Ether (ETH) led crypto majors into Monday as bitcoin held above $63,000, steadying after a week that pulled it off its lows and back to its highest in more than a month.

Bitcoin traded around $63,207, little changed on the day but up 5.5% over seven days, per CoinDesk data. Ether was the stronger performer over the week, up 12.4% to about $1,777, while BNB and dogecoin each gained around 5.5%. Solana held near $80.77 with an 11.2% weekly rise and Hyperliquid’s HYPE led the majors, up 14.6% on the week. XRP traded at $1.14, up 9.4% over seven days.

The gains held even as the backdrop turned cautious. A rebound in semiconductor and technology shares lost steam, reviving doubts about how durable this year’s AI-driven rally is. South Korea’s Kospi fell 1.4% as Samsung Electronics and SK Hynix declined, and an MSCI gauge of Asian chipmakers slipped.

Brent crude fell 0.6% to about $71.70 a barrel, easing some inflation pressure ahead of the U.S. price data due later this month.

The dollar strengthened against all its major peers, a headwind for crypto that has tracked the currency’s moves through the past quarter.

European regulators and central bankers are warning that the financial risks posed by “agentic” and increasingly autonomous artificial intelligence may be arriving faster than legal and supervisory frameworks can adapt. Their message is less about whether AI will be used in finance, and more about how it could behave under stress—when liquidity thins, volatility rises, and systems that rely on machine decision-making are pushed beyond normal operating conditions.

In recent remarks across Europe, officials raised concerns ranging from market-wide instability caused by faulty AI-driven trading to a widening gap between rapid AI development and the slower cadence of rulemaking. They also linked the issue to broader financial stability risks, including leverage and potential “boom-bust” dynamics in AI-linked asset markets.

Key takeaways

• Central bankers warn rulemaking may lag agentic AI: officials said traditional regulatory cycles struggle when AI technologies shift in weeks or months.
• Volatility could be amplified during stress: Bank of England deputy governor Sarah Breeden suggested AI could worsen market disruptions unless guardrails exist.
• Security and defense funding remain a weak point: ECB president Christine Lagarde argued cybersecurity risks are becoming more severe as models accelerate.
• AI leverage and refinancing risks are on regulators’ radar: warnings from the BIS and IMF pointed to debt-asset maturity mismatches and disruptive feedback loops.

Why European officials think agentic AI changes the risk equation

Bank of England deputy governor Sarah Breeden is among the central bankers arguing that agentic AI—systems that can act with a degree of autonomy—could intensify instability during periods of market stress. Speaking at the European Central Bank’s annual meeting in Sintra, Portugal, on Tuesday, Breeden raised the question of whether regulators should implement guardrails that function like “circuit breakers” or “kill switches,” designed to limit or stop market-wide trading if faulty AI models trigger a meltdown. In her remarks, she emphasized the potential for automation to turn localized errors into systemic disruptions.

The concern is not merely that AI could make trading decisions faster, but that it could create correlated behavior across market participants. When multiple systems respond similarly to the same signals—particularly under stress—small model failures could cascade into larger price swings.

Breeden also tied the issue to the competitive landscape for AI development. She noted that US companies lead in AI investment and frontier model development, while Europe’s financial system offers fewer capital channels into AI than US equity markets. She warned that regulating “too cautiously” could widen this gap further if AI firms seek out jurisdictions with less burdensome compliance requirements.

Regulation cycles can’t keep up, watchdogs say

Other regulators echoed Breeden’s core point: the speed of AI innovation makes conventional policymaking approaches ill-suited. Nikhil Rathi, CEO of the UK’s Financial Conduct Authority, told CNBC’s Squawk Box on Thursday that traditional regulation cycles do not work when the technology evolves rapidly. As he put it, some AI-related technologies move in weeks or months, which means a “traditional cycle of rulemaking simply doesn’t work.” He argued that regulators need “new tools” and a more collaborative way of working with markets.

