oAdam

A 股每隔十年左右，就会来一轮牛市。

2015 年，收割了一批 85 后。

2026 年，轮到 95 后了。

这轮 AI 牛市，台前是大模型、科技英雄，台后真正赚得盆满钵满的，可能是芯片、内存、光模块、PCB 这些以前被嫌弃的传统产业。

老玩家一边做生意赚订单，一边在股市里赚估值。

故事讲给年轻人听，利润留给懂行的人拿。

等泡沫散场，有人带着真金白银离开，有人留下高位买入的股票，还有人要花好几年，才能把亏掉的钱赚回来。

每一代年轻人都以为自己赶上了时代，直到发现自己只是这场盛宴的买单者。

There’s a subtle kind of loss in investing: not being able to wait.

If an investment doesn’t go up today, you want to switch to another one. If you haven’t made money this week, you want to increase your position. If you see others making money, you start chasing the rally.

After all that activity, you haven’t made much money, but you’ve spent plenty of energy and racked up trading costs.

Try taking a two-year view instead.

Rather than guessing the market every day and frequently switching investments, first find assets you truly understand and are willing to hold for the long term. Assess the risks carefully, then give your judgment some time to play out.

Of course, holding for the long term doesn’t mean buying and then ignoring your investment. If the fundamentals change or the risk exceeds what you can tolerate, you should still make adjustments.

Patience doesn’t mean stubbornly holding on no matter what. It means not letting short-term fluctuations lead you around.

You don’t have to succeed today or prove yourself by next week. Focus your energy on what truly matters, and make time part of your strategy.

You don’t need to do something every day in investing. Sometimes knowing when not to act is more important than knowing when to buy.

Is crypto just a passing fad, or part of the future?

During bull markets, some believe it can change the world; during bear markets, others are quick to declare it dead.

But you can’t judge the future of an industry by price movements alone.

With every cycle, some people leave, while others keep writing code, building products, and developing infrastructure.

Of course, not every project deserves to survive, and not every token has a future.

Bubbles burst, but that doesn’t mean the industry as a whole has no value.

Instead of arguing about whether crypto is the future, let’s see what problems it can ultimately solve and what it can leave behind.

The future isn’t proclaimed—it’s built, one step at a time.

When it comes to driving financial innovation, support among Americans is much higher—as long as you don’t mention Crypto directly.

A survey conducted by @fund_defi and @Ipsos found that 35% of respondents support political candidates who promote the development of the next generation of financial technology in the U.S. But when the question explicitly mentioned blockchain, cryptocurrency, and DeFi, support fell to 13%.

Another set of figures is even more telling: 74% believe the financial system needs to change so they can have more control over their money and assets. Yet only 9% say they know Crypto very or extremely well, and only 5% have complete or a high degree of trust in decentralized platforms.

Demand doesn’t equal trust, and wanting to reform the financial system doesn’t mean believing in Crypto.

The survey also found that people open to DeFi prioritize using it to save and transfer money within the U.S., rather than to speculate. Crypto ownership rose by 4 percentage points, but usage over the past year remained at 13%.

This shows that the industry can’t focus only on bringing in new Crypto holders.

What ordinary people really need may be cheaper transfers, more convenient asset management, and more direct control over their money. They may not care whether these features are powered by blockchain.

For Crypto to become part of everyday life, the answer isn’t to keep explaining how advanced the industry is—it’s to help ordinary people experience what it can actually do for them.

Four reasons to make more money:

1. So you don’t panic when your parents get sick.
2. So you can give your kids what they need.
3. So you can walk away when life treats you unfairly.
4. So you can hold your family together when things fall apart.

There’s nothing wrong with not having much money.

But never having enough to choose your own path is a different story.

Four rules to live by:

1. Never speak in absolutes.
2. Never burn every bridge.
3. Never underestimate anyone.
4. Never leave yourself with no way forward.

Leave a little room in everything you do.

Having room to maneuver isn’t weakness. It’s keeping a way out when life takes an unexpected turn.

The Crypto market could face a fresh test of volatility this week.

