AI will be a mass extinction event, but not in the ways that Twitter would have you believe
In corporate archeology, AI will be a mass extinction event, but not in the ways that Twitter would have you believe. Sure, some companies will die from not taking AI seriously enough: some will be overtaken by nimbler AI-powered competitors, some will implode from cybersecurity breaches, others will lose their best talent to self-employment. But most companies that die in the next five years will die by suicide, not by murder.
Occupational Cosplay. AI empowers us to roles that we are not familiar with, and creates the impression of efficacy. Using AI, the CEO can ship code, the coder can write marketing copy, and the marketer can write an investor email. In each case, the employee is using AI in a domain where they can’t evaluate the quality of what they’re producing, and as a result, assume they’re now capable of a job that they never really understood. To crystalize this, think about a metric: the percent of human time spent making decisions in one’s areas of expertise. In well run organizations, this number is high. AI tanks this metric because it allows employees to jump into jobs they don’t understand. Organizational disfunction and error will result.
Productivity Dysmorphia. Healthy organizations have a strong understanding for how all activities tie back to business outcomes. When an organization goes all-in on AI, every incentive pulls employees toward building the project that will demo well, toward blind automations, toward the huge first draft, and the visible signs of AI progress, mistaking use of AI for concrete business value. Employees, management chains, corporations, and markets are all buying into a distorted sense for what building value looks like, substituting the excitement for deployment of AI for genuine progress toward business objectives. Consider three facts that are benign individually, but braid into something lethal: AI is fun to play around with, management is all-in on AI, and AI makes many non-productive tasks feel productive.
Drowning in Content. Corporate information systems are delicately balanced ecosystems, and AI is an invasive species, replicating at a pace that will quickly out-compete both human-generated content, and any system of meaningful informational consumption. How’s your inbox/slack looked recently? Mine is noisy as hell - filled with AI content - sometimes slop, sometimes insightful and useful. Even when AI outputs are valuable (which isn’t always the case), finite constraints on attention, execution, and resources are hard constraints that don’t have any easy answers. AI to sort and filter to the important messages? AI to route to other AIs for further summary? AI to react to and take action based on AI analysis? People will try everything, but I suspect all such attempts will treat the symptom of noise rather than it’s cause - which is a lack of focus and vision. Teams that turn a blind eye to these hard questions will eventually see the their cattywampus AI infrastructure collapse under the weight of its cost and impotence.
Token Cliff. Today, frontier labs are heavily subsidizing token costs, as are the AI-powered venture backed startups running on their infrastructure. When those businesses shift toward making a profit, the cost of AI powered tooling is going to go up, probably a lot, and probably all at the same time. That shift will kill companies that baked unsustainable compute into their business models. For those that survive that cliff, there’s a famous saying about ad spend: half is wasted, but it’s impossible to know which half. Token costs will be like advertising expenses - generating real value, but spread out thinly over tons of waste, impossible to optimize because of the structure of value produced. Companies are rushing to ramp up a huge cost center without any tools for estimating the value of their expenditures.
Talent Disruptions. AI is throwing a wrench in talent pipelines. Experienced employees working with AI can do far more than they could before - competition for senior talent will shorten tenure in ways that incentivize short-termism. On the other side of the pipeline, junior employees leaning on AI can appear more competent than before, but seem to be failing to develop the depth of understanding that inculcates the judgement needed to step into more senior roles. Many companies will die simply because their employees learn less, and don’t stick around as long.
Distraction from Competency. AI allows companies to build far outside of their core competencies, and many companies will die because they decided to build infrastructure that they should have just paid someone else to build and maintain. Every software system looks like it’s just a database and a frontend until you get to know the challenges of its implementation, at which point you’ve paid large prices in time and tokens. Many companies will vibecode core pillars of their business, and then be typing into chat GPT “what is a race condition” when their shipment doesn’t show up, or “please implement withholding for all states make no mistakes” after the IRS sends them a letter. Successful companies have a strong and narrow thesis of how they generate value. Many companies will perish because they tried to solve problems that they were fundamentally not equipped to solve, but for which AI gave them a plausible first draft that gave them naive confidence.
Underinvesting in People. The final way that companies will fail is that they’ll forget about people. They’ll under-invest in their employees - treating them like input/output cost centers. They’ll neglect their customers, using bots to mediate and summarize all conversations - keeping them at arms length. They’ll scorn craft and judgement, blindly asserting that AIs can replicate things outside of their understanding. They’ll pour money into tokens and software, and divert resources and attention and time away from people, and then one day they will wake up and realize that the people are the only piece of this that ever actually mattered.
I’ll close with an insight I like from someone I don’t. Besos advises that companies should build their long-term business strategy not based on what is changing, but on what will stay the same - Amazon wasn’t built based on assumptions of how the internet would evolve, but on the recognition that consumers will always want low prices, large variety, and quick delivery. AI is the quintescence of flux - nobody knows where the technology will be in ten years, and projecting forward is speculation, not forecasting. But I’d bet even after ten years of AI disruption, expertise will still be valuable, companies will still need to focus, and the economy will still serve on the needs and wants and preferences of people, not machines.