ZachXBT Identifies Axiom Exchange’s Broox Bauer of Abusing Internal Tools To Access and Share Sensitive User Data

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February 28, 2026

ZachXBT, the prominent blockchain investigator, released a detailed exposé accusing Broox Bauer, a senior business development employee at Axiom Exchange; a Solana-based decentralized trading platform, of abusing internal tools to access and share sensitive user data.

Since early 2025 (over 10 months), Broox allegedly used Axiom’s internal dashboards to look up private user details, such as linked wallet addresses, transaction histories, referral codes, UIDs, and more—far beyond what’s typical for a BD role.

He reportedly shared this information with a small group including other employees and associates who compiled lists; in Google Sheets of prominent traders and KOLs to track and potentially front-run their trades, especially in memecoins.

Monitoring wallets tied to influencers like “Marcell“; who allegedly bundled memecoins privately before shilling them and others, enabling coordinated profitable trades ahead of public promotions. Leaked audio clips from a February 2026 call feature Broox discussing plans to help an associate profit $200K quickly via this access.

Plus claims of tracking users and prior schemes with other Axiom staff like Ryan (Ryucio). ZachXBT identified Broox’s main wallet http://FarpaWkzio7WQVpQeu2eURvNQZ3pCBZupJ95wUjoHcUN , with related addresses showing heavy memecoin activity and flows to CEX deposits—though pinpointing exact insider trades requires Axiom’s internal logs.

Broader issues highlighted: Poor access controls, minimal monitoring, and excessive data visibility even for non-technical roles, raising privacy and potential legal concerns; ZachXBT suggested SDNY jurisdiction given Broox’s NYC base.

Axiom responded that they were “shocked and disappointed,” revoked access to the misused tools, and are conducting an internal investigation while holding parties accountable. Wallets connected to Broox or associates reportedly profited around $400K by betting “Yes” on Polymarket markets predicting Axiom as the target of ZachXBT’s insider trading investigation.

A related Polymarket event “Which crypto company will ZachXBT expose for insider trading?” saw massive volume; tens of millions, with Axiom odds surging to near-certainty before the reveal—fueling suspicions of advance knowledge.

This ties into broader concerns about insider advantages in prediction markets and platform data security in crypto and DeFi. The allegations of abusing internal dashboards to access sensitive user data; wallet addresses, transaction histories, referral codes, UIDs for over 10 months have severely undermined confidence.

Axiom now faces scrutiny over privacy and security failures. Users are likely moving funds elsewhere, as the core promise of decentralized, trustless trading is compromised by off-chain data abuse. Axiom quickly revoked access to the misused tools, expressed being “shocked and disappointed,” and launched an internal investigation while promising accountability.

However, critics note the response feels generic and doesn’t address the scale of exposure or systemic access control issues; why a BD role had such broad visibility with minimal monitoring. Liquidity and trading volume could decline sharply if users flee, directly threatening Axiom’s fee-based model.

ZachXBT urged Axiom’s founders to pursue legal action against involved parties and suggested criminal charges could apply.
The scandal highlights governance gaps in DeFi platforms, potentially inviting increased regulatory attention to data security, access controls, and insider advantages in crypto trading venues.

This creates a meta-layer scandal: questions about insider trading on prediction markets themselves, raising doubts on platforms like Polymarket and Kalshi’s ability to detect and prevent complex insider plays where the market becomes a secondary payout vehicle for leaked info.

Reinforces debates on trust in decentralized platforms—on-chain transparency is undermined by poor off-chain controls. It may deter users from similar terminals and aggregators and fuel calls for better audits and governance. Echoes past scandals, spotlighting risks in employee access, KOL tracking, and memecoin front-running.

It could accelerate demands for stricter internal policies, third-party audits, and privacy-focused features across exchanges. Short-term negative for Axiom-related assets; any associated tokens saw sharp drops in some reports, with ripple effects on Solana ecosystem confidence.

However, it may ultimately strengthen the space by exposing weaknesses early. This is a high-profile case blending data privacy breaches, alleged insider trading, and prediction market exploitation—likely to drive short-term outflows from Axiom.

