Photo By: Igor Omilaev Latham & Watkins recently made an unusual investment for a law firm: its own Nvidia-powered AI servers. The firm is using that infrastructure to fine-tune open-weight AI models while continuing to use commercial tools such as ChatGPT, Claude and Gemini. Rather than replacing outside AI providers, Latham is building an internal option alongside them, giving its engineers more control over how certain models are customized and deployed. That distinction may matter beyond the legal industry. As AI becomes embedded in increasingly sensitive and specialized business processes, enterprises may be entering a new phase of AI adoption in which the question is no longer simply whether to build or buy. Instead, companies may increasingly combine the two: buying general-purpose capabilities while building or controlling the layers that are most important to their own businesses.

The Build-or-Buy Question Is Becoming More Complicated For much of the generative AI boom, the enterprise model was straightforward. A company could access increasingly capable models through an API or software platform without having to operate the underlying computing infrastructure. For many applications, that still makes sense. But the calculus changes when AI becomes deeply connected to proprietary data, specialized workflows or consequential business decisions. An enterprise may care not only about what a model can do, but also where it runs, how it can be customized, how much control it has over its data and how dependent it becomes on a particular provider. That is one reason open-weight models have attracted growing enterprise attention. Recent analysis from the Financial Times points to their appeal around customization, cost and data privacy, while Gartner has identified open-weight models as an increasingly relevant part of enterprise AI strategies. The result is less a rejection of commercial AI than a diversification of the stack.

Buy the Commodity. Build the Differentiator. The emerging enterprise AI architecture may therefore look less like a binary choice and more like a portfolio. A company might use a frontier commercial model for a general reasoning task, an open-weight model for a specialized workflow, its own infrastructure for particularly sensitive workloads, and custom software to connect those systems to proprietary data. The objective isn’t necessarily to build a better foundation model than the companies spending billions of dollars to develop them. It is to control the parts of AI that are strategically important to the business. That can include the data layer, evaluation systems, workflow orchestration, domain-specific customization or the infrastructure required for particular workloads. For some companies, the case for owning infrastructure may be compelling. For others, the capital, engineering and operational requirements will make an external provider the more rational choice. The important question is therefore not simply, “Should we build our own AI?” It is “Which parts of our AI stack are important enough to own?”

Why Latham’s Strategy Is Interesting Latham offers a useful case study because its approach doesn’t require choosing one side. The firm has reportedly spent several years developing its own Nvidia server infrastructure and is using it to fine-tune open-weight models, while retaining access to commercial AI systems. The strategy gives the firm additional flexibility in choosing the right model for different tasks, while keeping particularly sensitive work within the infrastructure it controls. That creates something enterprises increasingly value: optionality. If a commercial model is the best tool for a particular task, the company can use it. If a workload requires greater customization or control, the company has another route. This also changes the way enterprises can think about vendor dependence. Instead of locking an entire AI strategy into one provider, companies can build an architecture in which different models and deployment environments serve different purposes. That doesn’t mean every company should start buying GPUs. It means that as AI becomes more strategically important, some enterprises may decide that having an internal capability is valuable even if they continue buying most of their AI externally.

From General AI to Specialized Enterprise Intelligence The same logic applies at the application layer. The most important enterprise AI systems may not be the ones that simply provide access to the most powerful general-purpose model. They may be the systems designed around a specific business problem, proprietary data environment and decision-making process. Kapnova is one example of this approach. The company describes itself as an agentic revenue and profit optimization system for consumer brands, combining AI-driven signal discovery with quantitative methods designed to evaluate commercial decisions. Its platform is built around questions such as pricing, launches and marketing spend rather than general-purpose conversation. That reflects a broader shift: enterprises don’t necessarily need to own the entire AI stack to create specialized intelligence. They can combine general-purpose models, proprietary data, specialized quantitative systems and domain-specific software into something tailored to their own operating environment. For Kapnova CEO and co-founder James Sun, that distinction is central to how businesses should think about AI: the model is only one component of a much larger decision system.

The AI Stack May Become Hybrid by Design Latham’s investment doesn’t establish that enterprises are about to become AI infrastructure companies. Nor does it mean commercial models are losing their relevance. It points to a more nuanced possibility. As AI becomes increasingly embedded in business operations, enterprises may become more selective about what they buy, what they customize and what they control themselves. The next phase of enterprise AI may therefore not be defined by whether a company chooses to build or buy. It may be defined by knowing which parts of the AI stack are worth owning, and which are better left to someone else.

Artificial intelligence has officially reached higher education, and students, teachers, and administrators alike are following in the momentum. Across thousands of universities and educational institutions, many are now resorting to AI to draft papers, generate ideas, and conduct research in a matter of seconds. Due to these generative tools, the efficiency in school systems has never been more promising.

AI in education offers a number of unmatched benefits. It accelerates the learning process. Helps individuals think outside of the box. Computes complex data quickly. Eases the studying experience. Yet, beneath all of this convenience also lies numerous challenges.

According to The Center for Democracy and Technology cited in a CBS News article, approximately 85% of teachers and 86% of students used AI in the 2024-2025 school year, while 54% of students reported using AI weekly, and 25% said they use it every day. These numbers are continuing to spike, underscoring just how prevalent AI in schools has become.

