AI Infrastructure Commoditization and Business Model: Leveraging Proprietary Data Across Industries and Markets

The commoditization of artificial intelligence (AI) infrastructure—spanning GPUs, foundational models, and cloud platforms—has fundamentally reshaped competitive dynamics across industries. As AI technologies become increasingly accessible, differentiation now lies in their application to domain-specific challenges and workflows. This paper examines how businesses leverage proprietary data, vertical specialization, and ethical governance to sustain competitive advantage in the era of AI commoditization. Through case studies of Tempus (healthcare), John Deere (agriculture), Sette and NeuralMed (Brazil's emerging markets), Hugging Face (open-source AI), and Cresta (outcome-based pricing), I demonstrate how commoditized AI infrastructure enables innovation when paired with strategic data utilization. The findings highlight the critical role of sustainability, human-AI collaboration, and regulatory foresight in shaping the future of generative AI across both established and emerging markets.

Introduction

The democratization of AI infrastructure—GPUs, large language models (LLMs), and cloud services—has transitioned cutting-edge technologies into widely available utilities. Training costs for advanced LLMs have dropped by over 80% since 2020, while open-source alternatives now rival proprietary systems in performance. This shifts historical patterns where transformative technologies evolve into commoditized utilities, eroding traditional competitive advantages.

For businesses, this presents a dual reality: while AI adoption barriers have never been lower, differentiation now centers on applying commoditized tools to solve industry-specific challenges. This paper examines how companies sustain advantage through vertical specialization, proprietary data, and ethical governance, drawing on platform theory and real-world applications across healthcare, agriculture, and emerging markets.

Platform Theory and Generative AI Ecosystems

Platform theory categorizes digital ecosystems into three models: Innovation Platforms that enable third-party development (e.g., Hugging Face's open-source models), Transaction Platforms that facilitate exchanges (e.g., Cresta's outcome-driven pricing), and Hybrid Platforms that combine both (e.g., Microsoft's Copilot Stack).

In AI commoditization, these layers increasingly intersect. Foundational models like GPT-4 serve as innovation platforms, while cloud providers offer transaction-enabled infrastructure. Hybrid models dominate sectors like healthcare and agriculture, where proprietary data and domain expertise drive value.

Case Studies: Leveraging Proprietary Data for Differentiation – Globally and in Brazil

Tempus: Precision Medicine Through Genomic Data

Tempus continues to redefine precision medicine by integrating advanced AI with proprietary clinical and genomic datasets. The company’s AI assistant, Tempus One, leverages a proprietary Large Language Model (LLM) Agent Infrastructure to analyze unstructured data such as progress notes, pathology reports, and treatment histories across millions of patient records. This system enables clinicians to query identified patient cohorts, identifying candidates for clinical trials by analyzing over 30 million medical exams and 8 million reports, which reduces trial enrollment delays by 40%. Tempus’ generative AI capabilities also synthesize cohesive patient timelines, aggregating data from 7 million monitored lives to create longitudinal views of treatment outcomes.

The company’s collaborations with partners like Imagine have resulted in AI-powered biomarker prediction panels for non-small cell lung cancer (NSCLC), accelerating diagnostic workflows and enabling personalized therapeutic strategies. Financially, Tempus reported a 30% year-over-year revenue growth in 2024, reaching $693 million, driven by increased adoption of its genomic testing services and AI-driven data solutions. In 2025, Tempus launched xH, its first whole-genome sequencing (WGS) test for hematologic malignancies, combining genomic data with clinical records to guide therapy selection.

John Deere: Agricultural Efficiency at Scale

John Deere’s FarmSight platform exemplifies how IoT and AI transform agricultural productivity. By analyzing data from 158 million acres of farmland, the platform integrates IoT sensors and satellite imagery to deliver hyper-localized insights. Farmers using FarmSight have reduced fertilizer costs by 13% and improved crop yields by 25%, while soil moisture sensors optimize irrigation systems, saving 30% in water usage. The company’s See & Spray technology, equipped with AI-powered cameras and ExactApply nozzles, targets weeds with precision, saving 8 million gallons of herbicide in 2024 alone—a 59% reduction compared to conventional methods.

Looking ahead, John Deere’s 2025 innovations include Smart Planting systems with Seed-Level Sensing and Active Vacuum Automation for fertilizer application, projected to boost operational efficiency by 20%. As the global IoT-in-agriculture market grows toward $19 billion by 2032, John Deere’s precision farming tools command a 35% market share, solidifying its position as a leader in AI-driven agricultural innovation.

Sette: AI-Driven Agribusiness in Brazil

Sette exemplifies well how AI and proprietary data can transform agribusiness in emerging markets. The company has extensively analyzed approximately 148 million acres of soy and corn cultivation across 1,200 municipalities in 20 Brazilian states. This massive dataset, combined with satellite imagery and deep learning algorithms, offers Sette unprecedented insights into agricultural productivity, risk assessment, and environmental compliance.

By leveraging this extensive proprietary dataset, Sette provides actionable insights that address key challenges in Brazilian agriculture, from risk assessment and environmental compliance to productivity optimization. This demonstrates the power of AI in transforming traditional industries and contributing to increased productivity and sustainability in one of the world's most important agricultural regions.

NeuralMed: Transforming Healthcare in Emerging Markets

NeuralMed exemplifies how proprietary data can drive transformative change in healthcare, particularly in emerging markets like Brazil. The company has built a vast repository of medical data that serves as a foundation for its AI-driven solutions, monitoring more than 7 million lives, processing over 30 million medical exams, analyzing 8 million reports, and partnering with more than 200 hospitals.