While prompting longer-term reforms in how crypto platforms handle user data and internal safeguards. The full fallout depends on Axiom’s investigation results, any legal developments, and whether more evidence emerges.

10 Year US Treasury Dips Below 4% in Recent Trading Sessions

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Paul Ugbede Godwin

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February 28, 2026

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The 10-year U.S. Treasury yield has dipped below 4% intraday and in recent trading sessions, marking the first time since late November. Reports confirm this milestone today, with the yield trading around 3.97–3.99% in recent updates—its lowest in about three to four months.

This reflects a strong bond rally, with February marking the best monthly performance for Treasuries in a year, driven by factors like investor demand for safe-haven assets amid global risks, softer economic outlooks, and auction demand. This drop in the 10-year yield (a key benchmark) has directly influenced longer-term borrowing costs, including mortgages.

The average 30-year fixed-rate mortgage has fallen below 6% for the first time since 2022 specifically since September 2022. Freddie Mac’s Primary Mortgage Market Survey, shows the average at 5.98% for the week ending then—down from 6.01% the prior week and significantly lower than 6.76% a year ago.

The New York Times, noting it’s the first sub-6% reading in over three years. This is a psychological and practical milestone for the housing market: It could ease affordability pressures and potentially encourage more buyers and sellers to enter the spring buying season.

However, economists caution it may not spark a full housing boom without increased supply, as home prices remain elevated and other factors like policy uncertainties linger. Note that daily lender-specific rates can vary; some averages show around 6.0–6.04% today, but the key weekly benchmark from Freddie Mac has crossed below 6%.

Treasuries posted their best monthly performance in a year during February. This is largely driven by a “flight to safety” amid uncertainties like trade policy volatility; tariff developments and legal challenges, geopolitical risks, potential economic slowdown signals, and concerns over AI disruption impacting growth stocks.

General Impact on Stocks

Lower Treasury yields typically support equities in several ways: They reduce borrowing costs for companies and consumers, boosting economic activity and corporate profits over time.

They make stocks more attractive relative to fixed-income alternatives; lower “risk-free” rate improves equity valuations, especially for growth-oriented sectors like tech. Falling yields often signal investor caution or expectations of softer growth and Fed easing, which can favor rate-sensitive sectors; real estate, utilities, consumer discretionary.

The mortgage rate drop below 6% could provide a modest tailwind to housing-related stocks and the broader economy by improving affordability, potentially encouraging more home sales and listings in the spring season—though economists note limited boom potential without more housing supply.

However, the relationship isn’t always straightforward. Lower yields can sometimes coincide with risk-off sentiment; fears of recession or disruptive forces like AI reducing corporate earnings growth, pressuring stocks in the short term. On this specific day, the stock market has been under pressure despite the bond rally.

Major indexes opened lower and extended declines, with the Dow Jones Industrial Average dropping significantly (reports of 400–800 points lower at points, or around 1–1.5%). The S&P 500 fell roughly 0.7–1.1% trading around 6,843–6,859 levels. The Nasdaq Composite (tech-heavy) saw sharper losses around 1.4%, weighed down by AI-related fears and weakness in names like Nvidia.

Hotter-than-expected January Producer Price Index (PPI) data, which raised inflation concerns and tempered hopes for aggressive Fed rate cuts. Ongoing “AI scare trade” or fears of disruption to traditional business models and tech valuations. Lingering volatility from trade and tariff uncertainties, which initially fueled the bond rally but also hit equities.

While lower yields and mortgage rates are a supportive factor for stocks in a broader sense potentially aiding a recovery if economic data softens further, today’s market action shows risk aversion dominating—equities sold off as investors rotated into bonds for safety.

February has been choppy and volatile for stocks, with the S&P 500 eyeing a small monthly loss amid these crosscurrents.If you’re investing or tracking this, watch upcoming data and Fed signals, as they could shift the balance between the bond rally’s supportive effects and growth and inflation worries.

