If that's your only focus, you're missing the real opportunity to grow.
En la cumbre anual de Red Hat, el director de tecnología Chris Wright ofreció una advertencia velada a las empresas que ven la inteligencia artificial únicamente como una herramienta de recorte de costos: la eficiencia no es el destino, sino el punto de partida. Un banco europeo que reinvirtió las ganancias operativas generadas por la IA creó más de 600 millones de dólares en valor nuevo, ilustrando que la verdadera promesa de esta tecnología no está en lo que elimina, sino en lo que hace posible. En un momento en que la velocidad del cambio tecnológico no da señales de desacelerarse, Wright propone que las organizaciones elijan entre adaptarse o quedarse atrapadas en una conversación que ya quedó pequeña.
- La mayoría de las empresas están usando la IA para reducir costos, pero al hacerlo están dejando sobre la mesa la oportunidad más grande: convertir esa eficiencia en crecimiento real.
- El miedo a que la IA reemplace trabajadores persiste en los equipos, creando resistencia interna justo cuando las organizaciones más necesitan moverse rápido.
- Un banco europeo demostró que reinvertir las ganancias de productividad generadas por la IA puede producir más de 600 millones de dólares en valor nuevo, un modelo que pocos están siguiendo.
- Red Hat lanzó tres herramientas —imágenes de seguridad reforzada, un entorno de escritorio para desarrolladores y un orquestador de automatización— para ayudar a las empresas a escalar la IA del prototipo a la producción.
- El verdadero obstáculo no es técnico: es la brecha entre el prototipo funcional y un sistema en producción que convenza al equipo financiero de que la inversión tiene retorno.
- Las empresas que lideren no serán las que usen la IA para reducir plantillas, sino las que la usen para expandir lo posible y tengan la disciplina de reinvertir en nuevos territorios.
Chris Wright, director de tecnología de Red Hat, aprovechó la cumbre anual de la compañía en 2026 para cuestionar el enfoque dominante con el que las empresas están adoptando la inteligencia artificial. La mayoría, argumentó, persigue la eficiencia: menos costos, menos fricción, más output con menos recursos. Pero eso es solo la mitad de la historia, y no la mitad que importa.
El ejemplo más elocuente que ofreció fue el de un banco europeo que, en lugar de guardar los ahorros generados por la IA, los reinvirtió en nuevas áreas de negocio. El resultado fue más de 600 millones de dólares en valor creado desde cero. Ese es el movimiento que la mayoría de las empresas no está haciendo: confunden la eficiencia con el destino, cuando debería ser el punto de partida.
Sobre el temor al desplazamiento laboral, Wright ofreció una lectura distinta. La IA no reemplaza a las personas; cierra brechas de habilidades. En el desarrollo de software, personas sin formación técnica ya están escribiendo código funcional con asistencia de IA, aprendiendo en tiempo real mientras la tecnología se encarga de la sintaxis y ellos se concentran en la lógica. Eso no es sustitución, es democratización.
Red Hat presentó tres lanzamientos orientados a ayudar a las organizaciones a escalar la IA de forma rentable: imágenes con seguridad reforzada para reducir vulnerabilidades, un entorno de escritorio consistente para desarrolladores y un orquestador que integra la IA en los marcos tradicionales de automatización IT. Pero Wright fue claro: el software no es suficiente. Pasar del prototipo a la producción exige plataforma, infraestructura y la capacidad de justificar la inversión ante el equipo financiero. Ahí es donde la mayoría tropieza.
La conclusión implícita es que la carrera no la ganarán quienes usen la IA más agresivamente para recortar personal, sino quienes la usen para expandir lo posible y tengan la disciplina de reinvertir en nuevos territorios. Es un juego completamente distinto al que domina la mayoría de las conversaciones en las salas de juntas hoy.
Chris Wright, the chief technology officer at Red Hat, sat down during the company's 2026 summit to make a case that most businesses are getting artificial intelligence wrong. They're chasing efficiency—cutting costs, automating routine work, squeezing more output from fewer people. But that's only half the story, and the half that matters less.
