{"id":745,"date":"2026-07-24T09:01:18","date_gmt":"2026-07-24T09:01:18","guid":{"rendered":"https:\/\/www.vaultinsider.top\/?p=745"},"modified":"2026-07-24T09:01:18","modified_gmt":"2026-07-24T09:01:18","slug":"you-just-hired-a-million-bad-employees-how-tokenmaxxing-delivered-the-opposite-of-whats-promised","status":"publish","type":"post","link":"https:\/\/www.vaultinsider.top\/?p=745","title":{"rendered":"&#8216;You just hired a million bad employees&#8217;: How tokenmaxxing delivered the opposite of what&#8217;s promised"},"content":{"rendered":"<p><img decoding=\"async\" src=\"https:\/\/fortune.com\/img-assets\/wp-content\/uploads\/2026\/07\/GettyImages-1094918700-e1784835871921.jpg?w=2048\" \/><\/p>\n<p>George Sivulka, the 27-year-old Stanford grad who founded and runs the AI enterprise startup Hebbia, has put his finger on the defining anxiety of the AI agent era: companies raced to deploy AI workforces without building any of the management infrastructure to run them. <\/p>\n<div>\n<p class=\"wp-block-paragraph\">Hebbia already serves clients including BlackRock, KKR, and the U.S. Air Force, giving Sivulka a front-row seat to how badly this is going inside real enterprises. His essay, published on a16z\u2019s newsletter, argues that AI didn\u2019t cut labor costs\u2014it inverted the equation entirely: \u201cfor the first time in history, humans are cheaper than software.\u201d And for all those CEOs who rushed headfirst into the agent era briefly known as \u201ctokenmaxxing,\u201d he offered a warning: \u201cyou just hired a million bad employees.\u201d<\/p>\n<p class=\"wp-block-paragraph\">The era ended when Amazon famously disclosed a $500 million loss in one month alone as agents ran wild to little effect, while Ford Motor Company actually hired back a new force of human engineer \u201cgraybeards\u201d to work hand in hand with AI augmentation efforts. To Sivulka, the moment is like a mostly forgotten railroad crash in 1841 that ended one era, and began another.<\/p>\n<h2 class=\"wp-block-heading\">The railroad crash analogy<\/h2>\n<p class=\"wp-block-paragraph\">Sivulka reached back to the 1830s and 1840s, when American railroad track mileage exploded roughly 120-fold in a decade with no coordination systems to match the growth, until a fatal train collision in Massachusetts in 1841 forced the industry to invent modern management\u2014defined roles, reporting lines, hierarchies.<\/p>\n<p class=\"wp-block-paragraph\">That crisis, he argued, is what turned railroads into one of the first billion-dollar industries, \u201cat its peak representing roughly 60% of the stock market.\u201d Just as railroads unleashed the appetite to travel across the country, he argued that agents have done something similar to work on the web: \u201cWe just gave every employee, even the worst ones, effectively unlimited headcount and budget. Managing AI is harder than managing people, because AI scales dysfunction instantly.\u201d<\/p>\n<h2 class=\"wp-block-heading\">Why every company \u201chired a million bad employees\u201d<\/h2>\n<p class=\"wp-block-paragraph\">The essay\u2019s title captures Sivulka\u2019s core diagnosis: AI agents don\u2019t fail because the models are weak\u2014they fail because almost nobody in a company can articulate a task clearly enough for an agent to execute it well. He estimates just \u201c1 in 100 employees knows how to give AI context,\u201d calling that skill \u201ca rare breed\u201d of clear thinking that most workers simply don\u2019t have. The result is what he calls \u201clooping\u201d\u2014agents calling themselves over and over to self-correct for bad instructions, which he bluntly describes as \u201cspending tokens on spending tokens.\u201d<\/p>\n<p class=\"wp-block-paragraph\">UBS Global Research hosted \u201cmany of the highest-profile AI-native firms\u201d at its 5th Annual UBS Private AI and Software event in Menlo Park at nearly the same time, and nearly every executive privately confirmed Sivulka\u2019s thesis, of agentic failure happening at industrial scale. One firm executive told UBS\u2019 analysts: \u201cInternally, we don\u2019t have token budgets, it\u2019s not something that we, our engineers, have been trained to think about. But every single customer conversation is about this.\u201d<\/p>\n<h2 class=\"wp-block-heading\">The real cost wasn\u2019t the tokens<\/h2>\n<p class=\"wp-block-paragraph\">Sivulka argues the industry misdiagnosed its own hype cycle: \u201ctokenmaxxing\u201d spending exploded and then collapsed within a month, but \u201cthe amount of tokens spent was never the real problem\u201d \u2014 the problem was that people didn\u2019t know how to use them efficiently.<\/p>\n<p class=\"wp-block-paragraph\">UBS\u2019 sourcing puts real numbers behind that claim: one unnamed AI firm disclosed, \u201cour spend on Anthropic was $20k in December and we\u2019re about to cross $1m in July, a 50x increase in 7 months,\u201d adding that despite the surge, \u201cwe\u2019re not throttling back, we don\u2019t want people to stop using it.\u201d That firm is nonetheless installing what amounts to Sivulka\u2019s missing management layer after the fact: \u201cwe\u2019re now alerting if you hit a certain threshold on a monthly basis, we\u2019re going to start rolling out governors internally, like G&amp;A staff should not be using the frontier models.