Markets remain highly sensitive right now. These moves signal looser financial conditions, with bonds acting as a haven and feeding through to consumer rates. If you’re tracking this for buying, refinancing, or investing, rates remain volatile—check with lenders for personalized quotes.

MTN Nigeria Communications Plc swings to N1.11tn profit, proposes N15 dividend as balance sheet turns positive

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Samuel Nwite

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February 28, 2026

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MTN Nigeria has delivered its strongest financial rebound in years, returning to profitability in 2025 after a historic foreign exchange-driven loss in 2024, and proposing a total dividend of N20 per share for the year.

For the full year ended December 31, 2025, the telecom operator posted a profit after tax of N1.11 trillion, reversing a N400.4 billion loss recorded in 2024. Earnings per share rose to N53.07 from negative N19.05 a year earlier.

The performance was underpinned by robust service revenue growth, expanding margins, and a dramatic turnaround in foreign exchange dynamics. Total revenue climbed 54.9% year-on-year to N5.20 trillion, while service revenue — the company’s core top line metric — rose 55.1% to N5.17 trillion.

In the fourth quarter alone, pre-tax profit surged 248.8% to N569.6 billion from N163.3 billion in Q4 2024, reflecting sustained revenue momentum and improved cost efficiency.

Following the results, the board proposed a final dividend of N15 per share, bringing total dividends for the 2025 financial year to N20 per share. The dividend will be paid electronically to shareholders on the register on April 8, 2026, who have completed e-dividend registration.

Chief Executive Officer Karl Toriola described 2025 as a “significant turning point.”

“We closed the year with positive retained earnings of N400.4 billion (December 2024: negative N607.5 billion) and shareholders’ equity of N548.7 billion (December 2024: negative N458.0 billion),” he said, noting that improved macroeconomic conditions — particularly a more stable foreign exchange market and moderated inflation — helped ease margin pressure.

The company maintained its medium-term service revenue growth target of at least the low 20% range and revised EBITDA margin guidance upward from 53–55% to the mid-to-high 50% range, signaling confidence in operational leverage.

Data and fintech power revenue surge

MTN Nigeria’s recovery was driven primarily by data and fintech growth, reflecting the structural shift in consumer usage patterns.

Data revenue rose 74.5% to N2.78 trillion, becoming the largest contributor to service revenue. The growth was supported by a 34% increase in data traffic, an 11.6% rise in active data users to 53.2 million, and smartphone penetration climbing to 66.1%.

Voice revenue remained resilient, increasing 42.1% to N1.85 trillion, aided by subscriber growth and customer value management initiatives. Mobile subscribers rose 7.9% to 87.3 million.

Fintech revenue expanded 79.7% to N191.3 billion, driven by higher interest income and the expansion of advanced services. Active wallets increased to 3.7 million, reinforcing management’s push to diversify revenue beyond traditional connectivity.

Crucially, cost growth lagged revenue growth. Cost of sales increased 30.3%, well below the 55% rise in service revenue, while operating expenses rose 16.7%, reflecting efficiency gains and savings from tower lease renegotiations. The resulting operating leverage drove EBITDA up 108.9% to N2.74 trillion.

Foreign exchange, which had severely distorted 2024 earnings, became a tailwind. The company reported a net FX gain of N90.3 billion compared to a N925.4 billion loss in 2024, following the settlement of outstanding letters of credit and reduced dollar exposure.

Capital expenditure, excluding leases, rose 126.2% to N1.00 trillion, reflecting a significant investment in network capacity and quality. Even with this aggressive build-out, free cash flow jumped 215.5% to N1.2 trillion, underscoring improved cash generation and working capital discipline.

Balance sheet repair and market re-rating

The balance sheet shows a marked turnaround. Total assets increased 28.7% to N5.40 trillion, while shareholders’ funds rose 219.8% year-on-year to N548.7 billion, reversing the negative equity position recorded in 2024.

Retained earnings returned to positive territory at N400.4 billion, a dramatic shift from the N607.5 billion deficit a year earlier. The restoration of equity strengthens solvency metrics and supports dividend resumption.