The real opportunity, Wright argued, lies in what you do with the gains. A European bank understood this. When AI tools began freeing up capacity and cutting operational drag, the bank didn't pocket the savings. It reinvested them. The result: over $600 million in new business value created from scratch. That's the move most companies aren't making, Wright said. They see efficiency as the destination. They should see it as the starting line.
This matters because the technology itself isn't slowing down. The pace of change in AI is accelerating, and companies have a choice: treat that as a threat or as permission to think differently about how work gets done. Wright framed it as the latter. The only constant now is change itself. The question is whether you're building your organization around that reality or fighting it.
One of the stickier concerns about AI in the workplace is the fear it will simply replace people. Wright pushed back on that framing. AI doesn't eliminate workers; it closes skill gaps. He pointed to software development as the clearest example. Today, people without formal training in coding are writing functional code with AI assistance. They're not being displaced—they're learning to become developers in real time, with the technology doing the heavy lifting on syntax and structure while they focus on logic and problem-solving. That's not replacement. That's democratization.
Red Hat's own pitch to enterprises reflects this philosophy. The company announced three major releases at the summit, each designed to help organizations move AI from experimental prototype to scaled production. Hardened Images are security-focused versions of Red Hat's software with known vulnerabilities patched out—a response to the expanded attack surface that comes with widespread AI deployment. Red Hat Desktop gives developers a consistent working environment on their own machines. And an automation orchestrator integrates AI into traditional IT automation frameworks, shifting systems from reactive to proactive.
But the real work, Wright suggested, happens in the middle. Getting from a working prototype to a production system that actually generates measurable return on investment requires more than software. It requires platform, infrastructure, and the ability to convince the finance team that the money spent will come back. That's where most companies stumble. They build the proof of concept. They struggle to scale it. They never get to the reinvestment phase.
The implication is clear: the companies that win won't be the ones that use AI most aggressively to cut headcount. They'll be the ones that use it to expand what's possible, then have the discipline to reinvest the freed-up resources into new territory. That's a different game entirely from the efficiency narrative that dominates most boardroom conversations about AI. And it's the one that actually builds lasting value.
Notable Quotes
If your only focus with AI is efficiency, you're missing the opportunity to grow. With efficiency comes the ability to take those gains and invest in areas of growth.— Chris Wright, Red Hat CTO
Many people writing code aren't software developers. But they're learning to become developers by having AI do the work alongside them.— Chris Wright, Red Hat CTO
The Hearth Conversation Another angle on the story
When Wright talks about a European bank generating $600 million in new value, what does that actually mean? What did they build?
The source doesn't specify the exact business they moved into. The point is they took productivity gains—money and time freed up by AI automation—and deployed it toward something new rather than pocketing it as cost savings. It could have been a new product line, a market expansion, a service offering. The specifics matter less than the discipline of reinvestment.
But isn't that just good business practice? Why does it need AI to make that obvious?
Because AI is making the efficiency gains so visible and so fast that companies are tempted to stop there. Without AI, productivity improvements happen slowly, incrementally. You might not notice them. With AI, the gains are sudden and measurable. That creates a choice point. Most companies see a sudden gain and think "we can cut costs." The successful ones think "what can we build with this?"
Wright mentions closing skill gaps. But doesn't that also mean the people doing the work need to be willing to learn?
Yes. He's not saying AI solves the problem unilaterally. He's saying it removes one barrier—the barrier of not knowing how to code. But you still need curiosity, willingness to experiment, access to tools. The technology enables it; it doesn't guarantee it.
What about the security angle with the Hardened Images?
More AI in production means more attack surface. Vulnerabilities that were theoretical become practical targets. Red Hat's response is to ship software with known security issues already patched. It's a way of saying: if you're going to scale AI fast, at least don't do it with known holes in the foundation.
Is Wright saying companies should stop focusing on efficiency altogether?
No. He's saying efficiency is the tool, not the goal. If efficiency is your only goal, you've already lost the bigger game. The goal should be growth. Efficiency is how you fund it.