PANews has published a long-form commentary by Chen Xiaomeng examining why crypto users can spend months farming points and still end up with worse returns than simpler strategies. The article says points programs have become far more complex over the past two years. Instead of rewarding basic wallet interactions, many projects now tie points to deposit size, lending volume, trading turnover, LP liquidity, and holding duration. Some encourage users to loop borrowed funds so that 100,000 U in principal turns into several hundred thousand U in nominal deposits. Others let users use Pendle YT to gain point exposure measured in the teens or even dozens of times over. Some trading platforms, the author notes, effectively require tens of millions or even more than 100 million in volume just to have a chance of ranking for rewards.
The piece does not argue that points are useless. It says these systems have created more ways to chase rewards and, for some larger users, new income streams. The problem is that higher barriers usually come with higher capital costs and larger risk exposure. In the author’s framing, the real issue is not how many points a strategy produces. It is how much real cost is paid to acquire those points, and how likely those points are to become spendable cash in the end.
Why projects prefer points
Chen starts from the issuer’s side. For projects that have not launched a token, points are described as an efficient user acquisition tool. A protocol can use them to attract liquidity, push up trading activity, and raise product usage while delaying the actual settlement of rewards until later. For early-stage teams, that can reduce immediate pressure to pay cash or tokens and help build a user base before the product is fully developed.
The article gives a lending protocol as an example. If that protocol wants to bring in $50 million in total value locked, it can raise deposit rates and pay a clear subsidy cost up front, or it can use points to attract funds now and decide the eventual reward size and distribution later through tokenomics. That structure benefits the project immediately because the asset growth shows up in business metrics right away. For the user, though, the final reward amount, when it can be claimed, and what it is worth remain uncertain.
In the author’s view, projects want more capital and more users at the lowest possible incentive cost. Participants want the most rewards at the lowest possible capital cost. Those interests may align for a time, but not indefinitely. Once competition heats up and projects keep raising point multipliers, large amounts of capital can crowd into the same protocol for rewards even when the protocol’s real business demand does not grow at the same pace.
If the final reward pool stays unchanged, the user’s possible airdrop is diluted. That leads to the article’s first principle: the key drivers of eventual rewards are usually the user’s effective share of points, the size of the reward pool, and the price at which rewards can be realized. More points alone do not guarantee more profit.
Ten common points models and where the trap sits in each one
1. Interaction tasks: low capital needs, but fees and time add up
The author describes interaction farming as the earliest form of airdrop hunting. Users earn points through swaps, cross-chain transfers, borrowing, NFT minting, voting, and similar actions. Rewards may be based on interaction count, active days, cumulative volume, or other behaviors.
The upside is low capital usage. Many tasks can be completed with small amounts of money. The downside is fragmented cost, especially when activity has to be maintained over a long period. The article uses a simple assumption: if a project requires 50 interactions and each costs 0.5 U in gas, on-chain fees alone reach 25 U. If the process also involves repeated bridging, illiquid pairs, or extra service charges, the number goes higher. And if the campaign requires daily check-ins and active monitoring, time spent should also be treated as a cost.
There is also a screening risk. The piece says many projects review trading patterns, address links, funding sources, and operational behavior to filter out suspicious accounts. Paying the fees does not ensure that rewards will actually be distributed. Chen then adds a blunt personal judgment, writing that this category is already “dead” and saying it may be better to farm “alpha” instead.
2. TVL deposit farming: the easiest place to underestimate opportunity cost
TVL-based points systems are now one of the most common DeFi incentive structures. Users deposit stablecoins or other assets and receive points based on deposit size, duration, and multipliers. For larger users, the strategy is operationally simple and points accumulate steadily. The article argues, though, that this is exactly where users are most likely to forget what their capital could have earned elsewhere.
One example in the piece uses a project that pays a 3% base annualized yield and adds points on top. If a user has 100,000 U and a strategy with similar risk can earn 7% annually, joining the points program means giving up about 4,000 U in potential yearly income. If the capital is tied up for three months, the opportunity cost is close to 1,000 U.
The article says users need to compare base interest, potential airdrop value, and the return from an alternative strategy in one framework. Smart contract risk, depeg risk, redemption limits, and concentration risk should also be part of the decision.