This view is consistent with the broader European stance that governance frameworks must be designed to handle iterative updates and rapid deployment. In practice, that suggests supervisors may need to focus not only on static compliance at launch, but also on how models are monitored and controlled as they change over time.

At the same time, central bankers have repeatedly linked the discussion to other parts of the financial system, including cyber risk and market integrity. Those overlap points matter to crypto markets as well, since many on-chain and off-chain infrastructures rely on automated processes, and crypto trading systems can react quickly to market signals.

Cybersecurity and “defense” gaps are worsening with model acceleration

Christine Lagarde, president of the European Central Bank, warned in an interview with Les Echos on Thursday that AI technology poses a “major risk.” She contrasted today’s environment with the past decade, when regulators focused on cybersecurity threats such as hacking, data theft, and other forms of compromise.

Lagarde said the acceleration and deeper capabilities of AI models create a “much more serious risk,” partly because events can unfold quickly and partly because effective defense mechanisms and the funding needed to build them have not yet been fully found. Her remarks reframed cybersecurity from a slow-moving threat landscape into a faster feedback environment where response times and resourcing become critical.

From an investor and operator standpoint, that framing implies that AI-related risk management may need to cover not only model accuracy, but also the systems surrounding them—access controls, incident response capabilities, monitoring, and the ability to contain harms when something goes wrong.

Boom-bust warnings: leverage, asset pricing, and macro feedback loops

Separate from concerns about trading autonomy, European and global institutions have also flagged the possibility that AI-linked market activity could create financial stability vulnerabilities. On June 28, the Bank for International Settlements warned that “AI exuberance” could lead to major financial consequences.

The BIS noted that if central banks tighten policy to help contain inflation, it could trigger a sharp pullback in AI-related asset prices after a prolonged period of risk-taking. It warned that this could generate “disruptive macro-financial feedback loops.” In other words, asset price declines could impact broader financial conditions in a way that feeds back into the real economy, tightening conditions further and potentially worsening the initial downturn.

Breeden added a related observation in her Sintra remarks: debt financing has been rising rapidly, and she judged that the financial stability consequences of any fall in AI-related asset prices could increase. That emphasis on leverage suggests regulators are concerned not just with valuations, but with how funding structures could transmit shocks.

The IMF also contributed to the discussion. Tobias Adrian, Director of the IMF’s Monetary and Capital Markets Department, said in an interview with Bloomberg on June 30 that there is a “potential maturity mismatch” between the duration of physical assets and the duration of debt. The risk here is that companies or sectors may face refinancing pressure at the same time cash flows deteriorate—creating a pressure point that can amplify stress.

What comes next for markets watching AI risk controls

The immediate takeaway for market participants is that supervisors may increasingly push for risk management expectations that reflect fast-moving AI deployment—covering both operational safeguards (including cybersecurity and “circuit breaker”-style controls) and financial stability concerns tied to leverage. Investors should watch whether regulators move toward more dynamic oversight frameworks for AI-driven systems and whether AI-related financing conditions change as monetary policy expectations evolve.

TLDR:

• Bitcoin price reclaimed the $63,500 area after volatile trade, keeping the short-term structure constructive while $65,700 stays the next upside test.
• Spot Bitcoin ETFs pulled in fresh demand after a long outflow streak, giving buyers a stronger institutional signal after June’s weakness.
• Weak U.S. labor data cooled rate-hike fears, helping BTCUSD as Treasury yields eased and traders moved back into selected risk assets.
• A break below $63,500 could shift attention toward $61,000, while sustained support may force more short-covering near resistance.

Bitcoin price traded near $63,173 on Monday after a volatile session around the reclaimed $63,500 area. BTCUSD moved between $62,468 and $63,874, showing fast movement around a key support zone. 

The move followed weaker U.S. labor data, renewed spot Bitcoin ETF inflows, and short liquidations near $62,000. Traders are now watching whether Bitcoin can hold $63,500 and retest $65,700, where the last major rejection developed.

