One of the biggest implications of an AI-first enterprise will be that the AI stack an enterprise chooses will be a critical driver of any individual’s productivity in a company, which eventually compounds to determine the firm level productivity and thus competitiveness. This means that specific technology decisions that a company makes will determine the rate of software they can code, contracts they can review, leads that can be generated, campaigns that can be launched, breakthrough innovations that are discovered, customers that can be supported, and so on. The AI-first enterprise -and one that makes the right technology decisions- will see compounding returns and acceleration in all of these areas; conversely, the slower moving companies will tend to fall behind over time. As a result, AI will likely accelerate differences between firms based on their tech decisions, and even impact the kind of talent they can attract and retain, further hastening these competitive differences. We’ve actually seen this with software in the past, albeit at a smaller scale. For instance, for years employees at Facebook and Google benefited from a unique developer stack and tooling that’s long created efficiencies for shipping or scaling software that the rest of the world didn’t have. Fast growing startups notoriously leverage their lack of tech debt, and ability to implement a modern tech stack, as a means of moving faster than incumbents. Companies like Netflix leverage data to make better acquisition decisions. It all comes down to the tech stack. AI just takes this far further, because it will be a meaningful force multiplier on the execution of any employee. And with AI Agents, it’s almost akin to the quality of colleagues that you’re working with. Even subtle differences in the AI Agents you use for customer support, code writing, question answering, or workflow automation will lead to very different business results over time. And the need will only accelerate in the future as a new demographic enters the workforce. A younger workforce coming into the enterprise will have completely different technology habits than generations before, ultimately requiring an AI-first stack to be productive. We’re only in the earliest innings of fully understanding what an AI-first enterprise will look like, but it’s clear that work will likely look very different in a decade from now. And there’s a huge opportunity for those that are adapting early.

AI agents have the ability to fundamentally change the business model of enterprise software. Today, when you build a SaaS product, the primary business model is to sell seats that are tied to the end-users of your service. For "same store" sales, you can essentially grow at the rate of the headcount growth of that customer, or by adding additional capabilities on top of your offering. This business model is, of course fantastic, and has been around since the earliest days of enterprise software, and will keep going forever. But AI Agents unlock an entirely new business model in software, because you can now grow in a way that is uncorrelated to direct enterprise headcount or even external factors to your software. What's amazing about this model is that the AI vendor actually can directly grow itself within an enterprise account due to direct productivity gains.  For instance, imagine an AI Agent that can generate outbound sales demand for a startup. Traditionally, B2B companies will start by hiring a very small team of maybe one or two people, and increase resources in this area in a very incremental fashion -- essentially a process that looks like a guessing game where you try to predict how much demand there might be in the market, the ability to fund new resources, and the length of time it takes to hire great talent. In a world of AI Agents, you would simply set a budget and then decide how fast you want to grow. This is much closer to the business model of Google Adwords than it is of a traditional SaaS service. The same would be true for entirely different functions, where AI Agents can develop software at the rate of how many software projects you want built or how many legal documents you want reviewed -- all unconstrained by a company's headcount. This will even happen in areas where you wouldn't initially expect demand levels to change just because you move to AI, but in fact where there's plenty of latent demand. For instance, in front-line support, in a world where AI Agents can answer a greater set of questions a customer might have, you could see the kind of interaction volumes going up far higher in a world of AI.  This all has major implications to the economics of software, and it means that the AI Agent business model goes after a far greater pool of spend than just traditional IT. If software products now directly *drive* company productivity instead of just *enabling* company productivity, this could lead to a step-function change in the size of IT markets over time. Wild.