The FOMC rate decision, U.S. PCE inflation and GDP data, Japan’s unemployment rate, and earnings reports from major companies are all landing in the same week.

These developments could shift market expectations for interest rates, the economy, and risk assets. When global markets move together, Crypto is unlikely to stay completely untouched.

In a week packed with data, don’t focus only on catching a market move.

Reduce leverage, manage your positions, and leave room for your judgment to be wrong. Market opportunities will always come around, but if your position gets out of control, you may not be around to catch the next one.

The next contest for AI computing power may not take place solely in data centers.

In the past, the AI industry’s arms race for computing power revolved mainly around high-end GPUs. But as agent applications develop, more and more tasks are becoming fragmented: small-model decision-making, tool calls, real-time inference, API requests, and data processing. Not all of them need H100-class hardware; they may care more about cost, latency, privacy, and verifiability.

Acurast’s idea is to turn smartphones scattered around the world into potential edge-computing nodes.

Users register their devices through the Processor app, while developers can deploy lightweight LLM inference, API calls, scheduled tasks, and data-processing programs. For users, the barrier to participation may be lower than that of traditional, specialized computing deployments; for developers, the goal is to reduce server-management costs.

But the challenge for DePIN has never been just aggregating supply—it’s making that supply generate lasting commercial value.

Acurast is trying to use staking and penalty mechanisms to get nodes to commit a certain amount of computing power. Failure to meet their commitments may result in slashing. It also links protocol rewards to the pool of staked computing power, in the hope of tying token incentives more closely to actual services.

This is more targeted than simply rewarding devices for being online, but it still doesn’t guarantee that the economic model will work. No matter how many nodes there are, if developers don’t have a steady demand for tasks, the network could end up with excess supply.

So Acurast’s central question isn’t “Can phones run AI?” It’s whether the agent era will generate enough real workloads suited to processing on distributed edge devices.

AI is driving a broad increase in demand for computing power, but which computing network can ultimately make money will come down to cost, performance, and genuine paying demand.

The UTXO model Satoshi Nakamoto designed for Bitcoin may be one of the crypto industry’s most easily overlooked foundational innovations.

Traditional account models typically maintain account balances directly, while Bitcoin takes a different approach: it records each transaction output that has not yet been spent.

The balance shown in a wallet is essentially the sum of the relevant unspent outputs. Each output has a specific amount and spending conditions. When you initiate a transaction, the system references previous outputs as inputs and creates new outputs.

This mechanism also helps Bitcoin prevent double spending. Once an output has been spent in a transaction, it cannot be reused in another. Nodes verify whether outputs remain unspent and use consensus rules to determine which transactions are valid.

Projects such as Litecoin, Dogecoin, and Zcash also use UTXO or related unspent-output transaction models.

The value of this design isn’t that it makes transfers look more exciting, but that it establishes clear foundational rules for transaction validation and the movement of value.

The crypto industry often discusses prices, narratives, and ecosystems, but the networks are ultimately powered by technical choices like these, which tend to attract less attention.

Prices shape how the market views a blockchain; its underlying mechanisms determine how it processes every transaction.

Why has Grok bot been gaining popularity lately, while OpenClaw (Lobster) seems to be getting less attention?

I think competition among AI agents is entering a new phase: compared with how powerful their features are, how easily ordinary users can get started may matter more.

First, there’s the learning curve. OpenClaw is very appealing to tech enthusiasts, but installing, configuring, and maintaining it isn’t easy for ordinary users. Many people are blocked by the barriers to entry before they even get to experience the value of an agent.

Second, there’s engineering maturity. An open-source project developed by an individual can quickly gain traction through a great idea, but serving a large number of users also requires reliability, security, ease of use, and ongoing maintenance. Being able to run something and being able to rely on it over the long term are two different things. Of course, open-source projects can also achieve excellent engineering quality. What matters is the maturity of the specific product, not who developed it.