Bitcoin Price Holds Key Support After ETF Inflows Return

Bitcoin price action improved after U.S.-listed spot Bitcoin ETFs posted $221.7 million in net inflows. The daily intake ended a 10-day outflow streak and marked the strongest inflow in about two months. That shift mattered as June had damaged sentiment across institutional crypto products.

The inflow also arrived as Bitcoin reclaimed the $63,500 zone. Analyst That Martini Guy says the first rejection at that level looked normal. He added that prior resistance rarely breaks on the first attempt.

The technical setup now depends on whether buyers defend the area. Holding $63,500 keeps the short-term structure constructive. A clean push above it could put $65,700 back in focus.

A loss of $63,500 would weaken the rebound. The next downside area sits near $61,000, based on the analyst’s chart view. That level would show whether recent buying was durable or only a relief move.

Spot demand and derivatives flows also shaped the rally. Short sellers were exposed after Bitcoin moved above $62,000. Forced buybacks then added speed to the recovery and lifted BTC through crowded intraday levels.

The setup is still fragile. Bitcoin price has recovered support, but it has not cleared the last rejection zone. Buyers need steady volume and follow-through before the move looks more durable.

Fed Minutes And Labor Data Put BTCUSD Traders On Alert

Bitcoin price also gained support from softer U.S. labor data. June nonfarm payrolls rose by only 57,000, below expectations for 110,000. May job gains were revised lower, while the unemployment rate fell to 4.2% as labor force participation dropped.

That report lowered fears of a near-term Federal Reserve rate hike. Treasury yields eased, the dollar softened, and risk appetite improved. Lower yields often help non-yielding assets, including Bitcoin and gold.

This week brings more macro risk for BTCUSD traders. The Federal Reserve will release minutes from its June meeting on Wednesday. The minutes could show how officials judged inflation risks under new Chair Kevin Warsh.

Investors will also monitor services PMI, ADP employment data, and jobless claims. These numbers may shape rate expectations before earnings season starts. A stronger inflation or labor signal could pressure the Bitcoin price again.

For now, traders are weighing two opposing forces. ETF inflows and reclaimed support favor another test higher. Yet June’s heavy outflows, weak liquidity, and regulatory pressure in Europe still limit conviction.

Bitcoin price needs sustained spot demand to extend the recovery. A hold above $63,500 keeps $65,700 in play. Failure there could reopen the $61,000 area as traders reassess leverage and macro risk.

TLDR:

• Polymarket political bets from U.S.-linked wallets reached $571 million in 12 months, topping every other tracked country despite the ban.
• Allium said country-level wallet tags cover only about 6% of political-market wallets, making the data directional rather than exact.
• U.S.-linked wallets favored geopolitical prediction markets, with foreign conflict bets taking a larger share than election contracts.
• American wallets won resolved bets at nearly the same rate as other users, showing bolder positioning rather than a clear trading edge.

Polymarket political bets from U.S.-linked wallets reached about $571 million over the past year, even though the platform cannot legally serve American users. The figure, reported by on-chain analysis firm Allium, placed the United States ahead of every other tracked country. Hong Kong followed with $422 million in political market volume.

The data highlights a sharp gap between official restrictions and actual user behavior. Polymarket blocks U.S. users through IP checks. Yet crypto wallets, stablecoins, and VPN access appear to keep the offshore market open to American traders.

Polymarket Political Bets Expose Weak U.S. Access Controls

Polymarket political bets show how hard geographic blocks are to enforce on crypto rails. A traditional financial platform can reject an account, block a bank payment, or stop a broker connection. Polymarket works differently, as users interact through wallets and stablecoins.

That structure leaves fewer points of control. A VPN can mask a location, while a crypto wallet can settle trades without a bank in the middle. Allium’s tracking looked at wallet behavior instead of IP addresses, so the same VPN that bypasses access controls does not erase on-chain patterns.

