What the Hell Is AImaxxing?
In the age of looksmaxxing, savingsmaxxing, and tanmaxxing, you would be forgiven for being skeptical of this article’s title.
However, society seems to be at an adoption precipice. Instead of “to be or not to be?” it is increasingly “to AI or not to AI?”
Unequivocally, this article advocates for the latter.
Artificial intelligence can be applied across an unusually broad range of modern work and life, so why shouldn’t it be used to augment as many decisions, projects, and recurring processes as possible?
The low-hanging objections are fair: preserving authenticity, not outsourcing cognitive processes, minimizing the mass production of “slop,” protecting privacy, and preventing workers from becoming obsolete in specific industries.
AI can make people intellectually lazy. It can hallucinate and blatantly make up information. It can reinforce bad assumptions with tremendous confidence. It can generate ten pages of pristine-looking nonsense before breakfast. However, none of that is an argument for avoiding it. Instead, it is an argument for learning how to use it well.
For nearly every threat AI presents to the way people think and make decisions, it also presents opportunities to help diagnose, mitigate, or solve those same problems in response.
That is the idea behind AImaxxing.
AImaxxing is the deliberate practice of looking across your work and life for places where artificial intelligence can increase the quality, speed, scale, or sophistication of what you are already trying to accomplish. It does not mean outsourcing judgment. It means increasing the leverage of judgment.
The ways AI can improve aspects of life and society seem almost limitless: finances, careers, relationships, health, entrepreneurship, education, research, administration, and on and on and on.
Each of those domains contains hundreds of cascading decisions.
Some are microscopic:
Should I end this email with “sincerely” or “best” if I’m trying to develop a prospective client relationship?
Others involve considerably higher stakes:
How should I allocate $20,000 of a portfolio?
The mistake is to ask whether AI can “do” your job, run your business, manage your money, or make your decisions. Those questions are too large. Jobs, businesses, and lives are bundles of smaller activities: searching, calculating, writing, comparing, organizing, forecasting, coding, researching, checking, remembering, and ultimately deciding.
Break the work apart and the opportunities for augmentation become much easier to see.
Stop Using AI Like a Chatbot
Most people’s interaction with artificial intelligence still resembles an unusually sophisticated Google search.
They ask a question.
They receive an answer.
They close the tab.
That is using AI. It is not AImaxxing.
The greater opportunity comes when a recurring problem becomes a system.
Instead of:
Help me analyze this investment.
Ask:
How could I build a repeatable analytical process for evaluating investments?
Instead of:
Rewrite my résumé for this job.
Ask:
How could I build a system that maintains one source-of-truth résumé and generates truthful, role-specific versions from it?
Instead of:
What should my business website say?
Ask:
How can I create an operating system that continuously evaluates positioning, SEO, analytics, conversion, content, and customer acquisition?
The first question saves minutes. The second can change how you work indefinitely.
That is where the compounding advantage begins.
AImaxxing Your Money
Financial decisions and investing are one of the clearest places to see this distinction.
The stock market contains extraordinary amounts of public information, quantitative data, competing hypotheses, and uncertainty. Until recently, performing sophisticated portfolio analysis yourself generally meant acquiring substantial quantitative and programming knowledge or relying on professional tools and advisors.
AI can lower that practical barrier by helping people write code, understand methods, and organize analyses they would otherwise struggle to perform independently.
For example, PrimeStata developed an experimental retail-investment analytics pipeline after identifying a problem in a personal portfolio: more than 80 individual securities and funds had accumulated over time, creating substantial overlap and making it increasingly difficult to understand what each holding was actually contributing.
Using artificial intelligence as a coding and analytical partner, I was able to construct a Python-based pipeline that downloads historical price data, calculates portfolio-level performance, evaluates candidate securities, estimates factor exposures, examines correlations, performs principal-component analysis, compares optimization strategies, and generates proposed rebalancing actions.
Analytical/model outputs, not guaranteed investment returns.
AI did not choose the portfolio. It built the infrastructure for evaluating it.
The important metric was not merely return.
It was risk-adjusted return.
One commonly used measure is the Sharpe ratio, often summarized as excess return above a risk-free rate per unit of return volatility. In this specific analysis, the pipeline estimated the current tradable portfolio at a Sharpe ratio of approximately 1.15, while alternative optimization specifications produced modeled Sharpe ratios ranging from approximately 1.23 to 1.37.
And that difference matters for another reason: the models did not all recommend the same thing.
A maximum-Sharpe specification produced one portfolio. A minimum-variance model produced another. A factor-balanced approach produced another. A gain-versus-risk optimization produced yet another.
AI did not discover “the correct portfolio.”
It created the infrastructure to ask better questions about the portfolio.
That distinction is fundamental.
