Recently, while helping my son chart his career path, I found myself reflecting on my own journey. I’ve been in marketing before it was “cool” and building websites before the tech industry became the behemoth it is today.

Throughout my career, particularly in search engine optimization and digital experience optimization, I’ve witnessed incredible transformations in our field. But nothing compares to the massive shift happening right now with artificial intelligence.

The marketing landscape isn’t just evolving, it’s being completely reimagined. The hard truth I’ve come to recognize is this: marketers who don’t upgrade their technical skills may find themselves increasingly irrelevant in tomorrow’s job market.

The evidence is already appearing in unexpected places.

A colleague of mine—experienced and accomplished in marketing—recently applied for a role at a leading AI company. Despite impressive credentials, they were stopped in their tracks by an unexpected requirement: an SQL database test. They’d never worked with SQL and were immediately disqualified from consideration.

This got me thinking: how many marketers today could pass such a test?

My estimate is that only 10-20% of current marketing professionals. What does this mean for those of us who want to stay in the game—or perhaps even land roles at the companies defining our future?

To answer this question, I went straight to the source. I combed through job boards at Anthropic, OpenAI, Perplexity, and other AI pioneers. I analyzed every marketing-related position, identifying patterns in the technical skills these companies are seeking right now—and what this suggests about the development languages that will become essential for marketers in the near future.

The results were sobering. The technical bar is rising rapidly, and the timeline to adapt is shrinking.

In this article, I’ll share what I discovered about the specific development skills that will help marketers thrive in an AI-dominated marketplace.

The path ahead is challenging, but the alternative—becoming irrelevant in 3-4 years—is far worse. For marketers who want to remain competitive, the time to evolve is now.

Key Core Skills

My research across job boards at leading AI companies revealed a clear pattern: the technical bar for marketers is rising rapidly. Let’s examine the specific skills that are becoming essential for marketers who want to thrive in this new landscape.

Based on the job postings and trends from these companies, here are the key technical or coding skills that marketers should have:

Data Analysis and Visualization

• Skills Needed: Proficiency in tools like SQL, Python (for data analysis), and visualization platforms such as Tableau, Looker, or Power BI.
• Why Important: Roles like “Marketing Operations & Analytics Manager” at Anthropic emphasize the ability to analyze marketing performance data to inform strategy and decision-making.

Marketing Automation and CRM Tools

• Skills Needed: Experience with platforms like HubSpot, Marketo, Salesforce Marketing Cloud, or similar tools.
• Why Important: Modern AI companies rely on automated workflows for lead nurturing and customer engagement. For example, roles like “Growth Marketing Lead” may require expertise in setting up and optimizing automation campaigns.

Technical SEO and Web Analytics

• Skills Needed: Knowledge of HTML/CSS for SEO optimization, Google Analytics (GA4), and tools like SEMrush or Screaming Frog.
• Why Important: AI companies often focus on organic growth strategies. Understanding how to optimize content for search engines is critical for roles like “Growth Marketing Lead” or “Product Marketing Manager.”

Coding for Growth Experiments

• Skills Needed: Basic coding knowledge in JavaScript, Python, or R for A/B testing and growth experiments.
• Why Important: Growth-focused roles at AI companies often involve running experiments to optimize user acquisition funnels. Coding skills can help marketers collaborate with engineers or independently run tests.

API Knowledge

• Skills Needed: Familiarity with APIs (e.g., RESTful APIs) and how they integrate with marketing platforms.
• Why Important: Companies like Anthropic and Perplexity AI offer API-based products. Marketers need to understand how APIs work to effectively position these solutions to developers and businesses.

Content Management Systems (CMS)

• Skills Needed: Experience with CMS platforms (e.g., WordPress) and basic front-end coding (HTML/CSS).
• Why Important: Roles such as “Platform and Technical Communications Lead” may require managing technical content on websites.

Performance Marketing Skills

• Skills Needed: Proficiency in ad platforms (Google Ads, Facebook Ads Manager) combined with scripting skills for campaign optimization (e.g., Google Ads Scripts using JavaScript).
• Why Important: AI companies often run large-scale paid campaigns that require advanced targeting and optimization.

