Who gets rich from get-rich-quick? Mostly the person selling it.

Academic tests of Fibonacci and similar chart levels find no predictive power beyond chance; the golden-ratio-in-nature justification is numerology.

Empire/Automators claimed 'AI machine learning' would maximise store revenues for hands-off buyers; the FTC alleges virtually all purchasers failed to earn the advertised income and most lost their entire investment.

Leveraged rentals built some fortunes, but most ordinary households build wealth through home equity and retirement savings, and leverage magnifies losses.

Most digital products sell almost nothing without continual promotion, so passive income for years is the rare case.

Most evaluation-based prop firm customers fail and pay repeated fees, and few beginners ever trade funded size or get paid.

Among 1,551 persistent futures day traders the single best earned US$310 a day on average with a daily standard deviation of US$2,560, and only 0.5% beat a bank teller's wage; nobody shows multiplying an account in a month, though the setting is Brazilian index futures rather than momentum stocks.

The winning examples are survivorship-selected; most narrative and meme tokens lose most of their value and retail buyers rarely get in early.

The UK Amway judgment quoted by Taylor found the thin top of the income 'candlestick' was those who had been IBOs longest (since 1979), while recent joiners and many long-term IBOs formed the base; position in the chain, not skill, drives outcomes.

Liquidity, slippage and market impact erode edges as size grows, especially in thin small caps, so profits do not scale by simply sizing up.

Affiliate commission income is heavily skewed: a few creators earn large sums while most affiliates earn little or nothing.

Firms that disclosed data show only a small share of funded traders ever receive a payout, and fewer receive repeated ones.

The median self-published title earns almost nothing and the store is flooded with low-content books, so ongoing royalties with little work is not the typical result.

Success rates come from vendor testimonials, and regulators have found most buyers of such coaching do not recoup their fees.

Marketplaces are flooded with generic AI-written products, and buyers pay for trust and depth that expertise provides.

Industry figures put challenge pass rates in the single digits to low teens, and funded accounts are usually simulated with tight drawdown rules.

Zero marginal cost is real, but most digital products sell very few copies and selling them takes ongoing hours of marketing.

Rising ad costs, copycat competition and thin margins are structural and sink well-executed stores too.

A one-day double-digit gain needs positions that lose as much on adverse days, and most retail options traders lose money.

Inflation erodes idle cash, but an emergency fund and a high savings rate are among the strongest predictors of household wealth.

MLM income disclosures and small-business survival data show the large majority of people who try earn little or nothing in the first year.

AI builders make prototypes, but reliable sellable software and finding paying users remain out of reach for most non-coders.

Consistency does not guarantee views; most channels never earn meaningful ad revenue.

Small businesses can build basic sites themselves with the same tools, and beginners without a portfolio struggle to charge designer rates.

UGC is crowded with new creators and many land few or no paid deals; the stated ramp is the seller's own figures, not measured outcomes.

Experiments on cultural markets show large randomness in outcomes at similar quality, and most channels never reach monetization.

Lead arbitrage exists, but ad costs, licensing, liability and contractor quality make margin without knowing the work unreliable for most.

A few sellers reach that scale; the typical outcome is a loss, so the implied generality fails.

Ad spend, customer service, refunds and supplier problems all grow with volume, so the work is not the same at any scale.

The recaptured payments are your own savings plus loan interest paid to the insurer; the gain comes from paying off debt or saving, which works without a policy.

Survivor anecdotes aside, people who quit jobs for ventures often see income fall, and commitment does not create demand.

Outside investors do back proven operators, but the typical reader has no track record and building one usually takes their own capital first.

Most landlords refuse subleasing on lease, insurance and building-rule grounds, and a small bonus rarely changes that.

Repeated attempts raise the odds but guarantee nothing, and most sellers quit or run out of money before a hit.

ESMA: leverage increases possible losses as well as gains, raises the probability of a larger loss more than of a larger gain for retail clients, and makes positions vulnerable to margin calls on small moves; the amount at risk is not fixed.

This counts growth on all prior premiums as a return on one year's deposit; measured properly, whole life cash value compounds at low single digits.

Published tests of opening-range breakout rules show small, unstable edges that mostly vanish after costs, and most retail day traders still lose.

Print-on-demand margins are thin and marketplaces saturated; most sellers make few sales, and the revenue examples shown are outliers.

The refund comes only after passing and usually after a first payout, which most buyers never reach, so the fee is usually lost.

A cash-out refinance can return capital only when values and rents cooperate, and the 'infinite return' framing ignores the larger loan, thinner cash flow and added risk.

Commission closers depend on an offer's lead flow and most quit within weeks; large quick earnings come from the few placed on top influencer offers.