3. Lending points: looping amplifies points and risk at the same time
Lending points usually reward deposits, borrowing, or both. When a protocol lets users redeposit borrowed assets, it creates a looping strategy. The piece models a user with 100,000 U and a maximum borrow ratio of 80% of collateral value. If each borrowed amount is redeposited and limits and fees are ignored, that capital can theoretically become about 500,000 U in total collateral and 400,000 U in debt.
If points are awarded based on total deposits, the nominal points exposure may rise fivefold. But that also leaves the user paying interest on 400,000 U of debt and facing a much more liquidation-sensitive position. Even if both the collateral and the debt are stablecoins, the author says depegs, rate changes, and oracle problems can still cause losses.
Looping, in this framework, requires a full calculation of net carry, borrowing-rate volatility, the relationship between collateral and debt pricing, and changes in the health factor. For positions near liquidation thresholds, a small amount of extra points may not come close to covering the risk being added.
4. LP market making: fees and points do not prevent underperformance versus holding
LP farming often combines trading fees, liquidity incentives, and extra points, which is why interfaces can show a very attractive blended APR. The article says that figure can be misleading if the comparison benchmark is not set properly.
Using a standard ETH/USDC liquidity pool, Chen notes that if ETH keeps rising, the pool mechanics gradually reduce the user’s ETH exposure and increase the USDC share. The user may collect fees and points but still end with a portfolio worth less than simply holding the initial ETH and USDC.
The point is not that LP farming is always bad. It is that the correct benchmark matters. For a strategy funded by ETH and USDC, the article says a buy-and-hold portfolio with the same starting assets is the meaningful reference. Only after combining fees, incentives, points, and the rebalance effect can users tell whether the LP strategy actually outperformed.
5. Pendle YT: points need to be priced like a purchased asset
The commentary calls Pendle YT one of the clearest examples of a modern points strategy. In Pendle, a yield-bearing asset can be split into PT, which represents principal, and YT, which represents the right to future yield before maturity. After buying YT, users can receive the related yield stream and, where program rules allow, the associated points and rewards.
Because YT usually trades well below the nominal value of the underlying asset, users can gain a large amount of points exposure with relatively little money. That is why the market often talks about point efficiency in the high multiple range. But YT also has a very clear time-decay profile. As maturity approaches, the remaining duration of future yield keeps shrinking. After maturity, YT no longer carries the right to future yield and its own value falls to zero. Any rewards earned before that point are handled according to the relevant program rules.
The article says the key variables are the entry price, expected underlying yield to be collected, points valuation, and time remaining. One example assumes a user spends 2,000 U on YT and can collect only 120 U in underlying yield before maturity. In that case, the remaining cost has to be justified by the value of the points. Chen adds a personal note here as well, saying that right now the strategy only makes sense for “free money” or swing trading and that paying cash just to buy points is not attractive.
6. Perpetuals volume farming: the bigger the volume, the harder friction is to ignore
Perp platforms often distribute points based on trading volume, holding time, maker liquidity, or activity levels. Some also run seasonal leaderboards to push users toward ever larger turnover.
The author says these campaigns create very large headline numbers. A user with just 10,000 U of margin can generate millions in cumulative volume through leverage and frequent trading. But volume is not profit. Every trade can incur fees, slippage, and funding costs, while open positions also carry market volatility and liquidation risk.
Chen’s wording is direct again, writing that after “var,” perp activity can at most be treated as a form of yield strategy. The article’s broader point is simpler: if the strategy’s economics depend on huge volume, trading friction must be tracked line by line instead of waved away because the point count looks impressive.
7. Spot trading points: spreads and price moves hide a large part of the cost
Spot points programs often use cumulative volume, specific pairs, continuous trading days, and token holding requirements. Unlike perpetuals, there is no funding rate, but users still face trading fees, bid-ask spread, and execution slippage. In less liquid pairs, repeated buying and selling can become expensive quickly.
If the campaign also requires holding a new token for an extended period, price swings have to be included in the final result. The article says it is entirely possible for the token’s decline to erase the entire potential airdrop, even if the user earns a large number of points.
The most efficient approach in this category, according to the author, is to combine points tasks with spot trades the user already intended to make, minimizing extra turnover created only for farming.
8. Social and content points: time and attention also need a price
Social points include posting, replying, producing content, contributing to communities, and competing on various leaderboards. For users who are good at research, writing, or community work, the article says this category can be capital efficient. Chen describes it with the colloquial phrase commonly used in the market for “talk-based farming.”
