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Aug 21 2025
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How AI is going to change software development

by Eric Picard

As someone who’s spent decades watching technology waves crash over the software industry, I find myself constantly recalibrating my predictions about AI’s impact on how we build software. Two years ago, I wrote about the potential for AI to create massive productivity gains and fundamentally alter development practices. Now, with some genuinely surprising developments happening right under our noses, I can see we’re heading somewhere much more interesting than I initially thought.

The productivity gains from AI tools like GitHub Copilot, ChatGPT, Cursor, and Replit are real, but what’s caught my attention is how we’re seeing two distinct paths emerge. Professional developers are using AI as incredibly powerful assistants, while non-developers are essentially managing teams of AI-powered junior developers through conversational interfaces. Both approaches are working, just for different types of projects and different scales of ambition.

Two Worlds of AI-Powered Development

Professional developers have largely embraced AI as sophisticated tooling that makes them dramatically more effective. They’re using AI for code generation, debugging assistance, refactoring, and rapid prototyping, but they’re doing it within established architectural patterns and development methodologies. The productivity gains are genuine – I’m consistently hearing reports of 30-50% improvements in specific tasks – but these developers maintain architectural oversight and code comprehension. I’ve talked to several developers who are often referred to as “10x” developers, meaning they’re ten times more effective than others on their teams. These particular developers were 10x devs at big tech companies, so certainly more than 10 times most developers. Each of them has told me that they are 100X-ing themselves using AI. So this is not a small change.

Then there’s what Andrej Karpathy coined “vibe coding” – developers describing what they want in plain English and accepting AI-generated code without necessarily understanding every line. This “fully giving into the vibes” approach emphasizes rapid experimentation over careful architectural planning. Y Combinator reported that 25% of their Winter 2025 batch had codebases that were 95% AI-generated, which represents a fundamentally different relationship with code creation.

The key insight is that these aren’t competing approaches – they’re serving different needs. Professional development teams use AI to accelerate work within proven frameworks, while vibe coding enables non-developers or small teams to build functional applications that would have been impossible for them to create just a few years ago.

Replit: DevOps Intelligence for the AI Era

Replit’s explosive growth – from $10M to $100M ARR in less than six months – illustrates something important about where this is heading. Replit is actually the intelligent evolution of DevOps principles, bringing continuous integration, automated testing, and deployment automation to AI-driven development.

Traditional DevOps emerged because managing infrastructure, deployment pipelines, and scaling manually was becoming impossible at scale. Smart development teams adopted CI/CD, automated testing, infrastructure as code, and monitoring because these practices made complex systems manageable and reliable.

Replit takes these same principles and makes them accessible through conversational interfaces. When their AI Agent selects a tech stack, generates code, sets up databases, and handles deployment, it’s not eliminating DevOps – it’s automating DevOps intelligence so that non-developers can benefit from these practices without needing to understand them deeply.

This matters because it’s expanding who can build functional software. You don’t need to understand Kubernetes, Docker, CI/CD pipelines, or infrastructure configuration to get the benefits of modern deployment practices. The AI handles the complexity while applying proven DevOps principles under the hood.

The Custom Application Renaissance

What excites me most about this trend is that we’re finally approaching the custom application future that the internet promised but never quite delivered. For twenty years, we’ve talked about having rich, customized web applications for internal business processes, but the development overhead made it impractical for most organizations.

Now we’re entering an era where custom web applications for fairly complex business tasks can be built quickly and cost-effectively. The “intranet” that organizations have wished for – dynamic, task-specific applications that actually solve their particular workflow problems – is becoming achievable. Just this week I built two very powerful internal apps for one of my clients inside of CharmIQ. These apps automate extremely intensive processes that were bottlenecks for my client. And three weeks ago, I had no idea how to do this. I’d have hired someone to build them.

I’m hearing about custom applications for everything from inventory management to customer onboarding to internal reporting, applications that would have required months of development and significant ongoing maintenance. These aren’t replacing enterprise software entirely, but they’re filling the gaps where off-the-shelf solutions don’t quite fit.

The Architecture Challenge Ahead

As these AI-powered development approaches mature, we’re approaching a fundamental architectural question. Current approaches work well for their respective use cases, but we need architectural patterns optimized for AI collaboration rather than just AI assistance. Right now AI developers are about as talented as junior developers – with maybe 2-3 years of experience. They break things a lot, the code isn’t efficient, they’re not always architecting things properly. But that’s a short-term problem – we’re only a few years away from AI developing software as well as any human, or better. What happens then?