Analyzer tools extrapolate from established listings and tend to overstate earnings for new hosts.

Studies of leveraged retail crypto traders find the large majority lose money, so running small stakes to millions is a lottery outcome.

64 of 70 disclosures attribute success to hard work or skill, but the FTC finds no basis given that these qualities distinguish successful from unsuccessful participants, and none acknowledges that saturation or lack of demand can constrain earnings regardless of effort.

Ten trades is far too small a sample to distinguish an edge from luck; statistical tests of trading skill need hundreds of trades.

Deposits can be small, but finding discounted sellers takes paid data and marketing, and deposits are often at risk once contingencies lapse.

Free puzzle generators are widely available and the category is saturated with near-identical listings, so the premise of few sellers is false.

Affiliate income depends on building an audience or search traffic, which is a skilled, slow process; most affiliates earn little.

Most small businesses never grow past a handful of employees, so scaling any side hustle to millions is a rare exception.

Dividing a target by days is arithmetic, not a method; median earnings show most people cannot sustain such a daily figure.

Market saturation, regulation and landlord refusals mean most entrants do not reach those revenues.

Influencer attention can move tiny coins briefly, but it has no lasting effect on large caps like the ones listed, and the bumps tend to reverse.

New service agencies take months or years to sign clients; a month-one seven-figure run rate is far outside any base rate.

Agency success stories are survivors; most new service businesses earn little in their first year and agency-course audiences crowd the same market.

Most retail options buyers lose money, and a steady daily income from a few thousand dollars implies returns far beyond any documented sustained performance.

Lenders require down payments on investment property; no-money deals depend on partners, sellers or a track record most beginners lack.

Regulator-mandated broker disclosures show most retail forex and CFD accounts lose money, and few beginners become consistently profitable.

Three past cycles loosely fit the pattern, which is too few to call peaks predictable, and ETF-era flows have already broken the timing.

The dividend rate is not the policy's net return after insurance costs; long-run cash-value returns are a few percent and loan rates are often similar, so the spread is small or negative.

News spikes cause slippage and whipsaw fills, many firms ban news trading, and the presenter herself calls it gambling.

Implied volatility is bid up before earnings and collapses after the announcement, so pre-earnings option buyers lose on average.

Short-term momentum and gap patterns offer at most weak edges that option premiums and spreads absorb; most retail option buyers lose.

Master-resell-rights schemes sell the same course to buyers who mostly earn by recruiting further buyers, so the market saturates; the FTC has acted against such income claims.

TikTok's terms bar buying and selling accounts, bought accounts risk bans, and she herself warns many sellers are scams.

Six-figure closers are the top tail of commission sales, and most people who try remote sales earn far less.

A chatbot can summarize public signals but cannot measure conversion rates or ad costs, so it cannot validate whether a product will sell profitably.

China-based sellers now hold a large share of Amazon marketplace listings and tariff changes have squeezed many FBA margins, so the threat is real even for branded consumables.

Commitment does not change the structural payout; income disclosures show nearly all participants lose regardless of effort.

Reposting proven content raises the odds somewhat, but virality is largely luck and competitors copy the same videos, so it does not reliably produce sales.

Creator surveys show brand deals remain the largest income source for most full-time creators, and the claim comes from a vendor.

Persistent futures day traders had negative expected daily profit (mean -US$48.81, 97% losing) with large volatility, the opposite of earning a year's investment return in a day at equal risk.

A deep in-the-money call needs less cash, but it adds leverage, time decay and the risk of losing the whole premium, so it is not less risky.

The arithmetic works, but most self-published digital products sell close to nothing, so a few sales a week is not easy.

Unlike franchises, recruits have no territory or customer base and the chain must grow geometrically to pay the levels above.

Depreciation and exchanges can defer real-estate tax, but paying zero tax is an edge case, not the norm for entrepreneurs.

Many cities restrict short-term rentals to primary residences or ban them outright, which rules out arbitrage entirely.

Most automation channels never reach monetization and freelancer costs eat margins, so thousands a month on two hours a day is rare.

Influencer coin picks show no persistent skill, and many promoted tokens underperform after the promotion.

Most retail forex accounts lose money per brokers' own disclosures, so as a business it is worse than most, not the best.

Most altcoins never regain prior highs, so holding through drawdowns usually locks in losses rather than gains.

Stops do cluster at round and recent levels, but no independent evidence shows retail traders profit from fading these sweeps.

The figures are tool estimates for a handful of top sellers; most low-content books sell few or no copies in a crowded category.

Skill raises the odds, but research on entrepreneurial and wealth outcomes finds a large luck component, so neither 'not luck' nor a reliable decade-long rebuild holds.

