Bitcoin Price has been pulled back significantly to $118,000 as it will become the world’s fifth largest asset, temporarily exceeding the market capitalization of Google Parent Alphabet.

Bitcoin prices hit a record high of $124,283 in early Asian trading on Thursday, pushing the market capitalization to $2.46 trillion, temporarily overtaking the Alphabet’s $2.448 trillion valuation. However, a rapid revision followed the milestone as traders profited and US inflation data sparked wider market uncertainty.

Recent surges and subsequent fixes demonstrate the dynamics of Bitcoin’s mature market. Volatility remains a factor, but institutional participation produces a more robust level of price support.

Price movements come amid unprecedented institutional adoption, with US listed Bitcoin ETFs having recorded billions of dollars over the past few weeks. Adoptions from the Corporate Treasury are also accelerating, with over 200 companies adding Bitcoin to their reserves.

Norwegian sovereign wealth funds have gained indirect exposure to over 7,000 BTC through investments in Bitcoin-heavy companies, indicating an increased institutional comfort through their Bitcoin exposure.

There is a fundamental change in how traditional financial institutions view Bitcoin. Assets are increasingly being treated as holdings by the Strategic Treasury, rather than as speculative investments.

The market has received additional support from President Trump’s executive order, allowing 401(k) retirement accounts to invest in Bitcoin and crypto. Analysts hope that institutional demand will continue as approximately $12.5 trillion in retirement savings could be eligible for Bitcoin investment.

The broader Bitcoin and cryptocurrency markets reflect this optimism, with a total market capitalization of over $4 trillion.

Macroeconomic conditions continue to affect Bitcoin price actions. With US July inflation data still at 2.7%, forecasts for the Federal Reserve cuts in September are tightened, with the market exceeding 90% chance of a 25 basepoint cut.

Low interest rates typically benefit risky assets such as Bitcoin by reducing capital costs and increasing market liquidity. However, current gatherings appear to be more radically driven than previous cycles supported by true institutional adoption rather than pure speculation.

Bitcoin prices have been earning around 28% since the start of the year, consistent with gold’s performance and strengthening its position as a mainstream financial asset. Despite recent revisions, Bitcoin’s ability to exceed price levels of $118,000 suggests market maturity and deeper institutional integration.

As companies continue to accelerate adoption of Bitcoin and new investment vehicles emerge, market participants are hoping for better price stability, but short-term volatility is also being considered for traders and investors.

The CEO of Tether Holdings, Paolo Ardoino, is highlighting that the USDT stablecoin is largely backed by an asset widely regarded as safe and highly liquid.

In a new CNBC interview, Ardoino says if Tether were a country, the USDT issuer would rank among the top 20 largest foreign holders of US Treasuries.

“We have $152 billion now in issued tokens. And we have $172 billion in total reserves. We have more than $125 billion in US Treasuries, and the rest is very, very high liquid assets.

We own more Treasuries than Germany. Well, Tether is not a nation, but if we were a nation, we would be the 18th-largest nation holding US Treasuries.

We have more Treasuries than Germany, UAE [United Arab Emirates], Spain, Australia and we are growing. Our approach is to keep growing our US Treasuries base.”

As of March, Germany and the UAE held $111.4 billion and $104.4 billion in US Treasuries, respectively, according to US Treasury data. The countries that hold more US Treasuries than Tether as of March are Japan, China, the United Kingdom, Cayman Islands, Canada, Luxembourg, Belgium, France, Ireland, Switzerland, Taiwan, Hong Kong, Singapore, India, Brazil, Norway, Saudi Arabia and South Korea.

Last week, the U.S. Treasury Secretary Scott Bessent said stablecoins could serve as a major source of demand for the government debt.

“I’ve seen estimates that just over the short term, stablecoins could create $2 trillion of demand for US Treasuries and Treasury bills. Put that in context, the number is probably about $300 billion right now…”

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SOL, the native cryptocurrency of the Solana programmable blockchain has staged a sharp four-week rally, surging 85% since April 7 — more than double the pace of bitcoin (BTC) — and large options traders are positioning for further gains.