Investors have responded decisively. As of the close of trading yesterday, the shares stood at N760, making MTN Nigeria the most capitalized company on the Nigerian Exchange with an approximate market value of N16 trillion.

The stock has gained 33% in February alone, pushing year-to-date returns to 49%, following a 155.5% rally in 2025. The re-rating reflects renewed confidence in earnings stability, reduced FX risk, and sustained growth in high-margin data services.

The 2025 results signal more than a cyclical rebound. They point to a structural strengthening of MTN Nigeria’s operating model — one increasingly anchored on data monetization, fintech expansion, and disciplined cost management — at a time when macroeconomic headwinds have begun to ease.

Applying Tekedia EDIA Framework On Citrini Research’s “The 2028 Global Intelligence Crisis” Paper

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Ndubuisi Ekekwe

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February 27, 2026

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This piece summarizes this research titled THE 2028 GLOBAL INTELLIGENCE CRISIS by Citrini Research with the application of Tekedia EDIA Play in the research’s thesis.

Core Thesis: The article is a forward-looking scenario (not a prediction) arguing that AI success itself could trigger a macroeconomic crisis because abundant machine intelligence may erode the human income base that modern economies depend on.

1. The Paradox of “Abundant Intelligence”

The piece imagines a near future where AI dramatically boosts productivity and corporate profits, yet simultaneously weakens the broader economy. Companies replace large portions of white-collar labor with AI systems that work continuously and at lower cost, causing wage growth to collapse even as output rises.

This creates what the authors call “Ghost GDP”, economic production recorded in statistics but not translating into household income or spending.

2. A Self-Reinforcing Displacement Loop

Firms rationally cut staff to adopt AI, then reinvest savings into more AI, enabling further layoffs—a feedback loop with “no natural brake.” Unlike past technological shifts, displaced workers cannot easily transition to new roles because AI increasingly performs the very cognitive tasks humans would reskill into.

3. Collapse of Friction-Based Business Models

AI agents remove market frictions—price comparison, search costs, switching inertia—that many industries historically monetized. As AI optimizes purchases, negotiates subscriptions, and bypasses intermediaries, sectors built on convenience premiums, commissions, or information asymmetry see margins compress or disappear.

In short, machines do not exhibit brand loyalty, fatigue, or behavioral biases, eliminating advantages companies once exploited.

4. From Sector Disruption to Systemic Risk

Initially viewed as a tech-sector issue, AI disruption spreads because white-collar workers drive a disproportionate share of consumption. When those incomes fall, consumer demand, the backbone of service economies, contracts sharply, pushing the economy into recession despite continued AI investment and infrastructure growth.

5. Financial Contagion Through Overbuilt Tech Financing

Private credit and leveraged bets tied to software and services assumptions begin to fail as AI undermines recurring-revenue models, exposing a chain of correlated financial risks embedded across insurers, asset managers, and credit markets.

The Big Idea

The essay’s warning is structural: If intelligence becomes cheap and scalable, capitalism—designed around scarce human expertise and wage-driven consumption—may face a demand shock rather than a productivity boom. The danger is not that AI underperforms, but that it works too well, faster than economic institutions can adapt.

Mapping the “2028 Global Intelligence Crisis” to the Tekedia EDIA Framework

The conversation around an impending “global intelligence crisis” driven by artificial intelligence is, at its core, not about technology but about market structure. Through the Tekedia EDIA Play lens, what appears to be disruption is actually a misalignment in how firms are executing the four strategic plays of markets—Efficiency, Differentiation, Innovation, and Aggregation.

Markets remain stable only when these plays evolve in balance. When one accelerates ahead of the others, value creation detaches from value distribution, and the economic system begins to strain. The AI era risks becoming such a moment if organizations pursue productivity gains without designing mechanisms that keep human participation economically relevant.

