The State of AI in 2025: Reasoning Models, Talent Wars, and Agentic Coding

A New Era Dawns for AI

TLDR: 2025 marked the beginning of AI's industrial age, characterized by breakthroughs in model performance, a fierce war for talent, massive infrastructure investment, and the rise of AI-driven applications. This year set the stage for even deeper integration of AI into our daily lives.

Summary: The past year has been a period of explosive growth and transformation for the artificial intelligence industry. We witnessed significant leaps in model capabilities, particularly with the advent of reasoning models that can "think" through problems. This technological advancement was paralleled by an unprecedented corporate scramble for skilled practitioners, with companies offering massive compensation packages to lure top talent. The insatiable demand for computing power fueled a multi-trillion-dollar data center construction boom, which in turn had a noticeable impact on the U.S. gross domestic product. For architects and engineering teams, this signals a fundamental shift in the landscape. The technology is no longer a niche curiosity but a major economic force, creating both immense opportunities and significant challenges related to infrastructure, talent acquisition, and strategic implementation. The focus is shifting from simply building models to integrating them into robust, scalable, and indispensable applications.

Key takeaways:

• 2025 is being framed as the dawn of AI's "industrial age."
• Major themes include improved model performance, a talent war, and massive infrastructure buildouts.
• AI-driven applications have become indispensable.
• The coming year is expected to see even tighter integration of AI into daily life.

Link: A New Era Dawns

Thinking Models Solve Bigger Problems

TLDR: Models that incorporate reasoning strategies, like thinking step-by-step, have become the standard, leading to dramatic improvements in complex tasks like math, coding, and scientific problem-solving. However, this enhanced performance comes at a significant computational cost.

Summary: A major theme of 2025 was the embedding of reasoning workflows directly into large language models. Initially prompted with phrases like "let's think step by step," this capability is now a built-in feature, largely achieved through reinforcement learning. This has led to substantial performance gains, with models like OpenAI's o1 and DeepSeek-R1 demonstrating superior abilities in competitive math, PhD-level science questions, and coding challenges. The trend was further amplified by enabling these models to use external tools like calculators and search engines. However, this leap in capability is not without its tradeoffs. Reasoning-enabled models consume vastly more tokens, increasing both cost and latency. For architects, this presents a clear challenge: balancing the desire for state-of-the-art performance with the practical constraints of budget and user experience. The development of variable reasoning budgets, where models can dynamically allocate computational effort, represents a move towards more efficient systems, but the core tradeoff between reasoning depth and resource consumption remains a key architectural consideration.

Key takeaways:

• Reasoning workflows are now baked into most new large language models.
• This has led to significant performance improvements in math, science, and coding.
• The improved performance comes at a high cost in terms of token consumption and latency.
• The industry is exploring techniques like variable reasoning budgets to improve efficiency.

Link: Thinking Models Solve Bigger Problems

Big AI Lures Talent With Huge Pay

TLDR: The competition for top AI talent escalated into a full-blown war in 2025, with companies like Meta offering compensation packages worth hundreds of millions of dollars. This has driven the market value of skilled AI practitioners to unprecedented levels.

Summary: The past year saw a ferocious battle for AI talent among the industry's giants. Meta, in particular, disrupted traditional pay structures by offering massive, liquid compensation packages to poach top researchers and engineers from rivals like OpenAI, Google, and Anthropic. This included house calls from CEO Mark Zuckerberg and bonuses reportedly worth billions. The result was a dramatic inflation of salaries and a high-stakes game of musical chairs among the industry's most sought-after experts. This trend reflects the evolution of AI from an academic discipline to a technology of immense economic and strategic importance. For engineering leaders and hiring managers, it underscores the critical need for competitive compensation and a compelling vision to attract and retain top performers. The rationale is simple: when you are spending tens of billions on hardware, investing a fraction of that in the human talent required to make it effective is a logical business decision.

Key takeaways:

• Leading AI companies are engaged in a fierce talent war, with compensation reaching hundreds of millions of dollars.
• Meta has been particularly aggressive in poaching talent from competitors.
• The high salaries reflect the immense economic value now placed on AI expertise.
• This trend is a rational response to the massive investments being made in AI infrastructure.

Link: Big AI Lures Talent With Huge Pay

Agents Write Code Faster, Cheaper

TLDR: Agentic coding systems have moved beyond simple code completion to manage a wide range of software development tasks. This has become a major competitive battleground, with developers embracing sophisticated agentic frameworks that can manage entire codebases.

Summary: In 2025, agentic workflows for coding emerged as one of the most valuable applications of AI. Systems like Claude Code, Google Gemini CLI, and OpenAI Codex have transformed the development landscape, moving from simple autofill to managing complex software development tasks. The performance of these agents on benchmarks like SWE-Bench has skyrocketed, with the best models now solving over 80% of the challenges. The introduction of reasoning models further accelerated this trend, allowing for more efficient task planning and execution. This has led to a burgeoning industry around "vibe-coding," where users with little to no coding experience can build web applications from scratch. For development teams, this means that AI-assisted coding is rapidly becoming the new standard. While some feared the replacement of junior developers, the reality is that developers skilled in using these AI tools can prototype and build applications faster and more effectively than ever before.

Key takeaways:

• Agentic systems are now capable of managing complex software development tasks, not just code completion.
• The performance of coding agents on industry benchmarks has improved dramatically.
• Reasoning models have made agentic coding more powerful and cost-effective.
• AI-assisted coding is becoming the new standard, empowering developers to be more productive.

Link: Agents Write Code Faster, Cheaper