AI cannot guarantee investment returns, eliminate financial risk, or substitute automatically for professional financial advice. What it can do is give an ordinary person access to analytical capabilities that would previously have required considerably more programming expertise, statistical knowledge, time, education, or expensive outside assistance.
The purpose of AImaxxing is not to hand your brokerage password to a robot. It is to expand the set of analyses you are capable of bringing to a decision before your own judgment takes over.
AI Lowers the Minimum Viable Size of a Company
In my experience, artificial intelligence can give a solo founder considerably more entrepreneurial leverage.
PrimeStata itself is an example.
Since the beginning of the dot-com era, websites have increasingly functioned as the modern storefront. However, building a serious company website is not simply a matter of purchasing a domain and typing some copy into a template.
A modern commercial platform may involve web development, design, hosting, analytics, CRM infrastructure, search optimization, content strategy, accessibility, user experience, conversion architecture, maintenance, and security.
PrimeStata.com is a multipage consulting website with analytics tracking through Plausible, customer-pipeline infrastructure through HubSpot, hosting through Netlify, version control through GitHub, and a codebase involving HTML, CSS, JavaScript, and Python.
The interesting point is not that the software itself was inexpensive. It is that a single founder was able to coordinate work that ordinarily spans several professional functions.
AI frequently functioned as a proxy technical advisor: helping diagnose code problems, explain unfamiliar technologies, compare architectural options, generate implementations, inspect errors, and improve development workflows.
Beyond acting as a proxy CTO, AI has also served as a marketing and strategy partner.
Rather than treating questions about branding, page architecture, service positioning, SEO, analytics, social distribution, and conversion as isolated decisions, AI helped evaluate alternatives and build a coherent system around them.
It helped evaluate which analytics systems were appropriate, compare possible SEO targets, analyze website architecture, identify gaps in the buyer journey, develop social-media workflows, evaluate event opportunities, and challenge assumptions about how the firm should present itself.
AI generated alternatives. AI surfaced evidence. AI helped model tradeoffs.
The human still decided.
That may be the deeper organizational implication of artificial intelligence:
AI can lower the minimum viable size of an organization.
A founder does not suddenly become a senior software engineer, designer, CMO, lawyer, analyst, and operations executive simultaneously.
But the founder can increasingly orchestrate capabilities across those domains without needing to hire a separate human being for every intermediate task.
For a founder, that can change the economics of entrepreneurship.
AI as Bureaucratic and Risk Infrastructure
AI can also help navigate the less glamorous side of entrepreneurship.
Legal and regulatory work is one area where the distinction between augmentation and substitution becomes especially important.
Formal legal advice still belongs with qualified counsel when the stakes warrant it. Artificial intelligence should not be treated as an attorney simply because it can produce something that looks impressively attorney-like.
But much business administration consists of understanding forms, identifying requirements, organizing records, interpreting instructions, locating government resources, developing questions for professionals, and checking whether important steps have been overlooked.
Getting PrimeStata off the ground required company registration, administrative filings, tax documentation, banking infrastructure, contracts, and other bureaucratic processes.
AI helped translate unfamiliar requirements into manageable actions and provided a second set of eyes for identifying missing information or questions that needed professional clarification.
Artificial intelligence can also help strengthen due diligence.
PrimeStata may interact with prospective collaborators and clients well beyond its immediate professional network. Less familiar organizations naturally require greater scrutiny.
In those situations, AI can help formulate due-diligence questions, identify appropriate corporate-registration databases, compare supplied information with public records, organize risk factors, and identify inconsistencies requiring additional investigation.
It does not make the risk disappear.
It makes rigorous scrutiny dramatically easier to operationalize.
AImaxxing Your Career
If you are not trying to build a company, AI can still change how you navigate the labor market.
Headlines have repeatedly connected AI with layoffs, reduced hiring, and possible job displacement. The technology’s precise role varies across companies and labor-market conditions, but the concern is difficult for workers to ignore.
But workers have access to the same underlying technology.
Fight fire with fire.
As an academic looking to expand my university teaching portfolio, I faced a fragmented market. There is no single convenient button that says show me every psychology department in New York City that might need an instructor next semester.
So I used AI to construct the process.
It helped identify prospective institutions, research departments, organize contact information, and create a structured spreadsheet where opportunities could be moved through stages such as identified, contacted, application submitted, and interview requested.
It also helped assemble a reusable teaching portfolio summarizing previous courses and student evaluations.
Instead of conducting an ad hoc search across institutions, I now had a miniature academic recruiting pipeline.
Within roughly a week, multiple institutions responded, more than three conversations progressed toward interviews, and the process ultimately contributed to securing a fall teaching appointment.
Again, the value was not that AI “got me a job.”
