A16z's latest in-depth analysis of the AI market: Is your company still "bleeding" to work?

Author: DeepThink Circle

Have you ever wondered if the software industry might be experiencing a transformation even more dramatic than the shift from command-line interfaces to graphical user interfaces? Recently, I listened to a deep analysis of the AI market shared by David George from a16z, and I was struck by a set of data: the fastest-growing AI companies are expanding at an annual growth rate of 693%, while their spending on sales and marketing is far lower than that of traditional software companies. This isn’t an isolated case; the growth rate of the entire AI company group is more than 2.5 times that of non-AI companies. Even more astonishing, these companies’ ARR per FTE (annual recurring revenue per full-time employee) reaches $500,000 to $1 million, compared to the previous generation of software companies’ standard of $400,000.

What does this mean? It signifies that we are witnessing the birth of a completely new business model—an era of creating greater value with fewer people and lower costs.

David George mentioned in his presentation that this isn’t a minor adjustment but a complete paradigm shift. Core concepts—version control, templates, documentation, and even the very idea of users—are being redefined due to AI agent-driven workflows. I firmly believe that within the next five years, companies unable to adapt to this transformation will be completely eliminated.

The Astonishing Truth About AI Company Growth

The data David George shared made me rethink what true growth really means. 2025 is shaping up to be a year of accelerated growth for AI companies. After experiencing a slowdown in 2022, 2023, and 2024 due to rising interest rates and contraction in the tech sector, 2025 has completely reversed this trend. The most shocking part is that among companies ranked in different tiers, the truly exceptional outliers are growing at unbelievable speeds.

My first reaction when seeing this data was: are these numbers correct? The top-performing AI companies are growing 693% year-over-year. David said his team triple-checked this figure before believing it. But it aligns perfectly with what they’ve seen in their portfolio companies and case studies. This isn’t an isolated phenomenon; it’s a systemic change happening across the entire AI field.

More importantly, it’s about the quality of growth. Traditional software companies often take years to reach $100 million in annual revenue, but the fastest-growing AI companies are hitting this milestone much faster. David emphasized a crucial point: this isn’t because they’re spending more on sales and marketing—in fact, the fastest-growing AI companies spend less on these areas than traditional SaaS companies. They grow faster while spending less. What’s behind this? It’s because end customers have an extremely strong demand, and the products themselves are highly attractive.

This reveals a profound shift in business logic. In the past software era, growth depended heavily on large sales teams and massive marketing budgets—educating the market, persuading customers, overcoming adoption barriers. But in the AI era, truly excellent products can speak for themselves. When a product immediately creates value for users—making them feel a boost in efficiency from the first use—market demand will naturally follow. This product-driven growth model is much healthier and more sustainable than traditional sales-driven approaches.

Another interesting set of data David shared is that AI companies’ gross margins are actually slightly lower than those of traditional software companies. Their team’s unique perspective is that, for AI companies, a lower gross margin can be somewhat a badge of honor. If low margins are caused by high inference costs, it indicates two things: first, people are actually using AI features; second, over time, these inference costs will decrease. So, if an AI company’s gross margin is unusually high, it might raise suspicion, as it could mean AI features aren’t truly being purchased or used by customers.

Why Can AI Companies Be More Efficient?

I’ve been pondering: why can AI companies generate more revenue with fewer people compared to other software firms? David focused on the ARR per FTE metric—annual recurring revenue per full-time employee—which measures overall operational efficiency, encompassing sales, marketing, management, and R&D costs.

The best AI companies achieve ARR per FTE of $500,000 to $1 million, compared to about $400,000 for the previous generation of software companies. This might seem like a small numerical difference, but it reflects a fundamentally different business model and operational approach. David believes this difference mainly stems from the strong market demand for these products, allowing them to reach market with fewer resources.

But I think there’s a deeper reason. From the start, AI companies have been forced to think differently about how they operate. They had no choice but to use AI to redesign internal processes, product development, and customer support systems. This forced innovation has led them to discover a more efficient business model.

David shared a vivid example: he recently spoke with a founder who was dissatisfied with their product’s progress. The founder directly assigned two engineers with deep AI expertise to rebuild the product from scratch using the latest tools like Claude Code and Cursor, with an unlimited programming budget. The result? The founder said progress was 10 to 20 times faster than before. And the bills for these tools were so high that he started reconsidering the entire organizational structure.

This example impressed me because it’s not incremental improvement but a leap in scale. What does a 10- to 20-fold speed increase mean? Projects that once took a year could now be completed in one or two months. This speed difference can be decisive in competition. The founder concluded: I need to have my entire product and engineering team work this way, and I believe this will happen within the next 12 months. But it also means a fundamental redefinition of team organization—where do product, engineering, and design boundaries lie? These questions need to be rethought.

I believe December 2024 will mark a turning point in programming. David shares this feeling too. He says it feels like programming tools have made a qualitative leap at that time. Over the next 12 months, this change will either take root in companies or those that don’t adopt it will fall far behind. This isn’t alarmism; it’s reality.