Artificial intelligence is the most powerful Efficiency Play humanity has ever deployed. Firms adopt it to execute the same tasks faster, cheaper, and more reliably, compressing operating costs and decision cycles. From customer service to coding, AI removes friction with extraordinary precision. But efficiency, when unchecked, eliminates wages faster than markets can regenerate new forms of income. Historically, efficiency displaced certain jobs while creating adjacent industries that absorbed labor. The concern today is that AI’s reach extends simultaneously across cognitive, creative, and analytical domains, reducing the space into which workers can transition. Efficiency succeeds operationally but risks weakening the very consumption base that sustains markets.

At the same time, AI represents a sweeping Innovation Play, redrawing competitive boundaries and redefining how value is produced. Yet innovation must expand opportunity, not merely substitute for it. When innovation compresses capability, shrinking the need for human expertise instead of amplifying it, it generates technological success without broad-based economic inclusion. This also weakens the Differentiation Play. Many industries have long relied on branding, experience, and emotional resonance to command premiums, but algorithmic agents optimize for utility, not perception. Machines do not exhibit loyalty or bias; they pursue price and performance. As AI increasingly intermediates transactions, differentiation erodes, and markets gravitate toward commoditization.

The most consequential gap, however, lies in the underdevelopment of the Aggregation Play. Aggregation is what transforms productivity into shared prosperity by coordinating demand, supply, and participation at scale. Every enduring technological revolution, from electrification to the internet, succeeded because it aggregated new economic actors, enabling more people to earn, transact, and consume. If AI enhances production without creating new pathways for individuals and firms to generate income, economies may experience growth without absorption: output expands while participation contracts. This is the risk scenario, an economy rich in intelligence but thin in demand.

The lesson from the Tekedia EDIA framework is that sustainable progress depends not on the dominance of one play but on their orchestration. Efficiency must release resources that Differentiation refines, Innovation expands, and Aggregation redistributes through new markets and roles. The AI age will therefore be judged not by the sophistication of its algorithms but by the ingenuity of its market design. The real strategic question for leaders is no longer how to build smarter systems, but how to ensure those systems enable broader economic belonging. Markets thrive when productivity and participation grow together; when they diverge, even the most intelligent economy risks becoming structurally fragile.

Anthropic Rejects US Government Demand for Unfettered Access to its Claude AI Model, Trump Responds

By

Paul Ugbede Godwin

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February 27, 2026

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Anthropic has rejected the US government’s specifically the Pentagon’s demand for unfettered access to its Claude AI model. Anthropic CEO Dario Amodei publicly stated in a company blog post that the company “cannot in good conscience accede” to the Pentagon’s request.

This came after the Department of Defense (DoD), under Defense Secretary Pete Hegseth, issued an ultimatum giving Anthropic until 5:01 p.m. ET on Friday, February 27, 2026, to agree to remove certain safeguards and allow “all lawful uses” of Claude without restrictions.

The company has maintained red lines prohibiting Claude’s use for: Mass domestic surveillance of American citizens. Fully autonomous weapons; systems that can select and engage targets without human oversight.

Amodei emphasized that frontier AI systems are “simply not reliable enough” for such high-stakes applications and that these uses could undermine democratic values. Anthropic argued that recent contract language from the Pentagon offered little meaningful protection against these scenarios and could be overridden.

The DoD sought unrestricted access as part of a $200 million contract signed in 2025, under which Claude was the first frontier AI model deployed on classified US government networks for tasks like intelligence analysis and operational planning. The Pentagon rejected explicit carve-outs for Anthropic’s concerns, insisting on “all lawful purposes.”

Threats included: Canceling the contract.
Designating Anthropic a “supply chain risk”; a label typically reserved for foreign adversaries like Huawei, potentially barring US companies from partnering with Anthropic if they work with the military. Invoking the Defense Production Act to compel compliance.

Critics noted the threats were contradictory—one labels Anthropic a risk, while the other treats Claude as essential to national security.
Anthropic has been proactive in supporting US national security; deploying models to classified networks and national labs while restricting sales to entities linked to the Chinese Communist Party.