The firm added an important limit. It can link only about 6% of political-market wallets to a specific country. That means the $571 million figure should not be treated as a precise total. Still, the scale points to strong U.S. demand for offshore prediction markets.

The finding also raises a harder regulatory question. Polymarket’s ban may satisfy a legal access rule, yet it does not fully stop U.S.-linked wallets from trading. Instead, activity moves to a venue outside direct U.S. oversight.

Geopolitical Markets Pull More U.S.-Linked Wallet Activity

The bigger surprise is what American wallets traded. U.S.-linked wallets put 46% of their political volume into geopolitics, compared with 36% across Polymarket overall. Elections accounted for only 16% of U.S. volume, while the full platform average stood near 32%.

That split suggests American traders used Polymarket political bets less for election speculation and more for foreign conflict markets. Iran-related contracts were especially active. Five of the twelve largest U.S. wallet markets involved bets linked to an Iran conflict.

At one stage, American wallets placed 53% of their volume on a U.S. invasion of Iran. The rest of the platform stood at 26% on the same theme. That gap shows higher conviction among U.S.-linked wallets, though not better accuracy.

The largest single U.S.-linked market was more unusual. A contract on whether Ukrainian President Volodymyr Zelenskyy would wear a suit drew $20.8 million in trading volume. That market shows how offshore prediction markets list contracts that regulated U.S. venues often avoid.

Kalshi and compliant U.S. prediction venues focus more on elections, economic data, and rate decisions. Offshore polymarket markets include ceasefires, regime change, and war-related outcomes. That difference appears to pull U.S.-linked wallets toward the markets domestic rules restrict.

Resolved market data did not show a major U.S. betting edge. American wallets backed winners 81.9% of the time, close to 80.3% for other users. The main distinction was not accuracy. It was stronger interest in politically sensitive markets beyond U.S. regulatory reach.

TL;DR

• Coinbase is investigating an AI-generated alert that falsely reported a World Cup result before the match began.
• CEO Brian Armstrong confirmed the company is reviewing the incident after users raised concerns.
• The error has sparked fresh debate over the reliability of AI-generated prediction market content.
• The incident highlights the need for stronger AI safeguards as Coinbase expands its AI-powered financial products.

Coinbase is investigating an AI notification that falsely reported the outcome of a FIFA World Cup match before the game had even begun, prompting criticism over the reliability of artificial intelligence in prediction markets.

The erroneous alert claimed that Norway had defeated Brazil 3-2, with striker Erling Haaland scoring twice. However, Coinbase’s own prediction market page showed the fixture was under a weather delay at the time, meaning no official result existed when the notification was sent.

The incident quickly drew attention across social media, raising fresh questions about the safeguards surrounding AI content on financial platforms.

Brian Armstrong Confirms Internal Review

Coinbase CEO Brian Armstrong acknowledged the issue after users flagged the false notification online, confirming that the company had begun investigating what went wrong.

“Taking a look with the team – thanks for reporting it,” Armstrong said in response to user complaints.

The exchange has not disclosed what caused the incorrect alert or whether it originated from an AI model, an automated data feed, or another internal system. Coinbase also has not indicated whether similar notifications will be paused while the investigation is underway.

The mistake is particularly notable because Coinbase has increasingly incorporated artificial intelligence into its products and operations. The company has also expanded its presence in prediction markets through its partnership with Kalshi, offering users access to event-based markets alongside traditional crypto services.

The false notification has also reignited debate over Coinbase’s positioning of prediction markets as tools that can help surface reliable information. Critics argued that sending an incorrect result before an event had taken place undermines confidence in AI-powered alerts, particularly when they are integrated into financial products.

AI Accuracy Faces Growing Scrutiny

The latest incident adds to the broader conversation surrounding the use of artificial intelligence in customer-facing financial services, where inaccurate information can quickly spread to large numbers of users.

