What is the impact of AI across different levels of seniority?

Over the weekend, I read Sergey Tselovalnikov’s post on AI Impact Curves.

The software engineering curve reveals an intriguing pattern.

Junior engineers experience both benefits and risks from AI. Staff+ engineers also gain tremendous leverage at the senior end of the curve.

However, mid-level engineers see more modest impacts, primarily because they are proficient with their codebases and can write effective code adeptly.

Google’s earnings call identified some major changes and unexpected outcomes, including the performance of AI Ads, the importance of hardware compared to algorithmic efficiency gains, and the groundswell of developer adoption for Gemini models.

AI algorithms have had a thousand times reduction in inference cost over the last three years. There is another cost reduction curve, which is the compute per watt of electricity. Google reveals that this is decreasing at half of the rate of algorithmic efficiency. But it can be just as meaningful.

Web3 is putting up real revenue numbers. Over the last 30 days, the top 20 public Web3 projects generated $1.2B in revenue. This isn’t some theoretical valuation metric. This is hard revenue, derived from trading and other financial fees. And it tells a compelling story.

A power law is clearly at play. While we might expect this in a nascent market, the sheer scale is impressive. Even the smallest project on this top 20 list is running at a $75M annual run rate. That’s not chump change. It underscores the return potential of this space.

AI startups represent about 70% of B2B Series As, up from about 40% in early 2024. On average, AI Series As raise at 40% higher valuation than non-AI companies, a multiple that has been increasing over time.

But this is less of a premium than in the public markets. As of January 31, an AI publicly traded software company trades at twice the multiple of a non-AI software company.

Microsoft announced earnings yesterday & the data painted a brilliant picture for the future of AI.

Greater than 30% annual growth in back-to-back quarters is sensational for a $100b run rate business. Microsoft is projecting similar for next quarter. The AI subset is on a $13b run rate, more than double last year.

Azure other cloud services revenue grew 31%. Azure growth included 13 points from AI services, which grew 157% year-over-year, and was ahead of expectations even as demand continued to be higher than our available capacity… In Azure, we expect Q3 revenue growth to be between 31% and 32% in constant currency driven by strong demand for our portfolio of services.

Darwin’s finches sprinted to evolve when food sources changed. For decades, nothing—then sudden transformation. AI follows the same pattern & it’s accelerating.

Every morning, I wake up wondering what breakthrough will propel the ecosystem forward.

Last week, it was DeepSeek v2. This morning, a Hugging Face researcher announced that he could induce reasoning on the major advance of the last three months in one of the smallest models, a 3B parameter model.

A microwave that writes its own recipes. A smart watch that crafts personalized workout plans. A ticket kiosk that negotiates refunds in natural language. This isn’t science fiction - it’s 2025, & DeepSeek just made it far more affordable.

The Chinese AI company released two breakthroughs: V3, which slashes training costs by 90+%, & R1, which delivers top-tier performance at 1/40th the cost. But the real innovation? They proved that sometimes simpler is better.

There are two opposing forces in the world of data: an overall consolidation within the modern data stack & a massive expansion driven by AI capabilities. AI is rewriting every rule about what’s possible with data in 2025.

Here are Theory’s Top Themes in Data in 2025 with the full presentation at the bottom.

1. The Great Consolidation. After a decade of expanding complexity in the modern data stack, companies are looking to dramatically simplify their architectures to drive better results. Buyers we speak to say, “Do not sell me another tool.”

Slide 1

Clearing: While data world consolidates, capabilities have exploded with AI.

Content:

• AI is rewriting every rule about what’s possible with data
• Those two forces in tension will make for an exciting 2025

Slide 2

Clearing: My name is Tomasz Tunguz, founder and general partner at Theory.

Content:

• I’ve been investing in data for the last 17 years and have worked with companies like Looker, Monte Carlo, Hex, Omni, Tobiko Data and Mother Duck
• I founded Theory, a venture firm managing $700M with the idea that all modern software companies will be underpinned by data and AI
• We run a research-oriented firm, formed by 200 buyers of data and AI software

Transition:

Over the weekend, a small Chinese hedge fund turned star AI research outfit launched DeepSeek R1, a new massive open-weights model with state-of-the-art performance, trained on a shoestring budget.

Just how much interest is there in this advance?

I analyzed R1 downloads on Ollama, and I recorded my steps to perform this analysis with AI using speech, an AI model, & a developer environment. See the video below if you’re curious how I did it.