Other AI providers like Google, OpenAI, and xAI reportedly have similar DoD contracts with fewer restrictions. Replacing Anthropic’s tools on classified systems could take the Pentagon months, per sources. This standoff highlights tensions between AI companies’ ethical safeguards and government and military demands for unrestricted access to powerful models.

Anthropic appears to be standing firm, potentially risking significant penalties but prioritizing its principles on AI safety. Anthropic’s “red lines” refer to the strict, non-negotiable restrictions the company places on how its AI model, Claude, can be used—particularly in high-stakes or sensitive applications like those involving the U.S. military or government.

These red lines stem from Anthropic’s core commitment to responsible AI development, as outlined in its Acceptable Use Policy (embedded in contracts), its Constitutional AI framework for Claude, and public statements by CEO Dario Amodei.

They represent explicit prohibitions designed to prevent misuse that could cause catastrophic harm, undermine democratic values, or violate ethical principles. In the context of the ongoing 2026 dispute with the Pentagon, Anthropic has consistently highlighted two bright red lines that it will not cross.

No use for mass domestic surveillance of American citizens. This prohibits Claude from being deployed in systems that conduct large-scale monitoring or surveillance of U.S. persons (citizens or residents on American soil). Anthropic views this as a threat to privacy, civil liberties, and democratic norms.

The restriction is specifically focused on domestic (U.S.-based) mass surveillance; it does not categorically ban foreign surveillance or other national security intelligence activities. The company has sought explicit contractual assurances that Claude won’t enable such uses, arguing that frontier AI models are not reliable enough for these applications without risking abuse.

No use in fully autonomous weapons or lethal autonomous weapon systems without meaningful human oversight. This bans deployment of Claude in weapons systems that can autonomously select, target, and engage without human intervention or “in the loop” decision-making.

Examples include AI-driven drones, missiles, or other systems making final lethal decisions independently. Anthropic emphasizes that current AI is “simply not reliable enough” for life-or-death choices at this level of autonomy, and such uses could lead to unintended escalation, errors, or ethical violations.

The company has indicated some flexibility for defensive scenarios, but draws a hard line against fully autonomous offensive or lethal applications. These red lines are contractual guardrails in Anthropic’s agreements including its $200 million DoD contract from 2025, part of its Acceptable Use Policy.

They align with Anthropic’s broader AI safety philosophy: prioritizing long-term risk mitigation, constitutional principles, and avoiding contributions to existential or catastrophic risks. Amodei stated that Anthropic “cannot in good conscience accede” to demands for unrestricted “all lawful uses,” as recent Pentagon contract language offered insufficient protections against these scenarios and could be overridden.

The Pentagon has pushed for removal of these restrictions to enable “all lawful purposes,” rejecting explicit carve-outs. This has led to threats of contract cancellation, “supply chain risk” designation, or invoking the Defense Production Act.

Notably, other AI companies; OpenAI via Sam Altman’s statements have expressed alignment with similar red lines on mass surveillance and autonomous lethal weapons, potentially complicating the DoD’s alternatives. These positions reflect Anthropic’s founding ethos as a safety-focused AI lab, even as it engages with national security partners.

The company supports many military uses—like intelligence analysis or operational planning—but insists on these boundaries to avoid enabling dystopian or uncontrollable outcomes. Anthropic remains firm on these red lines despite mounting pressure.

Trump Responds

Trump instructed all U.S. federal agencies to stop using Anthropic’s Claude AI immediately, following the company’s refusal to ease safeguards against fully autonomous weapons and mass surveillance. He allowed a six-month phase-out for the Department of War and dependent agencies, while warning of severe consequences if Anthropic resists. The dispute arose when Defense Secretary Pete Hegseth demanded full access by Friday’s deadline; Anthropic CEO Dario Amodei rejected it, offering R&D collaboration instead. Supporters hailed it as protecting national security, while critics like Sam Altman and Sen. Mark Kelly warned it weakens U.S. AI edge against rivals like China.

ZachXBT Identifies Axiom Exchange’s Broox Bauer of Abusing Internal Tools To Access and Share Sensitive User Data

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