\u201d <\/p>\n<p class=\"wp-block-paragraph\">Public statements from OpenAI (\u201cAI costs have now become a huge issue that never came up at the start of the year\u201c) and enterprises like Uber installing spend guardrails confirm this isn\u2019t isolated\u2014UBS estimated last month that token-cost anxiety had become \u201ca real concern for ~60% of organizations,\u201d and \u201cthat figure now feels higher.\u201d Sivulka\u2019s point directly echoes Palantir CEO Alex Karp\u2019s public complaints that AI labs have \u201ccompletely, irresponsibly, oversold\u201d their models while enterprises burn money on token consumption without real ROI discipline. \u201cSomething has gone completely wrong,\u201d Karp told CNBC\u2019s <em>Squawk Box<\/em> earlier this month as he vented his spleen over misguided token usage. \u201cThe basic view among enterprises in this country is I\u2019m going to chillax and waste my time with tokens.\u201d<\/p>\n<p class=\"wp-block-paragraph\">Sivulka systematically reframes AI marketing claims by testing them against a workforce lens, and finds every one breaks down under scrutiny:<\/p>\n<p class=\"wp-block-paragraph\">He extends this to a broader indictment of bloated org charts, noting most companies are already \u201cmismanaged\u201d with workers who function as \u201ccogs in the machine\u201d\u2014and that Elon Musk\u2019s 80% staff cut at X performed better precisely because the cuts removed dead weight, which he says is mirrored by AI: \u201cJust like 80% of employees do nothing, 80% of tokens today do nothing.\u201d<\/p>\n<h2 class=\"wp-block-heading\">His fix: The \u201c100x token\u201d<\/h2>\n<p class=\"wp-block-paragraph\">Rather than concluding AI is broken, Sivulka argued that the solution is the same one railroads found in the 1840s: better management, not less technology. He predicted the defining skill of the next decade won\u2019t be engineering talent but context engineering: \u201cThe 10x engineer built the last era of companies. The 100x token will build the next.\u201d His summary of the economic shift happening now is that \u201chumans are cheaper than tokens on average, but good tokens are cheaper at scale. Management converts one into the other.\u201d<\/p>\n<p class=\"wp-block-paragraph\">UBS\u2019s sources describe working toward this exact fix independently through \u201cmodel routing\u201d\u2014matching specific tasks to specific models rather than treating AI as one undifferentiated tool. One AI firm executive explained the shift: \u201cAbout six months ago, we\u2019d take a whole task and say, \u2018all right, this model is probably the best model for it.\u2019 Now, the individual sub-tasks within that project will go to different models because we know exactly which models are good at which tasks.\u201d<\/p>\n<p class=\"wp-block-paragraph\">That same executive described the payoff in terms almost identical to Sivulka\u2019s \u201c100x\u201d framing: \u201cThere\u2019s a cost arbitrage opportunity when you can source from a bunch of different models\u2026 we have all this data on what the models are good at, what specific tasks they\u2019re good at, and so there\u2019s a lot of arbitrage on the price side that we can have and that we can pass on to our clients.\u201d<\/p>\n<p class=\"wp-block-paragraph\">Another firm described the split even more explicitly, telling UBS that for routine workflows \u201cwhere the human-like experience doesn\u2019t matter as much,\u201d they now use \u201ccheaper or faster models,\u201d while reserving frontier models for \u201ccore use cases\u201d that are \u201cour bread and butter\u201d\u2014a real-time version of Sivulka\u2019s argument that management, not raw model power, converts wasted spend into leverage.<\/p>\n<p class=\"wp-block-paragraph\">Sivulka also warned of organizational friction ahead, as employees start resisting handing over their institutional knowledge to AI systems that may eventually replace them. He pointed to Meta, where equity-holding employees\u2014despite being financially incentivized to want AI to succeed \u2014 have pushed back against the company using their own work context as training data, calling it \u201ca microcosm of what is about to happen across every industry.\u201d<\/p>\n<p class=\"wp-block-paragraph\">\u201cContext hoarding,\u201d he warned, is emerging as \u201cthe latest job security tactic,\u201d describing it as a \u201cmassive political problem with AI\u201d inside companies that will only get worse. \u201cEmployees don\u2019t want to teach AI systems their secret sauce,\u201d and now that they know their management isn\u2019t smart enough to use tokens cheaply, they have leverage.<\/p>\n<\/div>\n<p>#hired #million #bad #employees #tokenmaxxing #delivered #whats #promised<\/p>\n","protected":false},"excerpt":{"rendered":"<p>George Sivulka, the 27-year-ol&hellip; <\/p>\n","protected":false},"author":1,"featured_media":746,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[2],"tags":[828,1047,1049,420,378,673,704,1048,1046,1050],"class_list":["post-745","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-finance-news","tag-ai-agents","tag-bad","tag-delivered","tag-employees","tag-hired","tag-million","tag-promised","tag-tokenmaxxing","tag-tokens","tag-whats"],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v28.1 - 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