It’s interesting how quickly the LLM space has become commoditized. We're now in this wild era where there's a new model dropping almost every week—Claude, GPT-4, Gemini, Llama, Grok. We’re seeing new models emerge at a staggering pace, each claiming to be marginally better than the last.

All this energy focused on building slightly better general-purpose models is actually missing the bigger opportunity. Instead of creating the smartest general-purpose AI, the real value lies in building specialized applications that solve specific problems for specific industries.

The Great LLM Commoditization

LLMs are becoming commodities – full stop. There's a billion of them competing in an arms race to get marginally smarter, and while that's fascinating from a research perspective, it's leading to diminishing returns for actual business applications.

The pattern we're seeing with AI mirrors what we've seen with every other foundational technology. Web infrastructure became commoditized. Cloud computing became commoditized. And now we're watching the same thing happen with large language models.

In each case, the real value wasn't captured by the underlying infrastructure providers but by the application layer built on top.

• Amazon didn't win by creating better internet protocol. They won by building the most effective application of those protocols for e-commerce.

• Uber didn't win by creating better mapping technology. They won by applying it to a specific vertical (transportation) in a way that solved real user problems.

The same will be true for AI. The winners won't be the companies with marginally better foundation models, but rather those who build the most effective applications of those models for specific industries and use cases.

Why Marketing Needs Specialized AI Solutions

Marketing is especially ripe for verticalized AI solutions because it sits at an intersection of creativity, psychology, data analysis, and business strategy. Generic AI models struggle with this complexity because they lack the specialized understanding of marketing workflows, channels, and objectives.

When you drop a generic prompt into ChatGPT asking it to create a marketing campaign, you're getting the averaged output from a model trained on everything from scientific papers to Reddit posts. It wasn't built specifically to understand marketing psychology, brand positioning, or campaign performance metrics.

The problems with generic AI in marketing include:

1. Surface-level outputs: General LLMs can produce basic copy but miss the nuanced understanding of brand voice, audience psychology, and competitive positioning that drives actual results.

2. Disconnected workflows: Marketing involves complex, interconnected processes from audience research to content creation to performance analysis. Generic AI treats each step as an isolated task rather than understanding the full ecosystem.

3. Missing domain expertise: Marketing has its own language, metrics, and best practices that are only partially captured in general training data.

What marketers really need is an AI suite built specifically for their workflows, with deep understanding of marketing frameworks, metrics, and channel-specific requirements. They need tools that generate content while helping them think through strategy, analyze performance, and optimize across multiple channels.

The Application Layer is Where Magic Happens

The real value creation in AI isn't happening at the model level – it's happening at the application layer. This is where specialized knowledge, workflows, and user experiences come together to solve real business problems.

Take a simple example: a generic LLM can write an email. But a marketing-specific AI application can:

• Understand your customer segments and their purchase history

• Know which messaging has historically performed best with each segment

• Recognize where customers are in their journey

• Automatically adapt tone and content based on previous engagement metrics

• Integrate directly with your email platform and analytics tools

• Test variations and learn from performance data

That's the difference between a generic tool and a verticalized solution that drives real business outcomes.

The same principle applies across every marketing function:

• Social media management

• Content strategy

• Performance marketing

• Campaign planning

• Audience research

• Competitive analysis

In each area, the value isn't in having a slightly smarter general AI but in having purpose-built applications that deeply understand the specific challenges and workflows of that function.

Building the AI Marketing Suite of the Future

So what does this mean for the future of marketing technology? I believe we're heading toward a world where the most effective marketing teams will utilize comprehensive, AI-powered marketing suites tailored to their specific needs.

Not just standalone tools but comprehensive platforms that understand the entire marketing lifecycle and how different elements interact. They'll be built on top of foundation models but with specialized training, interfaces, and integrations designed specifically for marketing workflows.

The key components of these platforms will include:

1. Specialized training data: Models fine-tuned on marketing content, case studies, performance metrics, and industry-specific knowledge.

2. Workflow integration: Seamless connections between ideation, creation, distribution, analysis, and optimization – reflecting how marketing actually works.

3. Channel expertise: Deep understanding of different marketing channels, their unique requirements, and how they work together.

4. Performance intelligence: Built-in analysis capabilities that connect creative decisions to business outcomes.

5. Institutional memory: The ability to learn from your specific brand history, audience data, and past campaign performance.

The most powerful aspect of these specialized platforms is that they amplify human creativity rather than replace it. Rather than just execute tasks, they'll serve as thought partners that help marketers think through complex problems, identify opportunities, and create more effective campaigns.

The Transition from General to Vertical AI

We're already starting to see this shift happen. The early days of AI in marketing were all about generic tools:

• Simple copywriting assistants

• Basic image generators

• Rudimentary chat assistants

We're now entering a new phase, where more sophisticated, purpose-built marketing AI solutions are emerging.

This transition will accelerate as the limitations of general-purpose AI become more apparent. Marketers will increasingly demand tools that understand their specific challenges and workflows rather than settling for generic solutions that require extensive customization.

The winners in this next phase will be the companies that combine AI capabilities with deep marketing expertise to create true verticalized solutions. They'll be the organizations that understand marketing is less about generating content and more about driving business results through interconnected strategies across multiple channels.

What This Means for You

If you're a marketing leader, this shift has important implications for your technology strategy:

1. Be wary of generic solutions: Tools that promise to "AI-enable everything" without specific marketing expertise are likely to deliver disappointing results.

2. Look for vertical integration: The most valuable tools will understand how different marketing functions connect rather than optimizing single tasks in isolation.

3. Value marketing expertise: The best AI solutions will come from teams that deeply understand marketing challenges, not just AI technology.

4. Build for your workflows: Choose tools that adapt to how your team actually works rather than forcing you to change your processes.

5. Prioritize business outcomes: Focus on solutions that directly connect to marketing performance metrics, rather than merely generating more content.

The future of AI in marketing rests on having purpose-built tools that deeply understand marketing challenges and help you solve them more effectively.

The Dawn of Verticalized AI

I believe we're standing at the beginning of a major shift in how AI is applied to marketing. The general-purpose model race will continue, but the real innovation – and the real value – will come from specialized applications built for specific industries and functions.

For marketing teams, this means we're moving from the "wow, AI can generate content" phase to the "wow, AI can transform our entire marketing approach" phase.

This is the future of marketing technology – smarter AI that's specifically built to solve marketing challenges. I think that future is a lot closer than most people realize.