Adapting to AI or Being Eliminated

David pointed out a very stark reality: for companies founded before the AI era, they must either adapt or die. This sounds extreme, but I fully agree. And this adaptation must happen on two levels: front-end and back-end.

On the front end, companies need to think about how to integrate AI natively into their products—not just add a chatbot to existing workflows. They need to reimagine what their products can do with AI and be aggressive in disrupting themselves and making changes. David shared some interesting examples: one pre-AI software company’s CEO has been completely converted to the AI mindset, saying: We want to become an AI product. We want our product to say, your employees are now your AI agents. These are the topics he’s discussing now.

Another more extreme example: a CEO said that for every task they need to complete, he asks himself: can I do this with electricity, or must I do it with blood? This is an extreme mindset shift—using electricity refers to AI and automation, blood to human labor. This kind of thinking is a profound transformation, forcing a re-examination of every process and task within the company.

On the back end, companies need to fully adopt the latest programming models and tools. All developers should use the newest coding assistants, and every department should leverage the latest tools. So far, programming adoption has been the highest, and this is where the biggest leap has been seen. But this change is spreading to other functions.

David mentioned that for pre-AI companies, the good news is that the evolution of business models is still in early stages. The most disruptive scenario is when technology and product shift simultaneously with a change in business models. Currently, technology and products are undergoing dramatic change, but business model shifts are not yet fully underway.

He views business models as a spectrum. On the far left are license models—pre-SaaS license and maintenance models. Then come SaaS and subscription models, often based on seat licensing, which was a major innovation and highly disruptive—think of what Adobe experienced during this transition. Next is consumption-based models—paying based on usage, common in cloud services, where many task-based businesses have shifted from seat-based to usage-based billing.

The next stage will be outcome-based models. When you complete a task successfully, you get paid based on the outcome. Currently, the only fields where this is truly feasible are customer support and customer success, because you can objectively measure problem resolution. But as model capabilities improve, if other functions can also be measured by results, it will be a huge disruption for existing companies.

I find this evolution path very insightful: from licenses to subscriptions, then to consumption, and finally to outcomes. Each step disrupts the previous business model. We are now on the eve of shifting from consumption to outcomes. Once AI agents can reliably complete tasks and be objectively evaluated, outcome-based pricing will become mainstream. At that point, companies still charging by seat will find themselves completely uncompetitive.

The Dilemma of Large Companies’ AI Adoption

Regarding Fortune 500 companies adopting AI, David’s observations are very interesting. He says there’s a huge gap between what these CEOs say and what’s actually happening. They all say: we must adapt, we’re eager to understand what AI tools are needed, we’re ready to change, our business will roll out these tools, and we want to become AI companies.

But the reality is quite different. The biggest disconnect lies in change management: it’s extremely difficult. Even just getting people to use AI assistants to help them do their jobs better is hard enough. Managing actual business change, reengineering workflows, and leading transformation are even more challenging.

David isn’t surprised by rumors that progress is slower than expected. But for the most capable companies that fully embrace AI and know what to do, the impact has already been significant. He cited examples: Chime reduced support costs by 60%; Rocket Mortgage saved 1.1 million hours in underwriting, a sixfold increase in efficiency, saving $40 million annually.

This reveals a key issue: the gap between willingness and capability. CEOs of large companies want to adopt AI, but whether they have the ability to implement it is another matter. Change management is often underestimated. It’s not just about buying tools or hiring AI engineers; it requires fundamentally changing processes, culture, and organizational structure.

Many large companies also need to prepare their business for AI. Using chatbots is one thing; the productivity gains may be limited. But if you need to completely overhaul your systems, data, and backend to accommodate AI, much work is still in the pipeline, with no immediate results.

David predicts that the next 12 months will be very interesting. He believes we will see more cases, some companies will succeed, others will struggle. Those that succeed will gain enormous productivity advantages, while those that don’t will fall far behind. I think this divergence will happen faster and more intensely than most expect.

Model Busters and the Future of Markets

David mentioned a concept I find particularly insightful: Model Busters. These are companies whose growth rate and duration far exceed what anyone could predict in any scenario. The iPhone is a classic example. If you look at the consensus forecasts before its launch and compare them to the actual performance 4-5 years later, the consensus was off by a factor of three. And this was the most scrutinized company globally.

David believes AI will be the biggest Model Buster he’s seen in his career. Many AI companies will outperform expectations by a wide margin. I strongly agree. When a technological platform delivers not incremental improvements but exponential leaps, traditional forecasting models break down.

He notes that technology itself is a kind of Model Buster. Since 2010, technology has provided high-margin revenue at unprecedented speed and scale. Early on, it seemed expensive, but repeated outperformance created value far exceeding the capital invested. There’s no reason to believe this time will be different.

In terms of capital expenditure, David shared that compared to the internet bubble era, current capital spending is actually supported by cash flow, and the percentage of revenue spent on capital is much lower. The largest capital outlays are by hyperscalers—some of the most successful businesses ever.