AI could not interview for me.
It could not teach a psychology course.
It could not establish credibility with another academic.
What it did was remove enormous amounts of friction between deciding to pursue an opportunity and actually getting myself in front of the people who could offer one.
That is leverage.
Stop Asking AI to Rewrite Your Résumé
Traditional applicant-tracking systems provide another example.
AI has already reduced one small annoyance: many systems can now extract basic information from résumés without requiring applicants to manually retype every previous job into fifty separate text boxes.
But another problem remains.
Résumés increasingly need to be tailored toward particular roles.
Most people solve this by repeatedly uploading a résumé into ChatGPT, Claude, Perplexity, or another tool and saying some version of:
Make this fit the job.
That works.
But it does not scale particularly well, and unrestricted rewriting introduces another problem: hallucination.
A language model trying very hard to make you look qualified may quietly convert “worked alongside the analytics team” into “led enterprise analytics strategy.”
That is not optimization.
That is lying with excellent grammar.
PrimeStata therefore developed a different workflow.
A base résumé serves as the source of truth. A job description is introduced separately. The system then creates structured substitutions identifying language that can legitimately be reframed, prioritized, reordered, or removed for the target role.
For example, a general description such as:
Psychometrics | People Analytics | Assessment Science | Organizational Research
might legitimately become:
Analytics Leadership | Customer Insights | Retention & Decision Science | Applied AI
for a role where those aspects of the same experience are more relevant.
The underlying career history does not change.
The lens does.
Using VS Code, Python, and structured JSON edits, the workflow produces targeted versions while preserving the master résumé and documenting exactly what changed.
One source of truth. Multiple truthful representations.
Guardrail: Preserve the source-of-truth career history. Do not invent qualifications.
The result is a reusable system that can reduce the time and friction involved in tailoring high-quality applications while preserving accuracy and consistency across roles.
For job seekers, that means spending less time manually rewriting documents and more time evaluating opportunities, preparing for conversations, and deciding where their effort is actually worth investing.
If AI is helping employers move faster, job seekers should probably increase their own velocity too.
AImaxxing How You Learn
One of the most underappreciated applications of artificial intelligence is learning.
Historically, learning something difficult often involved a frustrating sequence: find a textbook, search for an explanation, realize the explanation assumes knowledge you do not have, search for another explanation, become stuck on one step, and either spend hours resolving it or abandon the problem.
AI can compress that loop dramatically.
Depending on the system and the subject, AI can explain the same concept at multiple levels of abstraction, generate examples, identify where reasoning may have gone wrong, produce additional practice problems, translate unfamiliar terminology, critique an attempted solution, and adapt the next explanation to the specific mistake that occurred.
That does not make expertise instantaneous.
For learners who use it carefully, it can reduce some of the time and friction involved in acquiring expertise.
Someone learning statistics can ask why a regression coefficient changed after introducing another predictor.
Someone learning Python can paste an error message and ask not merely how to fix it, but why the error occurred.
Someone studying mathematics can generate ten variations of the exact pattern they keep missing.
Someone entering an unfamiliar industry can construct a curriculum, glossary, reading list, and sequence of increasingly difficult questions in minutes.
The strongest use of AI in learning is therefore not:
Give me the answer.
It is:
Help me understand why I could not get the answer myself.
Used properly, AI becomes less like a replacement for education and more like an infinitely patient tutor, research assistant, sparring partner, and debugger.
The danger is obvious: if you ask it to do every difficult piece of thinking, you can create the illusion of competence without acquiring competence.
AImaxxing learning means using AI to shorten the feedback loop while keeping yourself inside it.
The Real Skill Is Task Decomposition
These examples may look unrelated.
Investing.
Building a website.
Marketing.
Due diligence.
Applying for jobs.
Learning.
They are not.
All of them involve the same underlying skill: breaking an ambiguous objective into smaller cognitive operations that can be augmented, automated, checked, or accelerated.
Suppose your goal is:
Grow my business.
AI cannot meaningfully execute that instruction by itself.
But growth contains subproblems:
Who is the buyer?
What problem is painful enough to pay to solve?
What services should be emphasized?
Which proof points are persuasive?
Where do buyers congregate?
What should the website communicate?
What should be measured?
Which prospects deserve follow-up?
Which content attracts the right people?
Which activities consume time without producing revenue?
Now AI has something to work with.
The same logic applies to nearly everything.
“Find a job” becomes prospect identification, role evaluation, résumé adaptation, application tracking, interview research, and follow-up.
“Improve my portfolio” becomes exposure analysis, diversification assessment, volatility modeling, candidate comparison, scenario testing, and rebalancing.
“Write better” becomes outlining, argument testing, evidence retrieval, editing, counterargument generation, and fact checking.