The piece also notes that content itself can create long-term value through audience growth, research accumulation, network expansion, and potential business cooperation. Those benefits have economic value of their own. But they should be separated from the direct return attached to the project’s points.
For KOLs, the author says the better approach is to focus first on projects they would have researched anyway and treat points as an extra incentive. That way, even if no airdrop materializes, the time spent on analysis and content may still pay off elsewhere.
9. Referrals and community programs: point boosts and cash income are not the same thing
Referral commissions, community partnerships, and accelerated point mechanisms have become key growth tools for many projects. For participants who can organize users or capital, these structures may create additional revenue.
But the article says community operators also take on credibility risk. If they recommend a project with unclear underlying risk in exchange for higher rebates, problems with the underlying asset may do more damage than the short-term cooperation fee can justify. The piece says users should look closely at cooperation terms, reward attribution, payment timing, actual realization conditions, and how benefits are disclosed to end users.
10. Staking and lockups: bigger multipliers usually come with weaker liquidity
Staking, lockup, and node-based points generally use time weighting to encourage long holding periods. The article gives an example where locking for 30 days earns base points, 90 days earns 2x, and 180 days earns 4x.
That may help a project stabilize its capital base, but for the user it means giving up some liquidity. If the underlying token drops sharply during the lockup or a better market opportunity appears, the position may not be adjustable. If early exit also comes with a penalty, the real cost rises again.
For volatile assets in particular, the author says lockup points have to be evaluated together with token price risk, the value of the points, and the cost of time. For larger principals, liquidity itself is a meaningful asset characteristic and cannot be ignored because the displayed multiplier is higher.
A unified way to value points
After mapping the types of programs, the article moves to valuation. Chen says many people estimate airdrop value based on fundraising size, backers, and market hype. A project with a large raise may be assumed to reach a $500 million fully diluted valuation after TGE, which then leads users to assign a high value to its points. The article argues that this is not enough.
FDV alone cannot determine an individual user’s reward. To get closer to a realistic estimate, a participant must know how much of the token supply is allocated to the relevant points pool, what share of that pool the user effectively controls, and at what price the tokens can ultimately be sold.
For projects that distribute rewards linearly by points share, the article gives a base formula:
Theoretical points value = FDV × token allocation for the points pool × the user’s effective points share ÷ total effective points across the network
The article gives a numerical example. If final FDV is $500 million, 2% of supply is allocated to a given points pool, and the user owns 0.06% of the effective points in that pool, the theoretical reward value is $6,000.
Even that is still only a reference number. The article says unlock schedules, vesting, and market sell pressure can all reduce the amount that can actually be realized. To account for this, Chen suggests applying an overall realization coefficient. If the coefficient is 50%, expected realized value falls from $6,000 to $3,000.
Then comes points inflation. If the project keeps the campaign open and total effective points in the network rise to three times the original amount while the user’s own points stay the same, the user’s share falls to one-third. With FDV unchanged, the theoretical reward drops from $6,000 to $2,000. Apply the same 50% realization factor and expected value falls again to $1,000.
The author therefore identifies at least four core variables in points valuation: final FDV, reward-pool allocation, the user’s effective points share, and actual realization conditions. A meaningful change in any of them can alter the strategy’s expected return.
The article also recommends modeling at least four scenarios: optimistic, base, pessimistic, and zero airdrop. In each scenario, the final airdrop amount should be compared with the real cost of the strategy. If a setup only breaks even in the optimistic case, Chen argues that position sizing should be much stricter.
The real cost usually sits outside the yield dashboard
One of the easiest mistakes in points analysis, the article says, is counting only visible fees. A complete points investment should include at least five components: direct costs, opportunity cost, strategy P&L, liquidity cost, and tail risk.
- Direct costs: gas, bridge fees, trading fees, slippage, borrowing interest.
- Opportunity cost: what the capital could have earned in a comparable alternative.
- Strategy P&L: LP rebalancing effects, YT decay, and trading gains or losses.
- Liquidity cost: lockups, redemption queues, and discounts on early exit.
- Tail risk: smart contract exploits, depegs, liquidations, and protocol failures.
The first several categories can often be quantified with records and market data. Tail risk is harder to model, but the article says it still needs to be managed through position limits, scenario analysis, and diversification.
To compare projects in one framework, Chen suggests two metrics. The first is:
Absolute net return = final net asset value + realized rewards − initial net capital outlay − extra costs.