He emphasized that as a portfolio company, they welcome this kind of capital expenditure. Building capacity as much as possible for training and inference is very positive. Most of the burden falls on the most successful companies.

They’ve also started to notice that debt has entered this equation. It’s impossible to fund all future capital expenditures solely with cash flow, so markets are beginning to see some debt. Overall, they’re comfortable with companies that finance with cash flow, continue generating cash, and use debt—especially if the counterparties are Meta, Microsoft, AWS, Nvidia.

He pointed out a noteworthy case: Oracle. Oracle has been highly profitable and buyback-focused, but they’ve committed to very large capital expenditures—essentially a gamble. They will run negative cash flow for many years. The market has started to notice: Oracle’s CDS spreads have risen to about 2% over the past three months. This is a warning sign.

I believe this capital-intensive phase is necessary but not without risks. The key is ensuring these investments ultimately generate returns. Currently, demand far exceeds supply. All hyperscalers report demand outstripping capacity. Gavin Baker, whom David interviewed, used a good analogy: during the internet era, laying fiber optic cables resulted in a lot of unused dark fiber. But in the AI era, there’s no such thing as dark GPU. If you install GPUs in data centers, they are immediately utilized.

Stunning Revenue Growth

One set of data David shared was particularly shocking. He compared cloud services, publicly traded software companies, and the net new revenue in 2025. Public software companies will add about $46 billion in revenue in 2025. If you only look at OpenAI and Anthropic, their combined revenue from operations is nearly half of that figure.

Moreover, David believes that by 2026, the entire public software industry—including SAP and legacy software firms—will see AI companies (model companies) adding 75% to 80% of new revenue. This pace is simply incredible. It means that within a few years, AI companies will generate more new value than the entire traditional software industry.

Goldman Sachs estimates that AI infrastructure will generate $9 trillion in revenue. Assuming a 20% profit margin and a 22x P/E ratio, this translates into a $35 trillion new market cap. Currently, about $24 trillion of market value is already priced in. While we can debate whether this is all attributable to AI or the performance of big tech, there’s still enormous room for growth—if these assumptions hold, the upside is substantial.

David also did a simple calculation: by 2030, the total capital expenditure of hyperscalers will be just under $5 trillion. To achieve a 10% return on this $4.8 trillion to $5 trillion investment, AI annual revenue would need to reach about $1 trillion by 2030. To put that in context, $1 trillion is roughly 1% of global GDP, which would generate a 10% return.

Is this achievable? Possibly, but it might fall short. David believes looking only at 2030 is limiting. Returns on these investments could be realized over a longer horizon, say between 2030 and 2040. And considering that current AI revenue is roughly $50 billion—mostly accumulated over the past year and a half—the path from $500 million to $1 trillion isn’t unimaginable.

My Reflections on the Future

After listening to David’s insights, my biggest takeaway is that we are at the beginning of a historic turning point—not mid or late stage. This could be a product cycle lasting 10 to 15 years, and we’ve only just started. This excites me but also makes me anxious.

The excitement comes from the enormous opportunities this shift presents. Companies that adapt quickly and fully embrace AI will not only gain a competitive edge but could also define the next era. We’ll see new unicorns emerge, new business models develop, and organizational structures transform.

The anxiety stems from the fact that the pace of change may be faster than most expect. David pointed out a striking statistic: the average tenure of companies in the S&P 500 within the index has fallen by 40% over the past 50 years. This indicates that disruption is accelerating. In the AI era, this speed could increase even further.

I believe there will be a clear divide: some companies will truly understand AI’s potential and fundamentally rethink their products, processes, and organization. These will achieve exponential efficiency gains and competitive advantages. Others, even if willing, will struggle due to change management challenges, organizational inertia, and technical debt. This divergence will become more pronounced over the next few years.

For entrepreneurs, now might be the best time. Market demand is extremely strong, technological capabilities are advancing rapidly, and capital markets are still willing to support promising companies. Compared to the previous software wave, it’s now easier to reach similar scale with fewer resources and faster—lowering entry barriers but raising the bar for product quality and market fit.

For investors, the key is identifying true Model Busters—companies whose growth rate and duration far surpass traditional predictions. This requires vision and patience, trusting in seemingly unreasonable growth trajectories.

For practitioners—engineers, product managers, designers, and others—rapid learning and adaptation to new tools and workflows are essential. The example David shared—two engineers using the latest programming tools to work 10 to 20 times faster—is not an isolated case but a trend. Mastering these new tools and methods will give individuals a significant career advantage.

Finally, I want to emphasize that this transformation isn’t just about technology; it’s a mindset shift. Moving from “how should we do it” to “what results do we want to achieve,” from “adding more people” to “solving problems with AI,” from “following established processes” to “reimagining possibilities.” The “electricity or blood” question, though extreme, captures the essence of this shift.

We are witnessing the rewriting of the software world. This isn’t a gradual upgrade but a fundamental rebuild. Those who understand and embrace this change will define the next era.