Over the past few weeks, several AI stocks have seen uncertainty, with high flyers retreating from their all time highs. Around the same time, MIT published a report showing that many AI pilots are still struggling to scale into production. Meta also announced a significant reorganization of its AI teams, a move that further signals the need for recalibration in the sector. To some, these signals suggest that the AI boom may be cooling. I see it differently: this was a healthy dose of realism applied to some of the more fantastical claims that have been made about AI.
As David Sacks commented on his All-In podcast, after the launch of ChatGPT in late 2022, a dominant narrative took hold: that Artificial General Intelligence was just two or three years away. Depending on who you asked, this near-term AGI would either replace half the workforce or grow into a superintelligence that reshaped civilization. Both utopian and dystopian visions stemmed from the same idea—that AI would rapidly and recursively improve itself until it left humanity behind. It was only natural that such expectations would spark pushback. In both the US and the EU, dozens of AI-related bills are now advancing, reflecting growing efforts to regulate this technology.
Even more, the release of GPT-5 a few weeks ago illustrates this reality. It is an impressive step forward across many domains. But rather than a dramatic leap, it represents steady, evolutionary progress. Other leading models—from Anthropic to xAI to Google—are also advancing incrementally, each developing unique strengths. Instead of one model racing toward dominance, we are seeing a healthy specialization: some excelling in reasoning, others in code, others in media or search, we are seeing a competitive, evolutionary race.
This is why I view the recent correction as constructive. It challenges those magical views of AI and grounds us in reality: AI is a powerful new form of computing that will take time, discipline, and iteration to fully unlock. Success requires prompting, integration into other systems but mainly, human oversight. To build an AI agent that can replace a human in a corporate environment, it must integrate deeply with corporate information systems, operate compliantly with strict security standards, and provide full auditability of how it handles sensitive data. You cannot expect that to happen quickly.
In fact, the recent MIT study clarified how many of the most common claims about AI need to be rethought. Predictions that AI would replace most jobs within a few years are not materializing—workforce changes remain selective and industry-specific. While adoption is high, transformation at scale is still emerging: only a small share of enterprises have fully integrated AI into workflows, and many sectors are just beginning to see structural change. Importantly, where adoption succeeds, companies report measurable AI-driven savings—reduced BPO costs, lower agency spend, and efficiency gains in operations. Rather than framing this as a setback, these findings point directly to where the opportunity lies. The study shows that internal builds often struggle, while external partnerships consistently achieve higher success rates (MIT, 2025). For the industry, this demonstrates that the challenges are not barriers but openings for specialists who can deliver integration and cost savings at scale.
This represents a massive opportunity for the professional services industry as a whole. As the MIT study underscores, partnering with experienced providers doubles the likelihood of reaching deployment, showing that the future belongs to those who can bridge complexity with expertise. The AI ecosystem is fragmented and fast-moving, and its complexity is growing exponentially, with new models, frameworks, and paradigms emerging every week that are difficult for enterprises to follow. The hardest problems are integration, orchestration, and organizational change. In this environment, trusted services partners aren’t optional; they’re how companies navigate complexity and turn incremental model progress into durable enterprise value.
That evolutionary race demands a parallel evolution in the professional services industry. At Globant, we are reinventing ourselves to meet this moment with a next-generation model for professional services. At the heart of this model are AI Pods: modular, subscription-based units that deliver the output of a high-performing senior team, accelerated by agentic AI and guided by our experts. Pods provide domain-specific capacity—whether in engineering, automation, or creativity—streamed continuously to clients. They are priced and measured through tokens, giving companies clear, auditable usage while preserving variable scope. Crucially, they are powered by our Enterprise AI platform, ensuring security, model independence, agentic workflow and seamless integration with core enterprise systems.
AI systems are not simply about knowing how to build a RAG pipeline or plug LLMs together. They require a profound understanding of business combined with a profound understanding of technology—without hype, and with the recognition that the systems of the future will blend multiple eras and paradigms. Generative AI unlocks natural language understanding, but its true impact in the enterprise emerges only when combined with the engineering disciplines that already work, guided by responsibility and quality. We have already proven this approach at Globant for many of our customers in entertainment, sports, energy, life sciences, and financial services. In these industries, savings are real and measurable. Magic implementations are not—results come from expertise, discipline, and integration.
We are at a moment in which expectations are being reset and the conversation moves away from myths toward the real, practical work of turning AI into value. Even defining AGI is extremely difficult—by some definitions, we may already be there, with models that outperform humans in specific tasks. But the path to putting these capabilities into production is steeper and more complex than ever. That transition—from hype to production, from pilots to scaled adoption—is where the true long-term opportunity lies.
AI is here to stay. It will define a new investment supercycle. But sustaining that boom will require reinvention—of technology and of business models. At Globant, we are embracing that reinvention, step by step, so that our clients and our industry can capture the immense promise of this extraordinary technology.