The second is:
Relative excess return = actual net return from the points strategy − net return from a risk-comparable alternative.
The article warns that lending strategies must first subtract any unpaid debt, and any profit or loss already reflected in final asset value should not be counted twice. For LP and YT positions, valuation should be based on the final realizable value of the assets rather than the starting principal.
One simple example in the text shows a points trade earning 1,000 U over three months while a comparable alternative would have earned 2,000 U. On an absolute basis the account is up. On a capital-allocation basis it has underperformed by 1,000 U. That is why the author says expected points value should be tracked separately from cash yield that has already been realized.
The article also distinguishes APR from APY. APR is generally a simple annualized rate, while APY reflects annualized return under a specified compounding assumption. Since different platforms present these metrics differently, adding base APY, token incentive APR, and unconfirmed points value in one line can materially overstate the true return.
Five examples of how points strategies can still lose to the benchmark
Case 1: A 100,000 U deposit for 90 days with a 500 U airdrop
The first example assumes a points project offers a 3% base annualized rate. A user deposits 100,000 U for 90 days. At the same time, a strategy with similar risk is available at 7% annualized.
Using simple annualization and a 365-day year, the points project earns about 740 U in base interest while the alternative earns about 1,726 U, a gap of roughly 986 U. If the points strategy also requires 100 U in operating costs and ends with a 500 U airdrop, total profit in the points strategy is still 1,140 U. But compared with the alternative, the user is still short by about 586 U.
Under those assumptions, the article says the airdrop would need to reach about 1,086 U just to cover the interest gap and operating costs. That is the break-even line for opportunity cost.
Case 2: Spending 2,000 U on YT
The second example assumes a user spends 2,000 U to buy a YT with 90 days left to maturity, receives 120 U in underlying yield before expiry, and pays 30 U in fees. If YT is held until maturity and its residual value goes to zero, the direct break-even condition is:
2,000 − 120 + 30 = 1,910 U
In other words, the airdrop has to be worth 1,910 U just to offset purchase cost and fees. If it ends up worth only 1,000 U, the strategy loses about 910 U in real terms. If the foregone return on the 2,000 U over those 90 days is added, the economic break-even line is even higher.
The article says this kind of calculation is essential for reading YT pricing. High point multipliers do not help if the market price already embeds an overly optimistic airdrop assumption.
Case 3: Looping 100,000 U into 500,000 U of collateral
The third example uses a user with 100,000 U who can loop at an 80% ratio and theoretically build 500,000 U of collateral and 400,000 U of debt. Collateral yields 4% annually, debt costs 6%, and rates are assumed unchanged over 90 days.
On those inputs, the 500,000 U collateral position generates about 4,932 U in interest, while the 400,000 U debt incurs about 5,918 U in interest expense. The base carry from the loop is therefore -986 U. The article notes that real markets may also impose capacity limits, rate-curve effects, or anti-looping points rules, so the full 5x notional expansion may not be achievable anyway.
The point of the example is that the nominal size of the position becomes much larger, but the user’s actual equity is still only 100,000 U. Points efficiency may rise, while balance-sheet complexity and liquidation risk rise with it.
Case 4: Farming 10 million U in perp volume
The fourth example looks at a perp platform that rewards trading volume. A user generates 10 million U in cumulative turnover during a season, pays an average effective fee rate of 0.04%, suffers average execution slippage of 0.02%, and pays a net 10,000 U in funding.
That implies 4,000 U in trading fees, 2,000 U in slippage and related execution costs, plus the funding expense, for a total cost of 7,000 U according to the article. Ignoring directional trading P&L for the moment, the points airdrop must be worth at least 7,000 U to cover those costs. If the airdrop comes in at only 3,000 U, the net loss is 4,000 U. If directional losses also occur while generating the volume, the damage grows further.
Chen says users should keep a separate trading-cost ledger for this kind of campaign and compare the cost per 1 million U of volume with the expected airdrop value, updating the estimate as fee tiers, maker ratios, and rebates change.
Case 5: A 20% LP annualized figure still loses to holding
The final example starts with 50,000 U of ETH and 50,000 U of USDC, for a total of 100,000 U, deposited into a standard 50:50 constant-product liquidity pool.
Over the next 90 days, ETH doubles. If the user had simply held the initial assets, the portfolio would be worth 150,000 U. In the LP, ignoring fees and incentives, the automatic rebalancing leaves the position worth about 141,421 U, roughly 8,579 U less than holding the assets outright.
