Lisa’s day starts with Netflix recommending exactly the show she wants to watch, continues with Visa automatically blocking a fraudulent transaction on her credit card, and ends with her company’s AI assistant helping her analyze quarterly sales data in natural language. She doesn’t realize it, but every one of these interactions is powered by the same underlying technology: foundation models.
While the tech world obsesses over ChatGPT and generative AI, a quieter revolution has been transforming how businesses actually operate. Foundation models — the technology behind these headline-grabbing applications — are already embedded in systems that millions of people use every day. Most just don’t know what to call them.
In just a few minutes we can understood what foundation models really are, why they’re different from everything that came before, and how they’re quietly reshaping entire industries.
What Foundation Models Actually Are (And Why the Name Matters)
Let me start with a definition that actually makes sense: A foundation model is an AI system trained on vast amounts of general data that can be adapted to perform many different tasks without starting from scratch each time.
Think of it like this: instead of training a new AI model for every specific task (like a human learning to drive a car, then separately learning to ride a motorcycle, then separately learning to operate a boat), foundation models learn general principles that apply across many related tasks (like understanding transportation, navigation, and vehicle control in general).
The term “foundation model” was coined by Stanford researchers in 2021, but the concept has been quietly powering major technological breakthroughs for years. When Netflix revolutionized entertainment recommendations, when Visa achieved sub-0.1% fraud rates processing 150 million daily transactions, and when Google Translate became remarkably accurate — they were all using foundation model approaches, even if they didn’t call them that.
The Three Pillars That Make Foundation Models Different
1. Scale: Learning From Everything
Traditional AI models are trained on carefully curated datasets for specific tasks. A fraud detection system might be trained only on transaction data. A recommendation engine might use only viewing history.
Foundation models take a radically different approach: they’re trained on massive, diverse datasets that capture broad patterns across domains. GPT-4 was trained on hundreds of billions of words from books, websites, and documents. Visa’s fraud detection models process data from billions of transactions across every type of purchase, in every country, across decades of payment history.
This scale enables something remarkable: the models learn general principles about human behavior, business patterns, and system interactions that apply across many different contexts.
2. Adaptability: One Model, Many Applications
Here’s where foundation models become truly revolutionary. Once trained, they can be quickly adapted for new tasks without requiring massive amounts of new training data.
Netflix’s recommendation foundation model doesn’t just suggest movies — it powers content acquisition decisions, thumbnail optimization, and streaming quality adjustments. The same model that learns viewing preferences can also predict which content will be successful and optimize delivery infrastructure.
At DeepTempo, our LogLM foundation model started with network security but is quickly expanding into, digital forensics. The same intelligence that detects security anomalies also identifies patterns and holistic attack histories.
3. Transfer Learning: Knowledge That Compounds
This is the secret sauce that makes foundation models so powerful: they can apply knowledge learned in one domain to completely different problems.
When Google’s DeepMind used foundation model techniques to solve protein folding with AlphaFold, they weren’t starting from scratch. They leveraged patterns learned from game-playing, optimization, and spatial reasoning to tackle one of biology’s hardest problems.
Similarly, when we built our LogLM for cybersecurity, we discovered it was perfect to look at historical data. The model hadn’t been explicitly trained for forensics tasks, but the underlying patterns of normal vs. abnormal system behavior transferred across domains.
Who’s Actually Using Foundation Models (Hint: More Than You Think)
The Obvious Players
OpenAI (ChatGPT): The poster child for foundation models, demonstrating how one model can handle writing, coding, analysis, and conversation.
Anthropic (Claude): Another conversational AI that showcases foundation model versatility across reasoning, writing, and analysis tasks.
Google (PaLM, Gemini): Powers everything from search improvements to document analysis to code generation.
The Financial Services Revolution
Visa: Processes over 150 million transactions daily with fraud rates below 0.1% using foundation models that understand global payment patterns.
JPMorgan Chase: Uses foundation models for fraud detection, trading algorithms, customer service, and regulatory compliance analysis.
American Express: Leverages foundation models for real-time fraud prevention, customer service automation, and credit risk assessment.
The competitive advantage is enormous. While traditional banks still rely on rule-based systems that generate thousands of false positives, foundation model-powered institutions can identify actual fraud with surgical precision.
Entertainment and Media Giants
Netflix: Their recommendation engine is essentially a foundation model that understands viewer preferences, content characteristics, and engagement patterns to optimize everything from what you see to when content is suggested.
Spotify: Uses foundation models for music recommendations, playlist generation, podcast suggestions, and even predicting which artists might become popular.
Disney: Employs foundation models for content recommendation across their streaming platforms, theme park optimization, and merchandising decisions.
The Enterprise Software Transformation
Salesforce: Einstein GPT integrates foundation model capabilities into their CRM platform, enabling natural language customer insights and automated sales processes.
Microsoft: Copilot integration across Office 365 demonstrates how foundation models can enhance productivity tools that millions of workers use daily.
ServiceNow: Uses foundation models to automate IT service management, enabling natural language incident resolution and automated workflow optimization.
The Hidden Infrastructure Players
This is where it gets interesting. Many companies you wouldn’t expect are leveraging foundation models:
FedEx: Uses foundation models for route optimization, delivery prediction, and logistics planning across their global network.
Walmart: Employs foundation models for inventory management, supply chain optimization, and customer behavior prediction.
