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Algorithms needed for unpredictable journey. Significant compute costs, endless data processing, long periods of unexplained failures. Safe convergence doubtful. Honor & recognition in event of success.

What Shackleton might have written in 2025.

AI isn’t perfect but its productivity gains are undeniable. ServiceNow, Microsoft, and Amazon, plus nimble startups generating tens of millions in ARR with teams small enough to feed with a few pizzas all have benefitted.

The new boast of 2025? ARR per employee measured in the millions up 5x or more from the previous generation of software companies.¹ Fueled by AI.

A decade ago, most startup pitches ended with a calculation justifying the amount they sought to raise.

In other words, the raise was an output of the financial model.

But for the most sought after companies, the raise amount is disjointed from the capital needs of the business - instead it’s driven by the fundraising auction.

Great fundraisers are the teams that build the most auction pressure.

This auction dynamic, combined with venture capital’s explosive growth over the last decade, has transformed fundraising strategy. The goal is no longer to raise what you need, but to optimize for the lowest cost of capital.

October 2024 marked a critical inflection point in AI development. Hidden in the performance data, a subtle elbow emerged - a mathematical harbinger that would prove prophetic.

What began as a minor statistical anomaly has since exploded into exponential growth.

Since then AI performance has surged attaining a new trajectory, a new slope - no longer linear but geometric.

Theory’s name isn’t just a name - it’s our ethos. We develop & test theories about the future of technology, business, & venture capital through constant experimentation.

We’re seeking an architect of the future, someone who thrives at the intersection of AI & process design.

Ideally, this person :

• enjoys architecting processes and implementing them with a team
• is relentlessly curious about new technologies & techniques
• has a good understanding or desire to learn about great sales teams
• thrives in a warm, collaborative, & urgent culture

Apply here if you’re interested.

For pre-seed to Series B founders, navigating GTM strategy, marketing, and positioning can be challenging. When should you hire your first marketing leader? How do you scale GTM efficiently? When is the right time to invest in branding?

To help founders tackle these challenges, Theory Ventures is hosting exclusive 1:1 Office Hours with Kady Srinivasan, former CMO at Lightspeed Commerce and GTM leader at Dropbox and Klaviyo. Kady has led GTM teams that scaled revenue from $150M to $600M ARR, drove successful IPOs, and optimized marketing efficiency at scale.

Cloudflare’s earnings last week revealed something more significant than just company optimism: a fundamental shift in software buyer confidence.

“However, as the quarter progressed, we saw encouraging signs that confidence is beginning to return, particularly in the U.S. Security, AI, modernization and efficiency form the word cloud we hear most often in these conversations. These themes play directly to Cloudflare’s strength.”

Sales cycles are accelerating.

“Beyond the qualitative, we saw measurable improvements in Q4. We saw a notable uptick in close rates. We saw an improvement in sales cycles. The majority of large customers I mentioned last quarter, those deals had slipped from Q3, reengaged and signed significant contracts in Q4. We crossed 3 million active developers on our platform, including Cloudflare Workers and Workers AI.”

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.