If the LP also earns a 20% simple annualized return in fees and incentives based on the initial 100,000 U, that adds about 4,932 U over 90 days. Final LP value rises to around 146,353 U, but it still falls short of the 150,000 U from holding by about 3,647 U.
The article says this shows why LP annualized yield and LP excess return relative to holding must be calculated separately. For more volatile assets, the path of price changes and the chosen range can have a large effect on the outcome.
Seven warning signs that a points project is more likely to disappoint
After the strategy analysis, the article turns to project quality and lists seven signals to watch:
- The project keeps marketing high point multipliers but never states the size of the reward pool.
- Seasons are extended again and again while the total points supply keeps inflating.
- Rules change frequently and users cannot form a stable expectation.
- Business growth depends heavily on subsidies.
- Ordinary users and special participants face clearly different incentive terms.
- Valuation expectations rely too much on market hype.
- The exit mechanism is complex and liquidity is weak.
The article explains that point multipliers do not say anything by themselves about dollar value. Without clear disclosures on distribution, effective points rules, and conversion mechanics, users cannot judge the reasonable cost of earning the points. If seasons keep getting extended while the pool is fixed, the value per point can keep falling. If TGE is pushed back, early users carry more capital lock-up cost.
The same logic applies when rules shift repeatedly, growth is driven mostly by incentives, or certain users enjoy much better terms than ordinary participants. The author argues that in those cases, users need to recalculate based on the conditions they can actually access. If a strategy only works under a very high TGE FDV, it is extremely sensitive to post-listing pricing. Add long cooldowns, withdrawal queues, quotas, lockups, and secondary-market discounts, and the risk profile worsens again.
Building a personal framework to avoid getting farmed back
For long-term points participants, Chen says the most important step is to create a fixed evaluation process instead of relying on market mood every time a new campaign appears.
The first step is to classify projects by risk level. The article says the author typically splits them into three groups based on the relationship between base yield and expected points value. One group is more suitable for long-term capital allocation. A second needs ongoing tracking of points economics and alternative yield. A third requires especially strict position limits, with YT, perp volume farming, and high-cost lockup strategies specifically mentioned.
This classification matters more as portfolio size grows. Once capital is large enough, stable and reasonable returns become meaningful in absolute terms. Giving up too much base yield to chase uncertain points can drag down the performance of the whole portfolio.
The second step is to define exit conditions before entering. Many points campaigns go through season changes, rule revisions, and timeline extensions. If participants keep allocating more money simply because they have already accumulated a large number of points, sunk-cost bias starts to drive decisions.
The article therefore recommends deciding in advance how long to participate, how much capital to commit, how much cost is acceptable, and what changes should trigger a reduction or exit. The examples given include slower point accumulation, persistent inflation in total points, rising borrowing rates, the end of project subsidies, or changes in redemption terms.
Past costs cannot be recovered, the author writes. Future decisions should be made based on what the remaining capital can still earn from this point on. Even after participating for months, there is no need to keep paying unreasonable new costs only to preserve old points.
The third step is to evaluate points inside the total portfolio. Joining 10 projects at once does not automatically create diversification. If those projects all depend on the same stablecoin, the same lending asset, or the same bridge, the underlying risk can still be highly concentrated.
The article is especially cautious about layered DeFi structures. It describes situations where capital is deposited into one protocol to receive a yield-bearing receipt, that receipt is then used as collateral to borrow, and the borrowed funds are deposited into another points project. This can stack multiple revenue streams, but it also increases smart contract dependence, asset-conversion complexity, and liquidation risk.
For that reason, the author recommends that a capital-management sheet record at least the following: principal deployed, current net equity, debt balance, base yield, points valuation, redemption timeline, and dependencies on underlying protocols. Unclaimed points, the article adds, should ideally be booked in a separate line item and marked conservatively so that realized profits and possible future rewards are not mixed together.
Scoring systems and probability scenarios
To improve screening efficiency, the piece says the analysis can be turned into a simple scoring framework. Each dimension can be rated from 0 to 100 and combined by weight into an overall score. As a rough guide, the article suggests treating scores above 80 as worthy of priority study, 60 to 80 as requiring caution, and below 60 as not worth allocating capital to for now.
