If software ate the world, AI’s digesting It

What investors and founders should watch for in 2025

They say lightning never strikes twice, but technology has proven this wrong time and again. The 90s saw enterprises transform through ERPs. The late 1990s ushered in the SaaS revolution, with Salesforce's founding in 1999 fundamentally changing how businesses consume software. Cloud computing took off in the late 2000s with AWS's launch in 2006, letting startups compete with giants overnight. The mid-2010s saw RPA emerge as a major force, with companies like UIPath and Blue Prism, rewriting the rules of operational efficiency.

As a technologist in Silicon Valley, who's lived through multiple tech cycles, I can tell you: this one's different. We're not just seeing incremental improvements but a fundamental reshaping of how software is built, deployed, and monetized. Let me break down what I'm seeing on the ground and what I believe 2025 has in store for the AI ecosystem.

Source: Gartner

Developer Explosion and the New Engineering Stack

80% of the engineering workforce will need to upskill due to the rise of GenAI through 2027. The way we build software is getting flipped on its head. When your fundamental building blocks are AI models rather than code libraries, everything changes. This in essence has completely flipped the talent equation. Companies' AI capabilities now directly influence their ability to attract the best engineers.

I'm seeing three key trends:

1. Traditional software engineers are retooling at unprecedented rates

2. Surge in “micro-innovation” projects by non-technical employees

3. Small teams achieving what previously required entire departments

Source: ZeroToMastery

Verticalization: Narrow, but Deep

If 2024 was about building general-purpose AI, 2025 will be about specialization. Vertical AI has moved beyond workflow automation to solving humanity's core challenges. It’s about time that startups built on top of other LLMs realize that winners aren’t determined by who has the most powerful models, but by who builds the most defensible data and distribution advantages or a strong MOAT than just being an “automator”. This also means accounting for interoperability and sophisticated prompt engineering pipelines.

New and stronger themes are emerging. From diagnosing diseases in healthcare to deploying AI research assistants to climate tech innovations, AI is becoming a core driver of innovation across every specialized field it touches.

The Rise of RAG

Retrieval Augmented Generation (RAG) has evolved from a technical curiosity to the backbone of enterprise AI deployment. What's fascinating is how this is enabling companies to leverage their proprietary data while maintaining security and compliance.

Incumbents across industries like Siemens, IBM, Shopify are all leveraging this technology to cut down on manual processes, automating and reducing their turnaround time.

Look at Morgan Stanley's documented success - their wealth management RAG leverages OpenAI tech and their vast intellectual capital giving their 15,000+ financial advisors accurate information in seconds.

The competitive moat is in the data architecture. General AI chatbots are commoditized, but RAG systems trained on proprietary trading strategies or drug trial data? That's where the 10x value sits.

Intelligence at Enterprise Scale

Enterprises are getting smarter about compute utilization. Rather than throwing more GPUs at problems, companies are developing sophisticated inference optimization strategies.

Hybrid AI architectures are emerging that run lighter models locally for quick tasks while leveraging cloud resources for complex operations.

Even more exciting is the rise of agentic AI systems that are learning to autonomously adjust and optimize their operations. Companies that can tap into these intelligent, adaptive architectures are seeing massive gains in efficiency and cost savings.

Source: IOT Analytics

The Dawn of AI-Native Startups

A new breed of lean, AI-first companies is redefining startup economics. Companies are replacing whole departments, not just employees with AI copilots and agents.

The barriers to entry for creating sophisticated AI solutions are dropping fast, and the results are game-changing.

But this transformation won't be smooth. We're about to see the first major AI-related corporate crises. Boards scrambling to create AI governance roles and responsible AI policies in place.

But these challenges will create opportunities. The winners in 2025 won't be who most people expect. They'll be the companies that:

1. Build deep vertical expertise rather than chasing general AI capabilities

2. Focus on distribution and timing rather than just technical excellence

3. Understand that AI isn't a feature – it's a fundamental business restructuring

The most interesting part? We're still in the early innings. The real value creation hasn't even started yet.

I’m more than excited about what 2025 holds and if you’re building something groundbreaking in AI, drop me a note on LinkedIn, I would love to know more about your venture.