Although Coinbase’s prediction market page correctly displayed that the match had been delayed due to weather conditions, the conflicting push notification created confusion by presenting a fabricated final score as though it were an official outcome.

The company has previously dealt with notification-related issues. Earlier this year, Armstrong addressed a separate bug involving push notifications, noting that Coinbase generally prefers to resolve technical problems without unnecessarily restricting customer access to its services.

So far, Coinbase has not reported any material impact on its business or share price following the incident. Instead, attention has shifted toward how the exchange verifies AI content before it reaches users.

As financial technology firms continue integrating artificial intelligence into trading platforms, prediction markets, and customer communications, maintaining accuracy is becoming increasingly important. The Coinbase incident highlights the challenges of balancing automation with reliability, particularly when AI-generated information has the potential to influence user decisions or public perception.

While the investigation continues, the episode serves as a reminder that AI-powered tools are dangerous and require robust oversight, especially as crypto exchanges expand their use of artificial intelligence across core products and market services.

Nearly one million investors in Donald Trump’s memecoin, Official Trump (TRUMP), are sitting on paper losses totaling about $3.8 billion, according to an analysis published by The New York Times that relies on on-chain analytics from Nansen. The findings underscore a familiar pattern in retail-heavy memecoins: a small group of early wallets capture outsized gains while the broader base carries most of the downside.

The same Nansen analysis also points to losses among buyers of World Liberty Financial’s token (WLFI), a crypto asset tied to a trading platform co-founded by Trump and his three sons. Together, the data arrives amid renewed scrutiny sparked by Trump’s financial disclosures describing substantial crypto-related income.

Key takeaways

• According to Nansen data cited by The New York Times, 988,905 TRUMP wallets (about two-thirds of buyers) were losing money as of the end of June.
• Total TRUMP losses for those wallets were reported at $3.81 billion, while a smaller group of wallets recorded gains totaling about $4 billion.
• Separate Nansen analysis of WLFI found that 85% of the company-tracked wallets held losses, totaling $83 million, with the rest profiting $23 million.
• Trump’s recent financial disclosure described substantial income from his crypto ventures, adding fuel to concerns about conflicts of interest while in office.

TRUMP holders face broad losses, early buyers capture gains

The New York Times reported that as of the end of June, 988,905 TRUMP buyers—roughly two out of every three—were underwater. Across those wallets, losses were estimated at $3.81 billion, with Nansen’s analysis including both holders who were still holding at a loss and those whose positions were already marked negative by the time of measurement.

By contrast, just under half a million wallets recorded profits. In total, the reporting stated that profitable wallets accounted for about $4 billion in gains. Nansen characterized the distribution as skewed, describing it as “a small number of early buyers capturing enormous gains while the broad retail majority absorbed the losses.”

The contrast matters for how investors interpret memecoin risk. These tokens often start with hype and easy participation, but the on-chain outcome can be highly uneven—especially when early buyers benefit from momentum and late entrants become liquidity providers to the exit of better-positioned traders.

In terms of performance since launch, the token’s peak is reported at more than $73 shortly after it debuted. Since then, it has fallen by over 97%, and CoinGecko data in the report placed TRUMP at about $1.70 at the time of publication.

Financial disclosure raises new questions around crypto involvement

The TRUMP loss analysis arrives days after Trump’s annual financial disclosure, released earlier in the week, drew attention for crypto-related earnings. The filing reportedly showed that Trump earned more than $1.4 billion in income from crypto ventures last year, renewing debate around how personal financial interests intersect with public duties.

The disclosure, described as nearly 1,000 pages, also reportedly indicated Trump made over $630 million on the TRUMP memecoin. Meanwhile, the filing suggested that all token buyers combined made a net profit of around $200 million—an aggregate that contrasts sharply with the large number of losing wallets highlighted by Nansen.