The traditional monolithic applications or coarse-grained microservices that work well for human development teams may not be optimal for AI-powered development environments. Some teams experiment with treating code as completely disposable, letting AI regenerate implementations for each iteration. This works for prototypes and simple applications, but it breaks down for complex systems where you lose accumulated knowledge and performance optimizations.

Components: The Architecture for AI Collaboration

I think the future lies in component-based architectures that provide the right granularity for AI systems to work effectively. This draws inspiration from earlier component models like Microsoft’s COM objects, adapted for modern cloud environments and AI capabilities.

Applications would be built from well-defined components with stable interfaces and clear functional boundaries. Each component handles specific capabilities – user authentication, payment processing, data transformation, content generation – with explicit contracts for inputs and outputs. The critical insight is that while these interfaces remain stable, AI systems can continuously optimize, refactor, or completely reimplement the internal logic of individual components.

This architecture offers several advantages for AI-powered development. Components provide bounded problem spaces where AI systems can operate effectively without breaking broader system functionality. The stable interfaces enable comprehensive testing and debugging, while internal flexibility allows for continuous optimization based on performance data and changing requirements.

What This Looks Like in Practice

Over the next five to ten years, I expect we’ll see component registries emerge that catalog available functionality with detailed specifications. AI systems will continuously monitor component performance and generate optimized versions for testing and gradual deployment.

Applications will become more dynamic, automatically reconfiguring by swapping component implementations based on load patterns, user behavior, or resource availability. Unlike current microservices managed by human teams, these components would be maintained by AI systems operating within architectural guidelines defined by human engineers. And eventually, we may even let the AI take that over too.

The development process shifts toward interface-first design, where human architects focus on defining component boundaries and interactions, while AI systems handle implementation details. This division of labor plays to respective strengths: humans excel at architectural thinking and business requirements, while AI systems optimize implementations and handle routine coding tasks. And as the AI gets better and better at architecture and business requirements development, we may see a whole new world emerge.

The Transition Path Forward

This transformation is happening gradually but accelerating quickly. Professional developers are becoming more effective through AI assistance while maintaining architectural oversight. Non-developers are building functional applications through conversational interfaces that would have been impossible for them to create previously.

Current service-oriented architectures provide a foundation that can evolve toward component models as AI capabilities mature. Organizations with good interface design practices, comprehensive testing strategies, and strong observability will be best positioned for this transition.

The engineers who thrive will be those who can think architecturally about system design while effectively directing AI systems. Product managers become even more critical because rapid prototyping capabilities make clear product vision, competitive intelligence, customer-centric approaches and market understanding the primary competitive differentiators.

The Strategic Reality

As we move toward this future, competition shifts in important ways. Technical barriers to building certain types of software continue falling, but success increasingly depends on architectural excellence and product strategy rather than implementation speed alone.

Organizations that can design effective component architectures and orchestrate AI development systems will gain significant advantages in both development velocity and system reliability. The ability to continuously optimize software systems without traditional refactoring risks could become a major competitive edge.

However, this also presents new challenges around managing dynamic system complexity, ensuring security across AI-generated code, and maintaining coherent user experiences across rapidly evolving implementations.

The transformation isn’t about replacing human engineers – it’s about creating new collaboration models between human architectural thinking and AI implementation capabilities. The future belongs to organizations that can effectively combine these strengths while maintaining clear product vision and strategic focus.

We’re witnessing a shift from static implementations toward dynamic, continuously optimizing systems. While full realization is still years away, the foundation is being built through current experiments with AI-assisted development, vibe coding platforms, and component-based architectures. Replit’s growth numbers suggest this isn’t theoretical anymore – it’s happening faster than most of us expected, and the organizations preparing now will be best positioned to capitalize on the opportunities it creates.

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Sep 17 2024
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Ad Technology, Artificial Intelligence, Marketers, Media Agencies, Online Media, Publishers

How AI is actively transforming advertising

by Eric Picard

Artificial Intelligence (AI) and its subset, Machine Learning (ML), have been integral to the advertising industry for decades. These technologies have transformed how businesses connect with consumers, optimizing many of the dollars spent and ensuring targeted engagement. But as AI evolves, particularly with the advent of Large Language Models (LLMs) and generative AI, we’re entering a new era where smart automation is a reality. Let’s explore how AI, ML, and LLMs are reshaping advertising, untangling the complexities of these technologies, and understanding their distinct roles and applications.

To begin with, it’s important to differentiate AI and ML. AI is the broader concept of machines performing tasks that typically require human intelligence, such as decision-making and pattern recognition.