With all this demand, many worry whether or not AI poses consequences like cheating scandals and academic integrity. A growing number of educators and workforce experts question whether this automation is actually making an impact, or if it is negatively affecting how students build their resumes and seek meaningful careers.

An emerging concept called people-centric AI aims to address this complexity. Instead of framing students as cheaters or replacing human intelligence, this idea puts students’ interests back at the forefront, uncovering where they add value best.

Companies like Advisor AI, founded by Arjun Arora, are part of this movement, operationalizing the education system by developing a platform to give institutions real-time visibility into student goals and progress. While school systems typically argue AI is risky, AI that puts humans first gives students the support they need to attain sustainable careers.

Crucially, prioritizing the human element vastly changes the advising experience as a whole. Academic advisors are finally able to better interpret recommendations, provide emotional intel, and help students set more meaningful intentions. 

Additionally, this hybrid model is gaining traction as universities confront a widening career readiness gap. Employers increasingly report that graduates lack applied experience, professional networks, and clarity about career paths. At the same time, automation is reshaping entry-level roles as millions are starting to get replaced by these machines alone. However, with human-driven AI, it is becoming the framework all institutions and students need to survive in this labor market.

In a world where traditional jobs are changing at record speed, school systems must take preventative action right now. A recent labor market study shows more than 1.7 million jobs were replaced by AI in 2025 alone, and that kind of pressure is only rising.

Beyond the stark numbers, what happens next for higher education may depend on whether colleges treat AI as a threat or an opportunity. Forward-looking universities are rushing to deploy AI across several different departments, using it as a resource to improve the entire student journey. Instead of a static four-year experience, education is shifting toward a continuous model in which skills are updated and careers are moving in parallel with industry needs.

On the other hand, if universities hesitate, the result could become career barriers, a lack of preparedness, and unpredictable futures. Without using AI as a source to unveil human potential, students will go unnoticed and overlooked in this volatile market.

For students already in the midst of relying on AI to get work done, it is important to remember why automation is here in the first place. It may look like a means of cheating, but it also serves as a way to make well-informed career decisions.

AI may always feel like a hurdle, but like many educators might put it, there is also immense promise surrounding it. When designed around the student, it can illuminate pathways that were once unreachable, and it can also add clarity and resilience to anyone seeking a job post graduation. 

Artificial intelligence has become an integral part of elite sports, quietly transforming how global events like the Olympics and the FIFA World Cup are prepared for, competed in, and experienced. What was once driven primarily by instinct and observation is now increasingly informed by data, algorithms, and advanced performance analysis, reshaping how teams compete and how analysts contribute to success on the world’s biggest stages.

Performance analysis has long been central to elite sport, but AI has expanded both its scale and speed. Modern tracking systems powered by machine learning and computer vision process vast quantities of data in real time, capturing player movement, positioning, and tactical behavior throughout competition and training. At major tournaments where the margin between victory and defeat is often minimal, these insights allow coaching staff to make quicker, more informed decisions. Rather than relying solely on post-event analysis, teams can adjust tactics during competition using live data that reveals emerging patterns and vulnerabilities.

AI has also become deeply embedded in athlete preparation and health management. Wearable sensors and monitoring technologies collect detailed physiological data such as workload, recovery, and fatigue indicators. AI systems analyze these signals to identify injury risks and guide individualized training plans. For Olympic athletes who may train for years for a single performance window, this precision can help preserve both peak condition and long-term health, while also improving consistency under pressure.

Despite the growing sophistication of AI tools, human analysts remain essential to translating data into competitive advantage. Wendy Lynch, PhD, founder of Analytic Translator and an expert in human behavior and technology adoption, explains that AI on its own does not create better decisions. Data must be interpreted within human, cultural, and situational contexts. Analysts play a critical role by framing AI-generated insights in ways coaches and athletes can understand, trust, and act upon. Without this translation layer, even the most advanced models risk being ignored or misapplied during high-stakes competition.

This partnership between human analysts and AI has shortened the distance between insight and action. By automating data collection and pattern recognition, AI allows analysts to focus on tactical interpretation and strategic communication. In fast-moving environments such as World Cup knockout rounds or Olympic finals, this collaboration enables teams to respond quickly while still relying on human judgment and experience.

Officiating has also been reshaped by AI-enabled technologies designed to improve fairness and consistency. Systems such as goal-line technology and semi-automated offside detection support referees by reducing human error and providing clearer evidence for decisions. Future developments, including AI-generated three-dimensional player models, aim to make complex rulings more transparent for players and fans alike, reinforcing trust rather than removing human oversight.

AI’s influence extends beyond competition into the fan experience. Broadcasters increasingly use AI to automate highlight production, generate real-time statistics, and tailor content to different audiences. At recent Olympic Games, these tools enhanced storytelling and analysis, offering viewers deeper insight into performance while maintaining the emotional appeal that defines global sport.

As AI becomes more embedded in elite competition, it also raises questions about access, equity, and over-reliance on automated systems. Advanced technologies are not evenly distributed, and sport must guard against widening gaps between those who can afford cutting-edge tools and those who cannot. There is also a need to ensure that data-driven decisions remain transparent and accountable.

When used responsibly, however, AI does not replace the human element that makes sport compelling. Instead, it amplifies it. At the Olympics and the World Cup, where performance, pressure, and public attention converge, the collaboration between AI systems and skilled analysts demonstrates that winning strategies still depend on human understanding, judgment, and the ability to turn insight into action.