This mismatch points to a crucial detail investors may overlook: even if the broader market settles slightly positive in aggregate, individual outcomes can still be heavily negative for most participants. Large early wins can dominate totals even when most holders end up losing money.

Trump launched the memecoin in January 2025, shortly before returning to office. The New York Times coverage and the Nansen-based approach place particular emphasis on buyer-level outcomes as of the end of June, offering a more granular view than price-only narratives.

WLFI token analysis suggests similar imbalance for retail buyers

Nansen’s work also extended to World Liberty Financial (WLFI), a token connected to the crypto trading platform of the same name. The platform’s co-founders include Trump and his three sons, according to the reporting in the article.

The token was reportedly sold directly to investors in two early rounds: first at 1.5 cents, and later at 5 cents. The analysis cited by The New York Times suggested that buyers who entered at 5 cents likely made a small profit overall. But among the nearly 27,000 wallets Nansen tracked, 85% recorded losses totaling $83 million, while the remaining wallets profited $23 million.

The article also cautioned that losses may be understated. Nansen reportedly noted that additional buyers likely purchased WLFI on exchanges where the relevant data is not public, meaning some losing positions may not be captured in the wallet set the firm analyzed. It was also noted that WLFI later became available to the public via secondary exchanges in September.

From a business-flow perspective, the reporting stated that Trump’s financial disclosure indicated he earned just under $800 million from the World Liberty Financial platform last year. The same section of the article said the Trump-linked business received 75% of WLFI sales regardless of the token’s price—an arrangement that could matter when assessing who captures value as token pricing fluctuates.

Scrutiny continues as Trump addresses conflict-of-interest concerns

In an interview with CNBC reported by Cointelegraph, Trump was described as dodging questions about perceived conflicts of interest related to his crypto activity and said there was “nothing illegal” and “nothing wrong” with the disclosed profits, adding that others were responsible for the investments.

For market participants, the emerging theme across these different threads—wallet-level loss concentration for both TRUMP and WLFI, and the scale of crypto-related income described in financial disclosures—is that retail participation may be riskier than price charts alone suggest. The on-chain outcomes reported by Nansen point to an uneven value transfer, where early access and execution timing can outweigh later entry enthusiasm.

Investors should watch whether additional on-chain breakdowns expand beyond the tracked wallet sets (especially for WLFI), and whether future disclosures provide clearer detail on how token-linked revenues and allocations are structured. As long as memecoins and tokenized platform interests remain central to attention, the key question is how consistently late participants are protected—or whether, as the Nansen analysis implies, they continue to pay most of the cost of early wins.

European regulators and central bankers have warned that rulemaking cannot keep pace with rapid advances in agentic artificial intelligence and have called for guardrails to protect the financial system. 

Bank of England deputy governor Sarah Breeden is one of several central bankers who have said that agentic AI could amplify volatility during bouts of market stress.

Breeden questioned if guardrails are needed, “analogous to circuit breakers or kill switches that would limit or stop trading market-wide if faulty AI models cause market meltdown,” she said at the European Central Bank’s annual meeting in Sintra, Portugal, on Tuesday.

US companies are leading in AI investment and frontier model development, and Europe’s financial system gives it fewer capital channels into AI compared to the US equity markets. Regulating too cautiously could widen that gap further, as AI companies may seek out jurisdictions with lower compliance requirements.

Cybersecurity and financial risk warnings 

European Central Bank President Christine Lagarde, in an interview with French outlet Les Echos on Thursday, warned that AI technology poses a “major risk.” 

“For about a decade now, we have been talking about cybersecurity risks, hacking, data theft, and so on,” Lagarde said. “But with the acceleration and deepening of AI models, we are confronted with a much more serious risk, because it is happening very, very quickly, and because the means of defense — and the funding required for them — have yet to be found.”

