\n<\/p>\n
\n\t\tOpinions expressed by Entrepreneur contributors are their own.\t<\/p>\n
A founder reached out because she wanted to build her business from zero to $50,000 in a month. She was smart, driven and had a track record of six-figure success from another industry. She relied on AI and used it heavily for nearly every major business decision \u2014 pricing, hiring, marketing, content, delivery and strategy.<\/p>\n
After one 15-minute conversation, I could see the collision coming. Not because AI was giving her bad information, but because it was rarely challenging the assumptions she was bringing to the conversation. She wanted to wait until she was \u201cready\u201d to hire professional coaching.<\/p>\n
AI kept optimizing the pricing model she\u2019d prompted. It could tell her the business model itself wasn\u2019t financially sustainable at scale. In this case, she priced her services like Target when she was delivering premium 1:1 services. Initially, clients loved the prices because they looked like luxury at bargain rates.<\/p>\n
Within six months, she hit her goal. Revenue climbed as planned. So did payroll, rent and overhead. She hired faster than cash flow could support. She invested in expenses, like a physical office space that cut into her profit. Externally, she looked successful. In reality, there was no profit to reinvest in the business \u2014 or even pay herself. <\/p>\n
As the financial pressure mounted, the cracks began to show: Projects missed deadlines, quality declined, team members left after delayed paychecks, and marketing overpromised but operations underdelivered. Clients began to complain. And a business built on impressive revenue ultimately collapsed under the weight of its own overhead. She was out of business within a year. After all, revenue is vanity. Profit is sanity.<\/p>\n
Every one of those problems traced back to a lack of seasoned discernment, not ambition. None of her decisions were irrational in isolation. They were simply the decisions of a founder building from the identity that had gotten her this far \u2014 not the identity her next level required. And that\u2019s where AI became part of the problem.<\/p>\n
Every stage of business demands a different founder identity. The habits that build a $100,000 business aren\u2019t the same ones that build a million-dollar company. AI can only answer based on the assumptions of the founder prompting the question today. If you\u2019re asking AI questions from the beliefs, assumptions and constraints of yesterday\u2019s business, don\u2019t be surprised when it helps you build a more amplified version of yesterday\u2019s problems.<\/p>\n
As Marshall Goldsmith said, \u201cWhat got you here won\u2019t get you there.\u201d The greatest risk of AI is that it can become the world\u2019s most convincing confirmation-bias machine to your existing<\/em> knowledge bank, which can cap your growth.<\/p>\n AI can optimize a pricing, marketing and revenue strategy. It cannot tell you whether you\u2019re optimizing the right business model or whether you\u2019ve outgrown the mindset that created it. AI reflects your current operating system. It doesn\u2019t upgrade it. It doesn\u2019t initiate transformation. Transformation comes when existing biases, beliefs, skill sets, mindsets, attitudes, values, habits and strategies are challenged and questioned.<\/p>\n MIT researchers found that chatbots<\/a> reinforce a user\u2019s existing beliefs rather than challenge them. Even an idealized \u201cperfectly rational\u201d decision-maker could become increasingly confident in flawed assumptions simply because the chatbot consistently affirmed their thinking. The chatbot wasn\u2019t asking, \u201cWhat if your assumptions are wrong?\u201d<\/em> The danger was reinforcement. Dr. Keith Sakata<\/a>, a psychiatrist at UCSF who has seen 12 patients hospitalized for \u201cAI psychosis,\u201d says AI isn\u2019t \u201cbad,\u201d but it can \u201csupercharge\u201d people\u2019s vulnerabilities.<\/p>\n Over the past 15 years, we\u2019ve already watched social media companies spend billions of dollars optimizing for engagement. It\u2019s no secret they want to keep users \u201chooked.\u201d Research<\/a> suggests that users actually prefer chatbots that affirm their beliefs \u2014 even when that affirmation undermines better judgment. Agreement builds trust. Like attracts like, after all. Trust builds engagement. That\u2019s a powerful and potentially dangerous combination for a founder who doesn\u2019t question their own assumptions when the system is optimized to make you feel right.<\/a><\/p>\nWhen AI starts agreeing with you too much<\/h2>\n
The blind spot AI won\u2019t find for you<\/h2>\n