Third, there’s the trade-off between local and cloud-based options. Deploying locally gives users more control, but it also means costs in terms of hardware, configuration, and maintenance. Cloud services eliminate a lot of the hassle—you can just open them and get started. For many ordinary users, a simple, seamless experience matters more than understanding the underlying technology.

Taken together, these three points reveal a trend: for the next generation of agents to reach more users, they’ll need to hide the complexity and let users get results directly.

However, it’s too early to conclude that Grok bot will replace OpenClaw. The two may serve different user groups, and cloud convenience and local control aren’t necessarily an either-or choice.

An agent that can truly reach the mainstream shouldn’t require every user to become a semi-engineer first.

The strangest thing about the AI wave is that it can bring both euphoria and frustration at the same time.

When a new tool first appears, immersing yourself in it can make you feel suddenly more capable. Things that used to be too much for one person to handle can now be done in minutes, as if an entire team had appeared out of nowhere.

But after using it for a while, you start to see the other side: skills you spent years honing can become basic features of a tool with a single model update. Abilities that once took repeated practice to master are now reduced to a text box.

Tools keep getting more powerful, and people can do more and more. Yet the very skills we once relied on to build our confidence may gradually lose their rarity.

And so our feelings about technological progress begin to swing: on one hand, we’re thrilled to see the limits of what we can do being pushed back; on the other, we worry that tools are redefining who we are.

Perhaps that’s exactly why, in this era more than ever, we need to make a conscious effort to step away from the screen.

Sleep well. Eat well. Go for a walk outside. Read books that take time to digest. Sit quietly and feel the breeze for a while.

These things won’t become obsolete with a model upgrade, and they don’t need to prove their worth through efficiency.

AI can help make the world move faster, but we don’t have to turn ourselves into machines constantly striving for an upgrade.

Technology is responsible for speeding things up; life is what lets us feel the passage of time.

Sentiment in the crypto market has become extremely bearish, but this environment could also create the conditions for a major short squeeze.

As more and more people bet on prices falling, short trades can become crowded. Once prices start to rebound, some short sellers may hit their stop-losses or buy back their positions to close them, creating additional buying pressure. If prices continue to rise, more short sellers may be forced to exit, triggering a chain reaction.

That’s how a short squeeze works: rising prices prompt short sellers to buy back their positions, and that buying pushes prices even higher.

However, extreme pessimism doesn’t mean the market has bottomed. Prices could keep falling, and short sellers may not give up right away.

Rather than betting directly on a reversal, it’s more sensible to wait for price signals: see whether the rebound can last, whether key levels can be broken, and whether short covering starts to amplify the move.

When the market is overwhelmingly bearish, don’t forget that a rally can also become the risk in the most crowded trade.

At Wednesday’s Federal Reserve meeting, markets may face a dilemma: whatever stance the Fed takes, it could trigger another bout of volatility.

CPI and PPI came in above expectations, and core inflation is also rising. The US-Iran conflict has driven up energy prices, putting further pressure on inflation. Meanwhile, the housing market is under strain, borrowing costs remain high, businesses are laying off workers, and signs of a weakening economy are mounting.

The Fed now faces two conflicting challenges:

If it is too lenient on inflation, investors may worry that prices are getting out of control, pushing bond yields higher. If it turns hawkish, markets will have to reprice for higher interest rates and tighter financial conditions, potentially weighing on stocks and crypto. If it signals rate cuts, some will question why it is easing policy when inflation is still rising.

Rising inflation alongside a weakening economy—that is what makes the risk of stagflation so troubling.

So, dovish should not simply be equated with bullish, nor hawkish with bearish. What markets really need is a credible policy path—one that can cool inflation while minimizing the economic cost.

If the Fed cannot balance these goals for long, asset prices could remain highly volatile.

Calling Bitcoin “digital gold” doesn’t mean it’s already as stable as gold.

The more important significance of this comparison is what it says about its role as an asset: an asset that depends on no single company or government is becoming part of more and more people’s portfolio discussions.

Kevin O’Leary has even predicted that cryptocurrencies could become the S&P 500’s 12th sector in the future.

