ai

The financial world is buzzing following the nomination of Kevin Warsh as the next Federal Reserve Chair. As we look toward the end of Jerome Powell’s term in May, a major shift in monetary philosophy may be on the horizon. 🏛️

Warsh is championing a compelling—though debated—reason to cut interest rates: The AI Productivity Boom. 🤖⚡

The Core Argument
Warsh suggests that Artificial Intelligence is ushering in the "most productivity-enhancing wave of our lifetimes." Drawing parallels to the dot-com era of the 1990s, he argues that:

High Productivity = Lower Inflation: When workers produce more efficiently, the economy can "run hot" without spiking prices. 📈

Structural Disinflation: Much like the internet, AI could naturally keep costs down, giving the Fed a green light to ease rates without fear of an inflation rebound. 📉

The "Greenspan" Leap of Faith
Warsh is urging his colleagues to take a "leap of faith" similar to Alan Greenspan’s in the 90s. By trusting anecdotal evidence of a productivity surge before it fully showed up in the hard data, Greenspan successfully avoided unnecessary rate hikes, fueling a historic era of growth. 🚀

A Divided Fed
However, the path to lower rates isn't guaranteed. Current Fed voters like Beth Hammack and Lorie Logan remain cautious:

The Neutral Rate: Some argue that high productivity actually justifies higher interest rates because the economy becomes more resilient.

Demographic Shifts: Unlike the 90s, we now face an aging population and a tighter labor market, which could offset AI's gains. 👥

As the markets hover at record highs—with the DOW near 49,500—all eyes are on whether Warsh can build consensus among a divided 12-person committee. 🏛️⚖️

What do you think? Is AI already boosting our economy enough to justify cheaper borrowing, or is it too soon to bet the house on tech-driven disinflation? Let’s discuss in the comments! 👇

#FederalReserve #KevinWarsh #AI #Economy2026 #InterestRates
$KAVA
$KNC
$LINK

While 99% of people on Binance Square are fighting over Bitcoin’s $70k support or chasing the next dog meme, a massive structural shift just went live on the Ethereum mainnet.It’s called ERC-8004.
And if you don't know what it is, you're already behind.
We are officially entering the Age of the Machine Economy.
Here is the "Out-of-the-Box" alpha that no one is talking about yet:
The Death of the "Wallet Holder" 👤❌
For 15 years, crypto has been built for humans. We sign transactions, we check prices, we get liquidated. But ERC-8004 changes the game. It’s a new standard for On-Chain AI Agent Identity.
In 2026, the biggest "whales" won't be hedge funds or billionaires.
They will be autonomous AI agents that trade, farm, and provide liquidity 24/7 with zero emotion and 100% efficiency.
The "Agentic" Supercycle 🚀
This isn't just "AI crypto" (which was mostly hype). This is Embodied AI. We are seeing the convergence of DePIN (Physical Infrastructure) and AI Agents.
AI agents are now renting GPU power from decentralized networks.They are paying each other in crypto using the new x402 payment protocol.They are building their own "Social Credit Scores" on-chain via ERC-8004.
Why This is the Ultimate "Hidden Alpha" 💎
Think about it: When thousands of autonomous agents start competing for block space, what happens to gas fees?
What happens to the demand for the "Trust Layer" (Ethereum)?
We are moving from a market driven by "Retail FOMO" to a market driven by Machine Demand.
This is a permanent, structural supply sink for top-tier assets that most people haven't even calculated into their models.
How to Position (The Visionary Play) 🛡️
Stop looking at the price chart and start looking at the Registry Stats.
Infrastructure: Watch projects building the "Brains" (AI models) and the "Body" (DePIN networks).
Identity: ERC-8004 is the trust layer. Projects integrating this standard early are the ones building the "Passport Office" for the future economy.
Liquidity: AI agents need deep, automated liquidity. The protocols that win their "business" will be the biggest winners of 2026.
MY TAKE:
The "Retail Bull Run" is a distraction. The "Machine Supercycle" is the real story. In two years, you won't be trading against other humans; you'll be trading against agents that never sleep.
Are you ready to join the machine economy, or are you still waiting for a "dip" that the agents will buy before you even see it?
WHAT’S YOUR MOVE?
🤖 A) "I’m already researching ERC-8004 projects."
🔌 B) "DePIN is my main play for 2026."
🦖 C) "I’m sticking to BTC/ETH and ignoring the bots."The future is autonomous.
Let’s discuss the "Agent Alpha" below. 👇🤖
#AI #ERC8004 #DePIN #Ethereum #CryptoTrends2026
Not financial advice. The machines are coming. DYOR

#investors ip in a volatile sell-off of perceived #AI losers”, choosing instead to stand on the sidelines until the full scale of the economic disruption becomes clearer.

The launch of a number of new AI tools in recent days has threatened to disrupt traditional business models in sectors including trucking, real estate, wealth management and advertising, with violent share price moves highlighting a market wracked with nerves about what comes next.

Despite companies rushing to reassure investors that AI will enhance their business and that plunging share prices are an overreaction, many portfolio managers are resisting the temptation to buy the dips that emerge.

“The world is changing very, very quickly . . . we wouldn’t have the conviction to try and bottom-fish,” s
🔥
EquitiesAdd to myFT
Investors reluctant to ‘buy the dip’ after AI scares
Sectors including wealth management and trucking have been hit with sudden share price declines

Some of the biggest software stocks have sold off significantly in recent weeks Michael Nagle/Bloomberg
Investors reluctant to ‘buy the dip’ after AI scares on #X (opens in a new window)
Investors reluctant to ‘buy the dip’ after AI scares on facebook (opens in a new window)
Investors reluctant to ‘buy the dip’ after AI scares on linkedin (opens in a new window)
Investors reluctant to ‘buy the dip’ after AI scares on whatsapp (opens in a new window)

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Emily Herbert in London

PublishedFEB 16 2026
Investors are shying away from buying the dip in a volatile sell-off of perceived “AI losers”, choosing instead to stand on the sidelines until the full scale of the #Economic disruption becomes clearer.