At the same time, the author says the score is only a way to maintain a consistent decision standard. The weights and raw scores remain subjective. For projects with serious smart contract vulnerabilities, opaque custody, or visibly abnormal redemption mechanisms, there should be separate risk veto conditions so that a high expected payoff does not hide severe principal risk.
The article also discusses estimating expected value through scenario probabilities. It says that if a points project has four possible outcomes and participating requires a certain 500 U in costs with no other return, the expected net result can still be -20 U. The exact probability assumptions are not presented as predictive fact. Instead, the author says the model is useful because it forces the participant to consider low-return and zero-airdrop cases rather than deciding based only on the most optimistic one.
For higher-risk strategies, Chen adds that expected return is not enough by itself. The user should also check the maximum loss under extreme scenarios, because a single large principal loss can damage long-term compounding even when expected value looks positive.
As capital grows, base cash flow matters more than points speculation
The final section broadens the argument beyond points. Chen writes that many users spend a great deal of time studying point multipliers and possible airdrops but rarely calculate how much annual cash flow their existing capital can already produce.
For smaller users, low-cost interactions and small-scale points speculation may still be attractive because the capital itself does not generate large absolute returns. Time spent pursuing high multiples can sometimes improve the overall outcome. Once capital reaches 100,000 U, 300,000 U, or even 500,000 U, the balance changes.
The article gives a 300,000 U example. If that capital can earn 8% annually under acceptable risk, it can theoretically produce 24,000 U per year, or about 2,000 U per month. The author explicitly notes that this has to be reduced by actual fees, taxes, and possible losses, and that an assumed annualized rate is not a guarantee. Even so, the base yield at that scale already has material economic value.
If a user shifts a large part of that capital into a points campaign and base yield falls by 4 percentage points, the annual opportunity cost on 300,000 U is 12,000 U. Even if the funds are tied up for only three months, the yield gap is about 3,000 U. That means a points project can distribute several thousand dollars in airdrop value and still add very little net value after costs.
Chen says the more reasonable setup is to allocate most capital first to core strategies the investor understands well and can monitor over time, then use a limited risk budget for points investing. If a protocol already offers acceptable base yield, points can be treated as additional optional income. If a strategy depends heavily on an airdrop, position size should be determined by actual cost and worst-case loss.
The article also warns about stacking multiple points campaigns on top of the same capital. Some users deposit funds into one protocol for points, place the receipt token into another protocol for extra rewards, and then use borrowing to expand exposure further. That can look capital-efficient in calm conditions, but if the underlying asset or one of the protocols fails, the risk can transmit across the whole chain of positions.
For people trying to build long-term cash flow through DeFi, the author says the cleaner the source of yield, the easier the costs are to count, and the more controllable the exit conditions are, the easier the total portfolio is to manage. Points are still worth studying, but they belong inside a full capital-management framework.
A 10-question checklist before joining a new points program
The article closes with ten questions users should answer before committing funds:
- Are the points rules clear, including use, reward mechanism, and qualification criteria?
- Can the size of the reward be estimated, including the allocation for the relevant pool and the rough size of total network points?
- Is the user’s points share reasonable, or is the judgment based only on high multipliers?
- What are the direct costs, including gas, fees, slippage, and borrowing interest?
- What is the opportunity cost versus a comparable alternative?
- What risks sit in the underlying asset, including depegs, price swings, LP rebalancing, or YT decay?
- Is leverage involved, and if so are the health factor, liquidation threshold, and rate changes manageable?
- Is exit easy, or are there lockups, queues, weak liquidity, or early-exit penalties?
- Can the worst outcome be tolerated if the airdrop is worth zero?
- What is the exit trigger, including maximum cost, target timeline, and reevaluation conditions?
In the last lines, Chen writes that mature points participants need to learn to pass on some opportunities. The market may keep producing more complicated points structures involving new yield receipts, trading mechanisms, capital-efficiency tools, and multi-protocol incentive combinations. But the basic discipline does not change: focus on actual capital deployed, count the full cost, estimate realizable rewards, and leave enough room for bad outcomes.
The author’s final message is straightforward. Once a user has meaningful principal, one of the most important skills is choosing which attractive-looking opportunities to ignore. Sometimes not joining an expensive points campaign and leaving funds in a strategy that is easier to understand and manage is itself a sound investment decision. Points can improve portfolio returns, but every extra unit of return has to be judged together with the cost and risk required to earn it.