This extensive primary data source allows NeuralMed to apply generative AI to healthcare workflows with unprecedented scale and accuracy. The company collects large-scale datasets from health providers—including legacy systems—and uses AI algorithms to process sensitive information into anonymized datasets suitable for analysis.

NeuralMed's intelligent triage systems prioritize patients based on urgency by analyzing medical imaging and clinical reports. For example, its algorithms process X-rays, CT scans, EKGs, and text-based clinical notes to identify deviations requiring immediate attention. This reduces delays in care for critical patients while optimizing clinical workflows for healthcare providers.

By leveraging its massive proprietary dataset, NeuralMed can fine-tune its AI models to address specific challenges in the Brazilian healthcare system, such as resource allocation in underserved areas or early detection of regionally prevalent diseases. The scale of data—spanning millions of patients and exams across hundreds of hospitals—enables NeuralMed to identify patterns and insights that would be impossible to discern from smaller or more generalized datasets.

Hugging Face: Democratizing AI Through Open Collaboration

Hugging Face has revolutionized AI development by creating an open ecosystem that democratizes access to cutting-edge tools. The company’s Transformers library hosts over 450,000 models, including Meta’s Llama 3 and Stability AI’s Stable Diffusion, which rival proprietary systems in performance. Developers worldwide fine-tune these models for niche applications, such as medical diagnosis or financial forecasting, reducing R&D costs by 40% for enterprises like Roche and General Motors.?

The Spaces platform, a browser-based environment for hosting interactive AI demos, fosters community-driven innovation. Over 50,000 Spaces exist, ranging from chatbots to image generators. This initiative achieved 55.15% accuracy on the GAIA benchmark for autonomous research tasks, showcasing the potential of decentralized collaboration.

Hugging Face prioritizes sustainability through initiatives like carbon-adjusted model rankings, which incentivize energy-efficient AI development. The platform also supports federated learning, enabling privacy-preserving training across distributed datasets—a critical feature for healthcare and financial institutions. Domain-specific hubs, such as?Hugging Face for Healthcare, curate FDA-compliant models for medical use cases, bridging the gap between open-source innovation and regulatory requirements.

Cresta: Transforming Contact Centers with Outcome-Driven AI

Cresta has positioned itself as a leader in AI-driven contact center optimization by leveraging commoditized infrastructure to deliver tailored solutions. The company’s platform integrates generative AI to enhance agent performance, reduce operational costs, and improve customer experiences. At the core of Cresta’s offering is real-time agent assistance, which provides live behavioral guidance during customer interactions. For instance, agents receive prompts such as "assume the sale" or "negotiate payment terms," resulting in a 5% increase in conversion rates and a 2.5x boost in upsell revenue.

The platform’s Knowledge Assist feature unifies internal knowledge bases—including FAQs, product documentation, and troubleshooting guides—with generative AI to deliver context-specific answers during conversations. This integration reduces average handle time by 15% while ensuring compliance with company protocols.

Cresta’s conversation intelligence capabilities analyze over six million customer interactions to identify high-impact behaviors. One client, for example, increased promises-to-pay (PTP) by 30% by training agents to ask specific questions like “How close can you come?” during delinquency discussions.?

Cresta’s monetization strategy combines subscription-based access with outcome-driven pricing. Enterprises pay a base fee of approximately $150,000 annually for platform access, covering up to 100,000 calls. Beyond this, Cresta charges incrementally for resolved issues, such as successful upsells or retained customers, at rates like $1.20 per chat or $1.50 per call. This hybrid model aligns costs with measurable business outcomes, appealing to clients like Cox Communications, Holiday Inn Vacations, and Intuit, which report 25% higher revenue per lead and 30% faster agent onboarding.

Architectural control remains central to Cresta’s strategy. Its proprietary "Ocean-1" AI model ingests conversation data from over 1.5 million connected devices, creating a competitive advantage through domain-specific behavioral insights. By anonymizing customer data and embedding compliance checks (e.g., PCI-DSS) into workflows, Cresta ensures ethical governance while maintaining scalability.

Strategic Implications

The case studies illustrate three key strategies for businesses navigating the commoditization of AI infrastructure:

First, leveraging proprietary data is essential for creating defensible business models in commoditized markets. As seen with Tempus's healthcare platform, John Deere's agricultural analytics, Sette's agribusiness solutions in Brazil, and NeuralMed's triage systems in Brazil's healthcare sector, domain-specific datasets enable firms to develop tailored applications that competitors cannot easily replicate.

Second, adopting hybrid monetization strategies aligns pricing with measurable outcomes rather than usage alone. Cresta exemplifies this approach by tying revenue directly to efficiency gains rather than per-user fees.

Third, prioritizing ethical governance is critical as regulatory pressures grow globally. Companies like NeuralMed address privacy concerns by anonymizing sensitive medical data before analysis; Sette ensures socio-environmental compliance through satellite monitoring systems; Hugging Face promotes transparency through open-source frameworks.

Conclusion

The commoditization of AI infrastructure is not an endpoint but a catalyst for innovation. Companies like Tempus, NeuralMed, John Deere, and Sette demonstrate that differentiation arises from strategic data utilization, vertical specialization, and ethical governance. Emerging markets like Brazil highlight how region-specific challenges can drive globally relevant solutions.

Future success will depend on balancing technological capability with societal responsibility. As AI evolves, businesses must prioritize sustainability, collaboration, and adaptability to thrive in an era where infrastructure is universal, but insight remains proprietary.