucinations: The tendency of LLMs to generate factually incorrect or nonsensical information.
• AGI (Artificial General Intelligence): Hypothetical AI with human-level cognitive abilities.
• DAX (Direct AI Execution): A deterministic approach to AI workflows, emphasizing control and predictability.
• Agents: Autonomous AI entities capable of performing tasks and making decisions.
• MCP (Model-Controlled Prompts): A protocol for making tools available to LLMs.
• A2A (Agent-to-Agent): Google’s protocol for enabling communication and collaboration between AI agents.
• Spec-Driven Development: A coding methodology utilizing LLMs to generate code based on detailed specifications.
• Context Engineering: Techniques for providing LLMs with the necessary information to generate accurate and relevant responses.
• Tool Calling: The ability of LLMs to utilize external tools and APIs to enhance their capabilities.

1. Limits of LLMs and the Search for New Paradigms

The current state of LLMs is characterized by persistent “hallucinations” – instances where the models confidently present false information. Despite ongoing improvements and increasing benchmark scores, a true breakthrough towards AGI using the current LLM architecture is unlikely in the near future. While models will improve at tasks like coding and tool usage, the fundamental method of extracting value and building applications around them will remain largely unchanged. The speaker highlights the work of Yan LeCun, emphasizing the consensus among leading AI researchers that current LLMs are approaching a limit and require a fundamentally different approach to intelligence engineering. The focus is shifting towards applied AI – utilizing existing tools effectively rather than solely pursuing theoretical advancements. A paper on recursive language models offers a promising, though incremental, technique for overcoming context window limitations by chaining LLM calls.

2. Google’s Ascendancy and the Ecosystem Advantage

Google is emerging as a significant player in the AI landscape, having been relatively quiet for the past two years. Gemini 3 is now achieving state-of-the-art results on benchmarks like Arc AGI. However, Google’s true strength lies in its complete ecosystem: best-in-class language, image, and video models, proprietary TPUs for model training (reducing reliance on Nvidia), and a vast data reservoir. The A2A protocol is particularly noteworthy, representing a step beyond MCP by focusing on complex workflows, interoperability, and agent delegation – enabling agents to collaborate rather than simply accessing tools. Google’s AI Studios demonstrate the potential of this integrated approach.

3. Workflows vs. Agents: Finding the Right Balance

The debate between deterministic workflows (DAX) and autonomous agents continues. Entropic, a leading AI lab, previously advocated for simple workflow patterns and optimizing single LLM calls for reliability. However, recent discussions suggest a re-evaluation of this stance. While models are becoming more adept at tool usage, DAX remains valuable for applications requiring high certainty and limited hallucinations. The optimal approach depends on the specific use case: DAX for critical processes (e.g., banking), agents for more flexible applications with human oversight (e.g., chat interfaces). The speaker emphasizes the importance of starting with the simplest solution and increasing complexity only when necessary.

4. Agentic Coding: A Major Area for Improvement

Agentic coding – utilizing LLMs to assist with software development – is poised for significant advancements. LLMs demonstrate strong coding capabilities, and focused fine-tuning on tool-calling tasks offers substantial potential. The speaker recommends exploring tools like Cursor and Claude Code, advocating for a hybrid approach. Spec-driven development is highlighted as a best practice for maximizing the effectiveness of agentic coding, moving beyond “vibe coding” and ensuring code quality. Staying current with these tools is crucial for developer productivity.

5. Context Engineering: The Cornerstone of LLM Applications

Context engineering – the art of providing LLMs with the right information at the right time – remains a critical skill. The speaker references a previous video detailing this topic, emphasizing its relevance regardless of whether DAX or agents are employed. Effective context engineering is the most impactful strategy for improving LLM application performance.

6. The Rise of Voice as the Primary Interface

Voice is predicted to become the dominant interface for interacting with technology within the next decade, offering a significantly faster input method than keyboards or touchscreens. Companies like OpenAI are investing heavily in audio technology. The speaker is developing a voice-to-text application, anticipating increased demand for voice-enabled tools.

7. We Are Still in the Early Stages

Despite the rapid advancements in AI, the technology is still in its nascent stages. The speaker notes that most companies are just beginning to explore AI, often limited to basic chatbot usage. This presents a significant opportunity for those with expertise in the field. The core fundamentals of working with LLMs remain consistent, even as models evolve. The speaker encourages developers to specialize and capitalize on the growing demand for AI skills.

Notable Quotes:

• “LLMs still hallucinate. Co-pilots still suck.” – Sets the tone for a realistic assessment of the current AI landscape.
• “If you really look at what happened in literally the past 2 years… the way you build applications around these models… is still pretty much the same.” – Highlights the incremental nature of recent LLM improvements.
• “Google could potentially be a big winner in 2026.” – Expresses a strong belief in Google’s potential due to its integrated ecosystem.
• “Always try to find the simplest solution first and then in increasing complexity later.” – A fundamental engineering principle applicable to AI development.
• “You are in the right spot.” – Encouragement to developers entering the AI field.

Data & Statistics:

• The speaker receives approximately 225 project requests, indicating a high demand for AI development services.
• Voice input is estimated to be 4-5 times faster than keyboard input.

Conclusion:

The AI landscape in 2026 will be characterized by continued incremental improvements in LLMs, a growing dominance of Google due to its ecosystem advantages, and a need for engineers to find the right balance between deterministic workflows and autonomous agents. Agentic coding and context engineering will be crucial skills, and voice will emerge as a primary interface. Despite the rapid pace of change, the fundamentals of AI development remain consistent, and the field is still in its early stages, presenting significant opportunities for those willing to learn and adapt. The key takeaway is to focus on applied AI, prioritize simplicity, and stay informed about emerging trends.