Matched betting is low-risk, but bonus supply is finite and accounts get restricted; typical sustained profits are a few hundred a month, not thousands.

Delta roughly approximates the chance of expiring in the money, not of clearing the premium paid, so the probability of profit is lower than delta.

Most faceless channels never reach monetization, and five-figure monthly claims come from operators selling courses.

Even the source's own test needed daily filming for a week and yielded modest profit.

Market data show falling revenue per listing and many arbitraged units earning below rent and costs, so a couple of thousand net is not typical.

The college wage premium is large and well measured, while coaching returns rest on self-reported, selected testimonials.

Ad platforms are easy to use but hard to profit from, and many small advertisers never reach a positive return.

Partnerships rarely send value to a token's holders, and the news is usually priced in by the time it is announced.

The returns cited are selected client testimonials; no independent data shows typical buyers recover high coaching fees.

Most challenge buyers never get funded or paid, so for the typical buyer the fees are a sunk loss, not tuition that pays back.

Automators' $5,000 one-on-one Amazon coaching advertised students making $10,000+ a month, but the FTC alleges virtually none of its coaching clients made the advertised amounts and most did not recoup their investment.

Tier limits, saturation and takedowns keep typical print-on-demand royalties small, and untapped niches close fast once found.

Markup ignores overhead, marketing and capital at risk; it is not an annual return on invested capital and cannot be compared to market returns.

Gross short-term revenue can beat rent in strong markets, but net of fees, cleaning, vacancy and management the premium is far smaller and often negative.

Oscillator-style indicators are lagging transforms of price; no independent evidence shows paid indicator suites produce an edge after costs.

Diversified index investing has delivered positive real returns over every long period on record; lack of control is not fraud.

Authority signals help conversion modestly, but most self-published books and courses reach few readers and do not presell prospects.

Some sellers underprice, but demand is price-sensitive for most offers; anecdotes of raised prices selling more are selected cases.

Raising a fund means securities-law filings, offering costs and a track record investors trust; first-timers rarely raise money this way.

Leveraged property bought badly can and often does lose money, as foreclosure waves show; early wins in rising markets do not generalize.

Platforms de-rank duplicate content and product interest fades, so reposting does not keep sales going without new work.

On-chain data show small holders (<1 BTC) buy as Bitcoin rises while whales and miners sell, so larger and more sophisticated investors profit at retail's expense even in calm periods, and 73-81% of retail entrants likely lost money; retail is the counterparty of insiders, not free of them.

The link rests on a couple of past cycles; with so few data points it has no demonstrated predictive power for altcoin prices.

A few large communities earn well, but most paid communities have few members and high churn; the earnings shown are platform outliers.

Guest messaging, turnover coordination, maintenance and reviews take far more unless a paid manager is hired, which cuts profit.

Most products fail to sell and each needs its own promotion, so one small win does not multiply into thousands a month.

Search engines demote thin scraped and aggregator sites, so most such directories earn nothing and are not passive.

Discoverability and retention, not scripts, are the binding constraints, and AI lowers the scripting barrier for every competitor equally.

Self-published book income is extremely skewed and most titles sell few copies, so volume alone does not reliably reach six figures.

Unaudited trackers can omit bets, and a few months of results are dominated by variance.

Wider targets are hit less often, so the risk-reward ratio alone does not change expected value; an edge needs entries with predictive power.

Retail option losses are concentrated in short-dated out-of-the-money contracts, where time decay and spreads eat the leverage.

Each unit needs fresh capital, a willing landlord and more management, and regulation caps many markets, so linear scaling is a best case.

Position sizing can reshape the daily P&L pattern but cannot create an edge; the consistency evidence is one trader's own record.

Off-the-shelf chatbot and automation tools cost tens to hundreds a month, so few small businesses pay agency retainers in the thousands for them.

Park yields run somewhat higher than single-family rentals for more work and risk, not many times higher, and low-cash seller financing is rare.

Bankroll rules limit variance but do not create an edge; sportsbooks and pick'em apps price in a house margin.

Most small-cap tokens lose most of their value or die; promoter-picked gems are a lottery where a few big winners hide many losers.

Family income is a powerful predictor of becoming an incorporated entrepreneur: a $100,000 higher 1979 family income is associated with an almost 60% higher probability of incorporation after controlling for ability, traits and education, which the authors read as credit constraints materially hindering entrepreneurship; the paper tests entry rather than founder discipline, but points the opposite way to the claim.

Startups produce many self-made fortunes, but most fail and median founder and early-employee payoffs are near zero, so they are high-variance rather than reliable.

Most Etsy shops earn very little, and digital printables are flooded with resold templates, so one seller's results don't carry over to most parents.