Product Marketing with Technical Depth

• Skills Needed: Ability to translate technical features into customer benefits; familiarity with product management tools like JIRA or Confluence is a plus.
• Why Important: Roles like “Product Marketing Manager” at Anthropic require marketers to communicate complex AI concepts effectively to diverse audiences.

Data Privacy and Compliance Awareness

• Skills Needed: Understanding of GDPR, CCPA, or other data privacy regulations; experience with consent management platforms.
• Why Important: AI companies handle sensitive user data, so marketers must ensure compliance in their campaigns.

Collaboration with Engineering Teams

• Skills Needed: Familiarity with GitHub/GitLab for version control or collaboration on technical projects.
• Why Important: Marketers at AI companies often work closely with engineers to launch technically complex campaigns or products.

Marketers aiming to work at modern AI companies like Perplexity AI or Anthropic should combine traditional marketing expertise with technical skills such as data analysis (SQL/Python), automation tools, basic coding (HTML/JavaScript), and API knowledge.

We now know the core skills needed to keep up. What does this mean in terms of learning coding specifically?

Programming languages that are most valued by AI companies for marketers

While understanding the broad categories of technical skills is important, I wanted to dig deeper into the specific programming languages that AI companies value most for their marketing teams.

These are the languages that appeared consistently across job postings and will likely become increasingly important in the years ahead.

For marketers aiming to thrive in AI companies, certain programming languages are particularly valued due to their relevance in data analysis, automation, and technical collaboration.

Based on the provided references, here are the most important programming languages for marketers:

Python

Why It’s Valued: Python is highly versatile and widely used for data analysis, automation, and machine learning. Its libraries, such as Pandas, NumPy, and Matplotlib, make it ideal for marketing analytics and data visualization tasks.

Applications for Marketers

• Automating reporting workflows.
• Analyzing campaign performance data.
• Building machine learning models for customer segmentation.

SQL

Why It’s Valued: SQL is essential for querying and managing relational databases, a critical skill for marketers working with large datasets or CRM systems.

Applications for Marketers

• Extracting insights from customer data.
• Creating custom dashboards.
• Optimizing database-driven marketing campaigns.

JavaScript (and TypeScript)

Why It’s Valued: JavaScript is crucial for web-based marketing tasks, such as optimizing landing pages or implementing tracking scripts. TypeScript adds structure and scalability to JavaScript projects.

Applications for Marketers

• Customizing front-end web elements.
• Implementing tracking pixels or tags.
• Collaborating with developers on website optimization.

HTML/CSS

Why It’s Valued: These are foundational languages for creating and editing web pages, particularly useful in email marketing and website updates.

Applications for Marketers

• Designing responsive email templates.
• Making minor edits to landing pages or content management systems.

R

Why It’s Valued: R is powerful for statistical analysis and data visualization, making it a good alternative to Python for marketers with a focus on advanced analytics.

Applications for Marketers

• Conducting predictive analytics.
• Visualizing campaign performance trends.

Java

Why It’s Valued: While less common among marketers, Java is useful in AI-related roles where integration with backend systems or APIs is required.

Applications for Marketers

• Collaborating on API-based marketing tools.
• Supporting technical product marketing efforts.

C++

Why It’s Valued: C++ is occasionally relevant in AI-heavy environments where performance optimization is critical, though it’s more niche for marketers.

Applications for Marketers

• Working with embedded AI systems in marketing hardware (e.g., IoT devices).

What did we learn here?

• For marketers in AI companies, Python and SQL are the most universally applicable due to their roles in data analysis and automation.
• JavaScript (and TypeScript) is essential for web-based tasks, while HTML/CSS supports creative roles like email marketing.
• Advanced marketers may benefit from learning R or Java to enhance their analytical or technical capabilities.

Think I’m done? Lets take this even one step further.

Emerging programming languages that AI companies are starting to value

Beyond the established programming languages, forward-thinking marketers should also keep an eye on emerging technologies.