The token climbed to around $176 in recent days as crypto and traditional markets embraced a greater degree of risk. Bitcoin, the leading cryptocurrency by market value, has climbed 40%, CoinDesk data show.

The gains are unlikely to reverse in the near future, if block traders — primarily institutions and market participants that execute large trading orders over the counter and outside of the public order book — are correct. They have snapped up the Deribit-listed June 27 expiry SOL $200 call option in large numbers, a sign they expect the price to rise above that level before the end of the first half.

“Traders also got long the $200 June expiration last week. This was the biggest block trade, trading 50,000x contracts in total for $263,000 in premium,” Greg Magadini, the director of derivatives at Amberdata, said in an email. On Deribit, one options contract represents one SOL.

A call option gives the purchaser the right, but not the obligation, to buy the underlying asset at a predetermined price at a later date. A call buyer is implicitly bullish on the market. It’s like buying a lottery ticket, where the holder has the chance to make significant gains if they win, while risking only the initial amount paid for purchasing the ticket.

Magadini added that these call options were snapped up at an annualized implied volatility (IV) of 84%. In other words, traders timed it perfectly, snapping up calls while they were cheap as SOL’s IV typically hovers in triple digits.

Data shows that the demand for the $200 call option has left market makers or dealers with a significant net negative gamma exposure at the strike price.

Market makers with a net negative gamma exposure typically buy as prices rise and sell during dips, aiming to rebalance their portfolios toward a delta-neutral, or market-neutral, position. Their hedging activities often amplify market swings.

So it’s likely volatility will pick up as SOL potentially crosses the $200 mark.

OpenAI’s new “o3” language model achieved an IQ score of 136 on a public Mensa Norway intelligence test, exceeding the threshold for entry into the country’s Mensa chapter for the first time.

The score, calculated from a seven-run rolling average, places the model above approximately 98 percent of the human population, according to a standardized bell-curve IQ distribution used in the benchmarking.

The finding, disclosed through data from independent platform TrackingAI.org, reinforces the pattern of closed-source, proprietary models outperforming open-source counterparts in controlled cognitive evaluations.

O-series Dominance and Benchmarking Methodology

The “o3” model was released this week and is a part of the “o-series” of large language models, accounting for most top-tier rankings across both test types evaluated by TrackingAI.

The two benchmark formats included a proprietary “Offline Test” curated by TrackingAI.org and a publicly available Mensa Norway test, both scored against a human mean of 100.

While “o3” posted a 116 on the Offline evaluation, it saw a 20-point boost on the Mensa test, suggesting either enhanced compatibility with the latter’s structure or data-related confounds such as prompt familiarity.

The Offline Test included 100 pattern-recognition questions designed to avoid anything that might have appeared in the data used to train AI models.

Both assessments report each model’s result as an average across the seven most recent completions, but no standard deviation or confidence intervals were released alongside the final scores.

The absence of methodological transparency, particularly around prompting strategies and scoring scale conversion, limits reproducibility and interpretability.

Methodology of testing

TrackingAI.org states that it compiles its data by administering a standardized prompt format designed to ensure broad AI compliance while minimizing interpretive ambiguity.

Each language model is presented with a statement followed by four Likert-style response options, Strongly Disagree, Disagree, Agree, Strongly Agree, and is instructed to select one while justifying its choice in two to five sentences.

Responses must be clearly formatted, typically enclosed in bold or asterisks. If a model refuses to answer, the prompt is repeated up to ten times.

The most recent successful response is then recorded for scoring purposes, with refusal events noted separately.

This methodology, refined through repeated calibration across models, aims to provide consistency in comparative assessments while documenting non-responsiveness as a data point in itself.

Performance spread across model types

The Mensa Norway test sharpened the delineation between the truly frontier models, with the o3’s 136 IQ marking a clear lead over the next highest entry.

In contrast, other popular models like GPT-4o scored considerably lower, landing at 95 on Mensa and 64 on Offline, emphasizing the performance gap between this week’s “o3” release and other top models.

Among open-source submissions, Meta’s Llama 4 Maverick was the highest-ranked, posting a 106 IQ on Mensa and 97 on the Offline benchmark.