If that day really comes, it would mean Crypto is no longer just a highly volatile trading market. Traditional finance might instead measure it as an independent industry, with its own sector weighting and allocation logic.

Of course, this is still just a prediction. Bitcoin’s volatility risk hasn’t disappeared just because it’s been called “digital gold.”

But Crypto’s long-term story may not just be about how high prices can rise. It may be about whether it can evolve from a tradable asset into an independent option in global asset allocation.

CoinDCX has acknowledged a $16 million theft, and the loss has also brought earlier security issues to light.

In July 2025, the exchange suffered a $44 million hack. Afterward, its automated fund-transfer system malfunctioned, leaving some users’ deposits accumulated in vulnerable wallets that were later decommissioned. The latest theft was linked to these wallets.

The two incidents have resulted in combined losses of nearly $60 million.

The way the incident was disclosed has further undermined trust: just days earlier, the CEO had described claims of a breach as a “false alarm,” but the exchange later confirmed a theft. CoinDCX says it will absorb the latest loss and that user funds remain safe.

But a promise to cover the loss is no substitute for an explanation of the security measures. Users need to know whether the affected wallets have been fully isolated, whether the automated transfer system has been fixed, and whether there are any other similar exposures.

On-chain investigator ZachXBT has urged users to withdraw their assets. That does not mean all user funds have been confirmed to be at risk, but the incident is another reminder that keeping assets on an exchange means accepting the risks of platform custody and operations.

What can truly restore trust is not just a promise to absorb the loss, but a clear account of what went wrong, completed remediation, and a way for users to verify the fixes.

Since 2011, Bitcoin has delivered an annualized return of about 120%, compared with around 6% for gold over the same period.

Even after gold’s sharp rally last year, the long-term return gap remains substantial.

One investment approach based on these figures is to increase your Bitcoin allocation when BTC is undervalued relative to gold, then gradually rotate some funds into gold once gold has completed its correction.

The core of this strategy is to adjust your positions based on the relative performance of the two assets, rather than betting on one of them indefinitely.

However, strong historical returns do not mean an asset will continue to outperform, and the view that BTC is undervalued needs to be based on clear criteria. No one can accurately predict when gold’s correction will end, either.

If you plan to shift from BTC to gold over the next 1–2 years, you need to consider the conditions for rotating your holdings in advance, rather than simply setting a date.

For example, you could establish rules for making gradual adjustments based on BTC’s relative price versus gold, changes in trends, your overall portfolio allocation, and your own risk tolerance. This can help you avoid staking the entire strategy on a turning point that is difficult to predict.

Investing is not just about choosing the right assets; it is also about deciding when to adjust your allocation as relative values change.

Bitcoin’s “U.S. midterm election curse” has appeared four times in a row.

In 2010, BTC fell 72%.
In 2014, BTC fell 65%.
In 2018, BTC fell 52%.
In 2022, BTC fell 27%.

Four cycles, four significant declines.

But lining up these numbers reveals another detail: the losses gradually narrowed, from 72% to 27%. The direction didn’t change, but the pullbacks became less severe.

So, will Bitcoin fall again in 2026?

These four historical episodes are certainly worth studying, but they aren’t enough to prove a consistent causal link between midterm elections and declines in BTC. Interest rates, liquidity, the macroeconomy, capital allocation, and the crypto market’s own cycles can all affect Bitcoin’s price.

The fact that the losses have gradually narrowed doesn’t mean the next one will necessarily be smaller. The sample is limited, so we can’t treat past changes as a rule for the future.

Focusing only on four consecutive declines can easily fuel fear; focusing only on the narrowing losses can lead us to underestimate the risks.

Historical trends can help us identify risks, but they can’t guarantee that the future will repeat the past. Rather than believing in an election curse, it’s better to have a sound trading plan.

A report claims that, apart from the Mag 7 (the seven tech giants), AI has yet to deliver any financial returns to other U.S. companies.

Its evidence: companies outside the Mag 7 have seen no significant growth in profit margins or revenue per employee.