The following languages are gaining traction in AI environments and could represent valuable additions to a marketer’s technical toolkit.

Emerging programming languages are gaining traction in AI companies due to their unique features and advantages in performance, scalability, and safety.

Let’s take a look at the list.

Rust

Why It’s Valued: Rust is increasingly popular for its memory safety, concurrency model, and high performance, making it a strong contender for replacing C++ in systems programming.

Applications in AI

• High-performance computing.
• Building reliable and efficient AI infrastructure.
• Robotics and embedded systems.

Rust is being embraced in sectors that traditionally relied on C++, such as robotics and AI frameworks.

Go (Golang)

Why It’s Valued: Go is known for its simplicity, efficient concurrency handling, and scalability, making it ideal for cloud-native applications and distributed systems.

Applications in AI

• Backend services for AI platforms.
• Cloud-based AI tools and microservices.

Go is increasingly used in large-scale projects like Kubernetes and Ethereum, which intersect with AI use cases.

Julia

Why It’s Valued: Julia combines the ease of Python with the performance of C++, making it ideal for numerical computing and scientific applications.

Applications in AI

• High-performance machine learning models.
• Data manipulation and scientific computing.

Julia is gaining traction for tasks requiring heavy computation, such as natural language processing and large-scale simulations.

TypeScript

Why It’s Valued: TypeScript builds on JavaScript with static typing, making it more scalable for large projects while retaining flexibility.

Applications in AI

• Developing web-based AI applications using frameworks like TensorFlow.js.
• Frontend development for interactive AI tools.

TypeScript is becoming a standard for UI-heavy AI projects due to its growing ecosystem.

Scala

Why It’s Valued: Scala’s functional programming capabilities and compatibility with Java make it suitable for handling complex data pipelines and machine learning algorithms.

Applications in AI

• Big data processing with frameworks like Apache Spark.
• Machine learning model development.

Scala is used in enterprise-level AI solutions requiring robust data handling.

Summary

Emerging programming languages like Rust, Go, Julia, TypeScript, and Scala are increasingly valued by AI companies for their performance, scalability, safety features, and adaptability to modern AI challenges.

These languages complement established ones like Python by addressing specific needs such as high-performance computing (Rust/Julia), cloud-native development (Go), or scalable front-end development (TypeScript).

The Technical Evolution: A Call to Action

The writing is on the wall. The marketing professionals who adapt to this technical transformation will not only survive but position themselves as invaluable assets to the companies shaping our future. Those who resist this evolution risk becoming expensive overhead in an increasingly automated world.

Your Strategic Roadmap

Start immediately with these foundational skills:

• SQL for database queries and customer data analysis
• Python for automation, analytics, and machine learning basics
• JavaScript for web optimization and tracking implementation

Build systematically toward advanced capabilities:

• Master API integration for platform connectivity
• Develop growth experimentation frameworks using code
• Learn marketing automation beyond basic platform usage

Stay ahead of emerging trends:

• Monitor Rust and Go for performance-critical applications
• Explore Julia for advanced data science applications
• Consider TypeScript for scalable web development projects

The Revenue Reality

Companies are already making hiring decisions based on these technical competencies. The SQL test that eliminated my experienced colleague isn’t an anomaly—it’s the new standard. AI companies understand that technical marketing skills directly correlate with revenue performance and operational efficiency.

The marketing professionals commanding six-figure salaries at Anthropic, OpenAI, and Perplexity aren’t just strategists—they’re technical practitioners who can implement, measure, and optimize at the code level.

Your Next 90 Days

The gap between technical and non-technical marketers will widen exponentially over the next three years. The choice is binary: invest in these skills now or accept declining relevance in the marketplace.

Begin with SQL and Python. Allocate 5-10 hours per week to structured learning. Build projects that demonstrate capability rather than just theoretical knowledge. The marketers who start today will have a decisive advantage over those who wait for the transition to become obvious to everyone else.

The AI revolution isn’t coming—it’s here. The question isn’t whether you need these skills, but whether you’ll acquire them before your competition does.