General Electric: Leverages foundation models for predictive maintenance, equipment optimization, and manufacturing process improvement.
Traditional AI vs. Foundation Models: The Fundamental Difference
Traditional AI: The Specialist Approach
Traditional machine learning works like hiring specialists for specific jobs:
- Train a model to detect credit card fraud
- Train a different model to recommend products
- Train another model to optimize logistics
- Each model requires extensive training data for its specific task
- Models can’t share knowledge between domains
- New tasks require starting from scratch
Foundation Models: The Generalist Approach
Foundation models work like hiring exceptionally intelligent generalists:
- Train one model on massive, diverse datasets
- Adapt that model for multiple related tasks
- Knowledge transfers between domains automatically
- New tasks require minimal additional training
- One model can handle multiple business functions
The Business Model Revolution
Foundation models are creating entirely new business models and destroying others.
The Platform Effect
Instead of selling multiple specialized AI tools, companies can build AI platforms that handle numerous business functions. This creates massive economic advantages:
- Reduced integration complexity
- Shared infrastructure costs
- Unified data models
- Cross-functional insights
The Consulting Disruption
Traditional consulting firms charge premium rates for specialized expertise. Foundation models can provide expert-level analysis across multiple domains, democratizing access to high-quality business intelligence.
McKinsey’s recent research shows that foundation models could automate 60–70% of traditional consulting activities while improving accuracy and reducing costs.
The Software Consolidation
Why buy separate tools for customer service, sales analysis, marketing automation, and business intelligence when one foundation model can handle all these functions through natural language interfaces?
This is why traditional software vendors are scrambling to add AI capabilities — they’re facing potential consolidation of their entire market category.
Industry-Specific Applications
Healthcare: Clinical Decision Support
Epic Systems: Integrates foundation models into electronic health records for clinical decision support, automated documentation, and patient risk assessment.
Physicians can ask: “What are the differential diagnoses for a 45-year-old female with chest pain and elevated troponins?” and receive comprehensive analysis drawing from vast medical literature and case histories.
Legal: Document Analysis and Research
Harvey AI: Uses foundation models to analyze contracts, conduct legal research, and draft documents, enabling lawyers to focus on strategy rather than document review.
Legal teams can query: “Find all clauses in our vendor contracts that could create liability under new privacy regulations” and receive detailed analysis across thousands of documents.
Manufacturing: Predictive Maintenance
Siemens: Employs foundation models to predict equipment failures, optimize maintenance schedules, and improve manufacturing efficiency.
Engineers can ask: “Which machines are most likely to fail in the next 30 days based on current sensor readings?” and receive prioritized maintenance recommendations.
The Technical Advantages That Matter to Business
Faster Time to Value
Traditional AI projects take 6–18 months to develop and deploy. Foundation models can be adapted for new business applications in weeks, days or in some cases minutes.
Lower Total Cost of Ownership
Instead of maintaining dozens of specialized AI models, organizations can leverage one foundation model for multiple business functions, dramatically reducing infrastructure and maintenance costs.
Continuous Learning
Foundation models improve automatically as they process more data, unlike traditional systems that require manual retraining and updating.
Natural Language Interface
Business users can interact with foundation models through conversation rather than requiring technical expertise to configure specialized tools.
The Competitive Implications
Organizations leveraging foundation models have fundamental advantages:
Speed: Deploy new AI capabilities in days rather than months Flexibility: Adapt to new business requirements without rebuilding systems Intelligence: Access expert-level analysis across multiple business domains Efficiency: Consolidate multiple AI functions into unified platforms Innovation: Discover unexpected connections between different business areas
These aren’t incremental improvements, they’re step-function changes in business capability.
What This Means for Your Organization
If your organization isn’t exploring foundation models, you’re likely falling behind competitors who are. The advantages compound over time, creating competitive gaps that become impossible to close.
Start With Use Cases, Not Technology
Don’t begin by trying to understand transformer architectures or training methodologies. Start by identifying business problems that could benefit from intelligent analysis across multiple domains.
Look for Cross-Functional Opportunities
Foundation models excel where traditional approaches struggle: problems that span multiple business functions or require expertise from different domains.
Experiment With Existing Platforms
You don’t need to build foundation models from scratch. Platforms like OpenAI’s GPT-4, Anthropic’s Claude, or specialized models like our LogLM at DeepTempo can be quickly adapted for specific business needs.
The Future Is Already Here
Foundation models aren’t experimental technology — they’re production systems powering critical business operations for leading organizations worldwide. The question isn’t whether foundation models will transform business, but whether your organization will lead that transformation or be disrupted by it.
Netflix didn’t become the dominant entertainment platform by building better video rental stores. Visa didn’t revolutionize payments by creating better paper check processing. And the organizations that will dominate the next decade won’t get there by incrementally improving traditional approaches.
They’ll get there by understanding that foundation models represent a fundamental shift in how intelligent systems work — and adapting their business strategies accordingly.
The foundation model revolution is happening now. The only question is whether you’ll be part of building the future or struggling to catch up to it.
Because in a world where AI can understand and operate across all business domains simultaneously, specialized approaches aren’t just inefficient — they’re obsolete.
The future belongs to organizations that embrace intelligence over process, outcomes over activities, and foundation models over fragmented tools.
