Token-maxxing is the intentional use of every possible AI token at your company's disposal. Because AI power players subsidize our consumption with discounted service costs, only a fool would let a single token go unused. Providers tune their Reinforcement Learning by subsidizing interaction with their models. It's a win-win.
To maximize your token use, aggressively implement AI into every element of the organization. For HR, spur your Return To Office (RTO) initiatives by having the staff chat with agents chatting with AI. Automated prompts are the new impromptu conversation. Data Centers are figuratively and literally the new water cooler. Have those culture-constructing water-cooler conversations with AI agents. Use the most expensive models.
On the Marketing front, we can't afford to be perceived as behind our competition. Token-maxxing is critical to brand positioning. We can mortgage the future by hemorrhaging cash now. From our perch atop the Token Totem pole, we'll be able to see which trends eventually profit from AI usage.
In IT, instead of decommissioning our old machines, we're re-purposing them to run local LLMs, imagine new features of the software, simulate end users who chat with our cloud-based agents about the undeveloped features, and build a pre-knowledge base about how to troubleshoot bugs before the software ever comes out.
Today's measure of productivity is tokens consumed, not tangible units produced. We can cut heads today by hiring Claude and Chad Jippity. At today's subsidized prices they're a little cheaper than human developers. Our quarterly profits will be reported before the next AI price hikes hit the books.
Excellent insights! I personally prefer the pre-proift phase because the infinite upside is always ahead us.
Inspirational -- I will found the first zero-person billion-dollar unicorn. Bleep bloop.
"Win-win" is a generous view of intentionally burning tokens merely to enhance AI adoption metrics. Although they're not physical, tokens cost real money, electricity, and water to produce and deliver. The last time I checked, humans need those resources to live. Competing with Big Data for those is bad enough without creating an bot vs bot echo chamber for Reinforcement Learning. Eventually, the knowledge base will stagnate.
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