One of the most common concerns about AI is the risk that it takes a meaningful portion of jobs that humans currently do, leading to major economic dislocation. Often these headlines come out of economic studies that look at various job functions and estimate the impact that AI could have on these roles, and then extrapolates the resulting labor impact. What these reports generally get wrong is the analysis is done in a vacuum, explicitly ignoring the decisions that companies actually make when presented with productivity gains introduced by a new technology -- especially given the competitive nature of most industries. The thinking generally goes that if a company could, say, be 50% more productive in a particular function, it would mean a commensurate reduction of jobs in that area. For instance, if a certain function (like engineering or sales) required 10 units of labor before, then with a 50% gain in productivity, in the future that same function would now only need ~7 units of labor. The challenge with this type of thinking is that it assumes that companies have maximized the amount of labor they wish they had for a particular function, when in reality many functions are only staffed at the level the company can afford. Further, it assumes that a company is not in a competitive field, and that the company would be complacent and happy about generating the same output as before, just with less costs. Finally, it ignores the fact that productivity gains in a market will lead to increased response from competition, which companies equally have to respond to with more productivity not necessarily more profit. Time and time again this is the type of flawed thinking that we tend to get out of broad economic studies on the labor needs in the economy. To break this down and make it practical, I thought I'd illustrate the point with the example of an engineering function -- one that already is seeing the benefits of AI starting to roll out. The numbers will all be kept simple, but you can change almost any variable and the point will remain the same. The key to thinking through job impacts is to think through what happens a step or two *after* the productivity gain of AI is experienced. So, imagine you're a software company that can afford to employee 10 engineers based on your current revenue. By default, those 10 engineers produce a certain amount of output of product that you then sell to customers. If you're like almost any company on the planet, the list of things your customers want from your product far exceeds your ability to deliver those features any time soon with those 10 engineers. But the challenge, again, is that you can only afford those 10 engineers at today's revenue level. So, you decide to implement AI, and the absolute best case scenario happens: each engineer becomes magically 50% more productive. Overnight, you now have the equivalent of 15 engineers working in your company, for the previous cost of 10. Finally, you can now build the next set of things on your product roadmap that your customers have been asking for. We can't assume it will be 50% more because there are new points of friction and coordination tax that emerge as you have 15 equivalent engineers, but let's say your output goes up meaningfully. Assuming you're acting in your best interests as a company, the features you build make your product that much more compelling, which means at some point (sooner or later) they should result in an incremental gain in revenue. Let's be somewhat conservative on what impact these new features will have on your product, but let's say they generate an incremental 10% of revenue over time or keep customers retained at a 10% greater rate (roughly the same financial benefit). Now let's assess the downstream impact. Firstly, any growth of revenue will often lead to some functions in the business growing as well to support these new customers, which will directly create new jobs. But further, the company now has to decide whether it remains satisfied with its 10 engineers that have the output of 15, or with their incremental revenue should they hire even more engineers to build the *next* set of features that will make them even more compelling to customers. Unless this company is in some rare monopoly position, they likely will want to build the next set of features even faster than the last set to grow even more quickly. This then means AI has caused the company --counterintuitively-- to hire more engineers than before, because the productivity of each engineer is much higher, allowing them to generate more return per engineer, and thus more revenue. What's interesting is this analogy works similarly for most functions in a business. In sales, if you could make sales reps 10% more productive (i.e. they sell 10% more of your products/services for the same cost), almost every company in the world would prefer to hire even more sales reps, instead of merely banking the incremental profit. That incremental sales productivity again would lead to downstream implications, like the need to deliver more features to customers, and thus more R&D hiring! Even back-office functions that don't as directly tie to revenue growth, often are a bottleneck to growth . If you can reduce the bottleneck -- say lawyers reviewing contracts, or people processing invoices-- cycle time in businesses accelerates, which almost always lets you serve more customers faster or grow more quickly, again letting a company reinvest those dollars. In the end, when you step out of the vacuum of just the specific productivity gain of a particular job function, and look at how the whole system will adapt and improve due to that productivity gain, a very different picture of AI's impact on jobs will emerge. Yes there will absolutely be changes to what jobs become more or less in demand in the future, but the competitive nature of companies inevitably ensures that across the whole system companies will be focused on leveraging AI to become more productive.