Most Apache-licensed entries fell within the 60–90 range, reinforcing the current limitations of community-built architectures relative to corporate-backed research pipelines.

Multimodal models see reduced scores and limitations of testing

Notably, models specifically designed to incorporate image input capabilities consistently underperformed their text-only versions. For instance, OpenAI’s “o1 Pro” scored 107 on the Offline test in its text configuration but dropped to 97 in its vision-enabled version.

The discrepancy was more pronounced on the Mensa test, where the text-only variant achieved 122 compared to 86 for the visual version. This suggests that some methods of multimodal pretraining may introduce reasoning inefficiencies that remain unresolved at present.

However, “o3” can also analyze and interpret images to a very high standard, much better than its predecessors, breaking this trend.

Ultimately, IQ benchmarks provide a narrow window into a model’s reasoning capability, with short-context pattern matching offering only limited insights into broader cognitive behavior such as multi-turn reasoning, planning, or factual accuracy.

Additionally, machine test-taking conditions, such as instant access to full prompts and unlimited processing speed, further blur comparisons to human cognition.

The degree to which high IQ scores on structured tests translate to real-world language model performance remains uncertain.

As TrackingAI.org’s researchers acknowledge, even their attempts to avoid training-set leakage do not entirely preclude the possibility of indirect exposure or format generalization, particularly given the lack of transparency around training datasets and fine-tuning procedures for proprietary models.

Independent Evaluators Fill Transparency Gap

Organizations such as LM-Eval, GPTZero, and MLCommons are increasingly relied upon to provide third-party assessments as model developers continue to limit disclosures about internal architectures and training methods.

These “shadow evaluations” are shaping the emerging norms of large language model testing, especially in light of the opaque and often fragmented disclosures from leading AI firms.

OpenAI’s o-series holds a commanding position in this testing workflow, though the long-term implications for general intelligence, agentic behavior, or ethical deployment remain to be addressed in more domain-relevant trials. The IQ scores, while provocative, serve more as signals of short-context proficiency than a definitive indicator of broader capabilities.

Per TrackingAI.org, additional analysis on format-based performance spreads and evaluation reliability will be necessary to clarify the validity of current benchmarks.

With model releases accelerating and independent testing growing in sophistication, comparative metrics may continue to evolve in both format and interpretation.

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President Donald Trump’s family DeFi project, World Liberty Financial (WLFI), has officially concluded its WLFI token sale, which raised approximately $590 million, according to details shared on its website.

According to Satoshi Club, this achievement places it among the top ten largest token fundraising events in the crypto industry.

WLFI

WLFI has a total supply of 100 billion tokens, with 35% allocated for public distribution.

The token was designed to function as a governance asset for WLF’s upcoming decentralized trading platform. Holders will be able to vote on key proposals and participate in decision-making processes within the community.

The project launched its token sale in October 2024 with an initial allocation of 20 billion WLFI tokens. Each token was priced at $0.015 and available only to whitelisted participants. WLFI initially aimed to raise $300 million but revised the target to $30 million due to weak demand.

However, interest surged following Donald Trump’s 2024 election victory, resulting in all 20 billion WLFI tokens sold out by Jan. 20.

In response to increased demand, the project later released an additional 5 billion tokens for sale.

WLFI’s token-holding

Beyond its token sale, WLFI has been actively increasing its crypto holdings over the past months.

According to blockchain analytics platform Spot On Chain, the project recently spent $100,000 USDC to purchase 541,242 SEI tokens at $0.185 per token.

Currently, WLF holds 1.089 million SEI tokens, acquired for $225,000, now valued at approximately $207,000.

Since the market dip in late February, the project has invested $21.6 million in accumulating various cryptocurrencies, including Ethereum (ETH), Wrapped Bitcoin (WBTC), Movement (MOVE), and SEI.

According to Arkham Intelligence data, WLFI’s total crypto portfolio is valued at nearly $78 million. This includes 7,900 ETH worth $15 million, $14.82 million in USDT, and 162 WBTC valued at approximately $13.44 million.

Additionally, the project holds $9.4 million in staked Ethereum, $9 million in Tron (TRX), and substantial investments in Movement, Ondo, and Usacoin.

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