AImaxxing is therefore partly a problem of metacognition.
The better you understand what you are actually doing, the easier it becomes to determine where AI belongs.
Where AImaxxing Goes Wrong
There is a stupid version of AImaxxing.
It is worth avoiding.
The first failure mode is garbage in, garbage out.
If your data are poor, your assumptions are wrong, or your objective is badly defined, AI can optimize the wrong thing with astonishing efficiency.
The second is false confidence.
AI systems are remarkably good at producing language that sounds more certain than the underlying evidence warrants. Fluency is not validity. As Data Science Has a Science Problem argues, computational sophistication cannot substitute for evidence quality or methodological judgment.
The third is automation without understanding.
If you build a financial model you cannot interpret, deploy code you cannot inspect, or act on legal guidance you have not verified, you have not eliminated risk. You have hidden it behind a more sophisticated interface.
The fourth is privacy.
Not every document, client record, dataset, health detail, contract, or proprietary business problem belongs inside a general-purpose AI system. The more deeply AI becomes embedded in workflows, the more deliberate people and organizations need to become about information governance.
The fifth is overoptimization.
Sometimes the model is answering the question perfectly and the question itself is stupid.
Maximizing clicks can degrade trust.
Maximizing application volume can produce terrible applications.
Maximizing portfolio performance against historical data can create a beautifully optimized backtest that collapses under different market conditions.
AI is particularly dangerous when it makes an easily measured proxy look like the actual goal.
That is another reason human judgment remains central.
The purpose is not to automate everything that can be automated.
The purpose is to identify where automation actually improves the outcome.
AImaxxing Is Not Brainmaxxxing-Out
The easier it becomes to outsource cognitive work, the easier it becomes to stop thinking.
That would be the worst possible interpretation of AImaxxing.
AI should often increase the amount of thinking you can do, not eliminate it.
If AI generates five strategic alternatives in thirty seconds, your responsibility is to understand why one is superior.
If it produces statistical code you could not have written independently, your responsibility is to understand what the analysis is doing before trusting the result.
If it summarizes a legal requirement, your responsibility is to verify that requirement when the consequences matter.
If it produces a beautiful argument supporting exactly what you already believe, your responsibility is to ask it to build the strongest case that you are wrong.
The increasingly scarce skill may not be producing information.
It may be judging information.
That makes expertise more important in some contexts, not less.
AI can produce analyses, options, drafts, models, plans, and predictions faster than any person could reasonably inspect all of them.
Someone still has to decide which ones deserve to survive.
- 01Prompt
- 02Task
- 03System
- 04Judgment
- 05Compounding Leverage
AImaxxing turns isolated assistance into a reusable system while keeping human judgment in the decision loop.
Build Systems, Not Prompts
The lowest level of AI adoption is asking isolated questions.
The next level is developing good prompts.
The more consequential level is building systems.
A résumé question becomes a résumé engine.
An investment question becomes an analytical pipeline.
A website question becomes a continuous digital operating system.
A job search becomes a CRM.
A content idea becomes a publishing workflow.
A difficult subject becomes an adaptive learning system.
Each system becomes reusable.
And that is why AImaxxing can compound.
Saving five minutes on one email is useful.
Building a process that saves five minutes across hundreds of emails is considerably more valuable.
Automating one analysis is useful.
Building infrastructure that allows the next fifty analyses to happen faster is transformative.
The real gains from AI are therefore not always located in the output sitting directly in front of you.
They are often located in what becomes easier the next time.
This is the difference between experimenting with prompts and designing durable AI strategy and workflow systems. It is also why AI augmentation increasingly overlaps with business intelligence and decision systems: reusable infrastructure matters most when it improves the quality of a human decision.
So, Should You Be AImaxxing?
Yes.
But not because artificial intelligence knows everything.
It clearly does not.
Not because you should outsource every difficult decision.
You should not.
And not because every process improves simply because someone managed to insert a large language model into it.
Many do not.
You should AImaxx because human time, attention, working memory, technical knowledge, and cognitive bandwidth are scarce.
AI gives people a new way to allocate those scarce resources.
Use it to search faster.
Use it to calculate things you previously could not calculate.
Use it to learn unfamiliar tools.
Use it to challenge your reasoning.
Use it to automate repetitive work.
Use it to build systems.
Use it to create things that previously would have required another employee, contractor, or six months of learning.
Use it to ask better questions.
And then retain enough judgment to know when the machine is full of shit.
The question facing most knowledge workers is increasingly not simply whether AI will replace them.
A more useful question is how much more capable they can become by learning to work with it before somebody else figures out how to use it better than they do.
That is AImaxxing.
Russell Steiner is the founder of PrimeStata, where he works on measurement, analytics, and AI-enabled decision systems.