Related: Anthropic to bring back Fable 5 as US lifts export controls

Meanwhile, Nikhil Rathi, CEO of the UK’s Financial Conduct Authority, told CNBC’s Squawk Box on Thursday that traditional regulation cycles don’t work in an era of fast-moving AI development.

“Technology moves incredibly fast, and we need to think differently about some of the innovations that we are seeing on AI,” Rathi said.

“The reality is some of these technologies now move in weeks or months, and the traditional cycle of rulemaking simply doesn’t work in that way, so we need to think about new tools and a different way of working with the market in a more collaborative way.” 

Central bankers, especially in Europe, have raised the same red flags about crypto, claiming that it could disrupt the traditional financial system. 

Bankers warn of AI boom-bust risk

The Bank for International Settlements warned on June 28 that AI “exuberance” could have major financial consequences.

If central banks tighten policy to contain inflation, this could precipitate a “sharp pullback in [AI] asset prices after a prolonged period of exuberant risk-taking,” which could trigger “disruptive macro-financial feedback loops,” the BIS said. 

Breeden said that debt financing was rising rapidly. “We therefore judged that the financial stability consequences of any fall in AI-related asset prices could well increase,” she said. 

Meanwhile, Tobias Adrian, Director of the IMF’s Monetary and Capital Markets Department, said in an interview with Bloomberg on June 30 that there is a “potential maturity mismatch in between the duration of the physical assets and the duration of the debt.”

Magazine: AI is banking the unbanked in Africa… faster than crypto

Nearly 1 million buyers of Official Trump (TRUMP), US President Donald Trump’s memecoin, have lost a collective $3.8 billion on the token as of the end of June, newly reported analysis shows.

As of the end of last month, 988,905 TRUMP buyers, or around two out of every three buyers, have lost money on the memecoin, The New York Times reported on Saturday, citing a report by analytics firm Nansen.

Those buyers lost a total of $3.81 billion, which includes those who have held on to the token at a loss.

The report comes days after Trump’s annual financial disclosure, released on Tuesday, revealed he earned more than $1.4 billion in income from his crypto-related ventures last year, raising fresh concerns about the president’s crypto dealings while in office.

Just under half a million wallets had reportedly recorded a profit on the token, totaling $4 billion, which Nansen said reflected “a small number of early buyers capturing enormous gains while the broad retail majority absorbed the losses.”

Donald Trump posted to X on Jan. 18 about his new self-branded memecoin. Source: Donald Trump

The nearly 1,000-page disclosure showed Trump had made over $630 million on his TRUMP memecoin, while all buyers of the token taken together made a net profit of around $200 million in comparison.

Trump launched the memecoin just days before he re-entered office in January 2025, where the token quickly peaked at over $73. It has since fallen by over 97% and currently trades at $1.70, according to CoinGecko.

World Liberty Financial token buyers also likely lost out

Nansen also analyzed World Liberty Financial (WLFI), the token tied to the crypto trading platform of the same name that lists Trump and his three sons as co-founders.

WLFI was first sold directly to investors at 1.5 cents, then at 5 cents. Nansen said those who bought WLFI at 5 cents have likely made a small profit, but of the nearly 27,000 wallets the company tracked, 85% had recorded a loss, which amounted to $83 million in total, while the remaining wallets profited a total of $23 million.

Nansen said there are likely more investors who have lost on the token, as other buyers purchased WLFI on exchanges where the data is not public. The token was made available to the public via secondary exchanges in September.

Related: Senate Dems urge probe into $500M crypto deal between Trumps, UAE

Meanwhile, Trump’s financial disclosure showed that he earned just under $800 million from the World Liberty Financial platform last year, as a business tied to Trump collects 75% of the sales of WLFI regardless of its price.

In an interview with CNBC on Thursday, Trump dodged questions about perceived conflicts of interest and said there was “nothing illegal” and “nothing wrong” with his disclosed crypto profits and claimed that other people were responsible for his investments.

Magazine: Trump’s crypto ventures raise conflict of interest, insider trading questions