Machine Learning (ML) is a subset of AI, and has been the engine that powers many of the smart decisions in today’s advertising landscape. At its core, ML is about teaching computers to learn from data, much like how we learn from experience. There are several approaches to ML, each with their own unique applications and strengths in advertising.

One of the most prevalent techniques is supervised learning. This approach involves training an ML model on a dataset that includes both inputs and the correct outputs—sort of like coaching a sports team with the playbook in hand. In advertising, supervised learning is often used for predictive targeting. By analyzing historical data, these models can forecast which segments of an audience are most likely to respond to a specific ad campaign. This allows advertisers to allocate resources more effectively and maximize return on investment.

Unsupervised learning, on the other hand, is a bit like sending a detective into a room full of clues without any instructions. The model explores the data, finding patterns and relationships on its own. This technique is ideal for audience segmentation, helping advertisers discover new and potentially valuable consumer groups based on shared behaviors or characteristics. It’s akin to discovering hidden subcultures within a larger community, providing insights that can drive more personalized marketing strategies.

Reinforcement learning is another fascinating ML approach, where models learn by trial and error—similar to training a pet with rewards and consequences. In the dynamic world of real-time bidding, reinforcement learning algorithms adjust bidding strategies on the fly, learning which actions yield the best results. This adaptability is crucial in environments where market conditions and consumer behavior can change rapidly.

It’s also worth mentioning neural networks, a type of ML model inspired by the human brain’s structure and function. These networks are particularly powerful in tasks involving complex pattern recognition, such as image and speech recognition. In advertising, neural networks can enhance programmatic buying by evaluating vast datasets to identify subtle patterns in consumer behavior, enabling more precise targeting and personalization.

While these examples illustrate some of the common ML techniques used in advertising, the field is vast and continually evolving. Each method brings its own set of tools to the table, contributing to a more nuanced and sophisticated advertising ecosystem. As ML technology advances, its role in crafting more targeted, efficient, and engaging advertising experiences will only grow, pushing the boundaries of what’s possible in the digital marketing space.

Now, let’s address the role of LLMs in advertising. LLMs, such as GPT-4o, are advanced AI models that excel in understanding and generating human-like text. They are not primarily designed for data analysis or real-time decision-making—which are traditional ML use cases—but rather excel in tasks that involve language processing and text-based interactions. LLMs are being leveraged to automate processes that require a nuanced understanding of language and context, such as drafting personalized ad copy, facilitating customer service interactions, and enhancing chatbots.

In media buying and selling, LLMs are being applied to automate complex processes that traditionally required human intervention. By programming LLMs to think with a specific viewpoint and set of instructions, they can streamline tasks like scheduling and orchestrating campaigns, crafting and refining ad messages, and even generating comprehensive reports. These models act more as strategic partners, assisting human teams in managing and executing processes efficiently.

The application of ML in advertising remains robust, focusing on data-driven decision-making. ML algorithms excel in predictive targeting, analyzing vast datasets to identify optimal audiences, and optimizing bids in real-time to maximize ad spend efficiency. These capabilities are essential for real-time bidding environments and dynamic pricing models, where decisions must be made swiftly and accurately based on ever-changing data inputs.

Generative AI, closely related to LLMs but with distinct applications, is making significant impacts in creative advertising. While LLMs are adept at processing language, generative AI models are designed to create new content—be it text, images, or even video. In advertising, generative AI can automate the creation of ad visuals or video content, generating variations tailored to different audience segments. This capability not only enhances creativity but also accelerates the production process, allowing for rapid experimentation and iteration.

The distinction between generative AI and LLMs is important. While both can be used in the creative process, LLMs focus on language and dialogue, whereas generative AI extends to producing varied forms of media content. Together, they offer a comprehensive toolkit for advertisers looking to innovate and engage audiences more effectively.

As AI continues to advance, ethical considerations around data privacy and algorithmic bias abound. Transparency in how AI systems operate and make decisions is essential to maintain consumer trust. Balancing automation with human creativity and insight is also crucial. While AI can handle data-driven processes, human expertise is irreplaceable in crafting compelling narratives and understanding the subtleties of consumer emotions.

Looking ahead, the fusion of AI technologies promises even more sophisticated advertising solutions. We can anticipate AI models that predict consumer needs with remarkable precision, integrating seamlessly into the consumer journey. This future of advertising is not just about efficiency—it’s about creating meaningful, anticipatory experiences that resonate with consumers on a personal level.

By automating routine tasks and augmenting human capabilities, AI is enabling advertisers to deliver more personalized, effective, and efficient campaigns. The key to success will be embracing AI as a partner, leveraging its strengths while preserving the creativity and empathy that only humans can provide.

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