But there’s a key problem with this conclusion: a lack of growth in aggregate data doesn’t mean AI hasn’t created value.

A more reasonable way to measure AI’s contribution is to compare companies that use AI to different degrees: how do companies that use more AI differ from those that use less in productivity, costs, and profits?

Looking only at the overall average across all companies makes it difficult to distinguish AI’s actual contribution. Factors such as interest rates, energy costs, wages, and changes in demand can all affect the final results.

The way the companies are grouped is also debatable. The Mag 7 aren’t just AI users; they’re also major suppliers of cloud services, chips, models, and AI infrastructure. Many other companies, by contrast, are primarily buyers of AI. The two groups already have different business models and revenue structures, so directly comparing their profit margins makes it difficult to isolate AI’s effects.

The original article also mentions the economic pressures at the time: rising interest rates, oil prices reaching $116 a barrel, high inflation, and consumer confidence at a historic low.

If companies can still maintain their profit margins in such an environment, it’s worth investigating whether AI helped them offset some of the cost pressures. But that alone doesn’t prove AI has already delivered returns; more detailed data is needed.

So the issue isn’t that we should assume AI must be effective. It’s that we shouldn’t use an aggregate metric that can’t identify cause and effect to declare that AI has no financial value.

Aggregate data can tell us what happened, but not necessarily why. To measure AI’s contribution, we need to separate it from other influencing factors.

In the future, you might have an AI agent dedicated to negotiating better prices for you.

Your broadband bill goes up? It negotiates. Your mobile plan is too expensive? It compares options. A subscription is due for renewal? It first checks whether there’s a cheaper alternative.

Many people have been in this situation: you sign up at a great price, then after a while your provider quietly raises it. You can’t be bothered with the hassle, so you end up accepting the increase.

But if you say you’re thinking of switching, they might suddenly offer you a discount they never mentioned before.

That shows the price on your bill isn’t necessarily the lowest price you could negotiate. Many companies can maintain their current prices because consumers don’t have the time to compare offers and negotiate over and over.

In the past, big companies had dedicated procurement teams, while ordinary people had to research plans, call customer service, and haggle over prices on their own. Many felt it wasn’t worth spending half a day just to save a little money.

AI agents could change that equation.

They can continuously compare competitors, look for alternatives, and contact businesses to negotiate better terms on your behalf—with your permission. The kind of in-depth negotiation that once required the resources of a company could become an everyday tool for every consumer.

If consumers can easily compare prices and negotiate, companies will find it much harder to treat shelf prices as the default transaction price. Profit margins that rely in part on information gaps and consumer inertia will also come under pressure.

Of course, companies will use AI to optimize pricing too, and agents won’t be able to guarantee the lowest price every time.

But the key change isn’t just about saving money. It’s about bargaining power beginning to shift toward consumers.

When AI negotiates on everyone’s behalf, consumers are no longer just the ones passively accepting an offer.

AI hasn’t yet reached the most frenzied stage of the dot-com bubble.

ARK Invest’s view is that AI infrastructure may still be underallocated. Even though debt tied to computing capacity and chips offers high yields, investors are still asking: where is the end demand?

Imagine a fixed-income team where someone spots attractive yields on computing-capacity and chip debt and wants to increase investment, but first has to persuade the organization to allocate more capital.

Management pushes back: Who will keep buying all this computing capacity? Can future demand justify the investment being made today?

This creates a paradoxical situation: the investment opportunity looks promising, but capital hasn’t fully followed.

During the dot-com bubble, the market eventually entered an almost frenzied state, in which many companies’ valuations were no longer tightly constrained by fundamentals. The AI cycle could reach a similar stage in the future, but clearly hasn’t yet.

Moving from skepticism to euphoria takes time. Investors’ views, internal resource allocation at institutions, and the market’s confidence in end demand cannot all change overnight.

This time lag could leave room for excess returns in AI infrastructure.

But high yields don’t mean guaranteed profits; they may also reflect credit risk and uncertainty about demand.

The AI story isn’t just about models and chips. It’s also about when capital truly believes that demand will materialize.