Stops cap losses in normal conditions, but gaps, slippage and fast markets can fill well beyond the stop.

Search engines state the top-level domain does not affect ranking; the figure is a registry marketing claim.

Empire consistently claimed hundreds of clients generating over $150 million in revenue; the FTC alleges this was false and most clients lost money, so this type of aggregate-sales claim was fabricated in at least this case.

Filters catch some rug pulls, but most meme coins still collapse toward zero and most traders of them lose money.

The figure uses mean side-hustle income; at the median it would take years, not months.

Order flow carries some information in academic data, but public flow alerts arrive late and noisy, and nothing shows retail traders profit from following them.

Total wealth grows, but being wealthy is largely relative and scarce assets are constrained, so positive-sum growth does not mean everyone can become wealthy.

Scriptwriting gigs exist, but freelance platforms are crowded, creators increasingly use AI themselves, and income for beginners is irregular rather than reliable.

Most monetized channels earn little from ads, so significant income is the exception rather than what monetization typically brings.

Issuers raise limits based on usage and payment history, and cut dormant lines.

Surveys of the ultra-wealthy show a large majority hold degrees, many from elite schools.

Survivorship bias is well documented; the specific path of a rapid success rarely replicates because luck and timing dominated it.

Easily replicated strategies see returns compete away as entrants flood in, consistent with observed cycles in self-publishing and dropshipping niches.

The vast majority of channels never reach monetization thresholds; early view counts are typically near zero.

Of the 601 surveyed participants, 25% made any profit over their whole time in the MLM: 14% under $5,000, 6% $5,000 to $9,999, 3% $10,000 to $24,999, 3% $25,000 or more, and 3 people (0.5%) $100,000 or more; 47% lost money (41% under $5,000, 3% $5,000 to $9,999, 1% over $10,000) and 27% broke even; all self-reported.

Possible with paid staff and a mature product, but most sellers report more hands-on time and the setup labor is excluded.

Wealth surveys show most large fortunes come from business ownership and assets, but high-earning salaried professionals do accumulate wealth by saving and investing.

Academic tests of candlestick signals on major indices find no profitable predictive power after costs.

First-hand tests and brokerage data lean to losses, but signal quality varies and samples are small.

Compounding arithmetic; no documented investor has sustained anything close.

All 30 MLMs with earnings data (and all 350 whose compensation plans Taylor reviewed) pay rewards primarily for recruiting a large downline, not for retailing products, with most payout going to the highest levels.

Customer acquisition is the dominant cost in online business, but many profitable stores run on paid traffic without an audience; the audience framing suits a YouTube coach.

Firms' own and regulator disclosures show low pass rates and fee-driven revenue; some firms have been sanctioned.

Authors conclude that for prospective day traders trading to learn is no more rational or profitable than playing roulette, yet under 3% of traders are predictably profitable, so it is not pure chance.

The college earnings premium and the stability of salaried work are well documented, though college is not right for everyone.

A few sellers reach that scale; the typical outcome is a loss, so the implied generality fails.

Creator time-use reports consistently show planning, filming and editing dominate, far beyond the posted runtime.

Professional quant and discretionary shops run on teams and paid data; part-time retail traders underperform in brokerage studies.

Lenders require 15 to 25 percent down on investment property, and repairs and vacancies are routine costs.

de Silva, Smith and So document retail losses of five to fourteen percent around earnings announcements from overpaying for implied volatility.

No retail trading bot has an audited record of beating the market; many are outright scams.

Ten trades is far too small a sample to distinguish an edge from luck; statistical tests of trading skill need hundreds of trades.

Dividing a target by days is arithmetic, not a method; median earnings show most people cannot sustain such a daily figure.

UGC marketplaces and gifting-only campaigns are standard practice; pay is low or in-kind for small creators.

Trend-following has some empirical support, but defining the trend is only clear in hindsight, so the rule is hard to act on in real time.

Celebrity-promoted tokens have repeatedly collapsed and regulators have fined undisclosed paid promotions.

Share of profitable day traders falls monotonically with days traded (29.8% to 3.0%) and fixed-effects panel regressions on persistent traders find no learning; expected profit stays negative.

Typical hard money terms are 10-20% down, 2-4 points, and 10-14% interest.

True of most large altcoins across the 2017 and 2021 cycles, with a few exceptions.

A framing about creative strategy with no clear evidence either way.

Major retailers prohibit reciprocal and incentivized reviews and have removed reviews and banned accounts for it.

The FTC Business Opportunity Rule bars earnings claims unless the seller has a reasonable basis, written substantiation, and gives prospects an earnings claim statement with the number and percentage of purchasers who achieved that level; a US legal requirement.

Sales performance develops over years of practice; no short course has demonstrated otherwise.