The launch of a number of new AI tools in recent days has threatened to disrupt traditional business models in sectors including trucking, real estate, wealth management and advertising, with violent share price moves highlighting a market wracked with nerves about what comes next.

Despite companies rushing to reassure investors that AI will enhance their business and that plunging share prices are an overreaction, many portfolio managers are resisting the temptation to buy the dips that emerge.

“The #world is changing very, very quickly . . . we wouldn’t have the conviction to try and bottom-fish,” said Robert Schramm-Fuchs, portfolio manager at Janus Henderson. 

“The AI models today are substantially more powerful than the ones from six or 12 months ago. What seems protected as a business model today might not be [in the future],” Schramm-Fuchs added. “It makes it even harder to buy the dip.” 

The Nasdaq Composite gave up 2.1 per cent this week and the broader S&P 500 shed 1.4 per cent. But the relatively measured index-level declines mask far more violent moves beneath the surface, with trucking giant CH Robinson falling 12 per cent and investment firm Charles Schwab down 11 per cent. Commercial real estate firm CBRE dropped 16 per cent and insurance broker Gallagher declined 13 per cent this week.

While billions of dollars were wiped from market caps, newly slashed valuations and share prices have largely failed to recover in subsequent sessions.

“Hesitation” had characterised markets this week, said Valérie Noël, head of trading at Syz Bank. “There’s been very little willingness to defend sharp moves the way you’d normally expect,” she said, and the market was “prioritising uncertainty management over dip-buying”.

While some of the biggest software stocks have sold off significantly in recent weeks, most investors are so far continuing to sell the sector rather than choosing to buy the dip, according to custodial markets data from State Street, which the firm uses to provide a snapshot of investor appetite.

“We see no sign of institutional investors trying to buy the dip in the [software] sector,” said Marija Veitmane, head of equities strategy at State Street, adding that money was instead going to the hardware end of the tech sector.

Goldman Sachs last week launched a new pair trade combining long positions on software “that AI cannot realistically displace because they require physical execution, regulatory entrenchment . . . or human accountability” with short bets against “software-tilted workflows that AI could increasingly automate or rebuild internally”.

“We expect [the former] to recover from the recent software sell-off while [the latter] lags behind,” the bank’s equity strategists said in a note on Thursday.

The logistics sector plunged in erratic trading on Thursday, when an announcement by a little-known $3mn karaoke-turned-freight company in Florida triggered one of the worst ever sell-offs for the trucking sector, wiping billions of dollars from the value of some of the industry’s most established names.

The tiny company at the heart of that sell-off — once the Singing Machine Co, now Algorhythm Holdings — released a white paper on Thursday that said its AI platform could scale freight volumes by up to 400 per cent without a corresponding increase in headcount.

The note ignited fears that new technology would destroy the market value of some of the industry’s leaders, sending logistics companies CH Robinson and Landstar both down by about 15 per cent in a single day.

Wealth management giants suffered similar moves earlier in the week, when AI tax planning firm Altruist released a suite of tools — sending FTSE 100 wealth manager St James’s Place 13 per cent lower — with insurance names similarly hit by a model from AI start-up Insurify. 

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Wall Street hunts next casualty from AI threat to white-collar work
For some fund managers, however, the size of the stock falls looks like an overreaction.

“There is a lot of irrationality in markets at the moment,” said Alex Wright, a portfolio manager at Fidelity International. Wright said he had picked up some bargains in the recent sell-off because “a lot of stocks are not being priced appropriately”.

But others remain reluctant to jump back in.

“I think the [software] sell-off is totally logical,” said Charles Lemonides, the founder of hedge fund ValueWorks. “Valuations were absurd coming into this. Companies that were trading at 50 times earnings have come down to 30 times earnings because they will be hit by some AI disruption.”

Dan Hanbury, a portfolio manager at fund firm Ninety One, said that a lot of “great companies” had been swept up in the recent sell-offs.

“[But] I think the disruption is real, and you have to be very careful,” he added. “AI is going to get a lot more powerful — how can I guarantee that the moats around these companies are still going to be here? I’m not trying to trade that bounce.” 

Last week I tried to pay a contractor from my phone. Simple job. Small amount. And still… the usual mess. Copy this address. Pick the right network. Pay a fee that feels random. Wait. Refresh. Ask “did you get it?” again. It’s 2026 and we still move value like we’re mailing cash in a plain envelope. Now zoom out. People want to run AI apps that pay for data, pay for compute, pay for results, all in tiny chunks, all day. If a chain can’t handle boring payments cleanly, it has no shot at being the rails for AI. Most new L1 chains are still built like they’re hosting a 2019 DeFi demo day. Great for swapping tokens. Weak for constant machine-to-machine work. AI changes the traffic pattern. It’s not “a user signs two big tx a day.” It’s “a service signs thousands of small tx an hour” and those tx are tied to proof, access, usage, and payouts. That’s why “AI-ready” isn’t a slogan. It’s plumbing. Vanar Chain ($VANRY ) is interesting here because it’s aiming at the unsexy stuff: payments rails, predictable execution, and a system that can connect on-chain logic with off-chain compute without turning into chaos.

Think of it like running a busy kitchen. DeFi-only chains are good at cooking one fancy dish at a time. AI workloads are a food truck line at lunch. Same menu. Repeat orders. No time for drama. If your stove heats up and cools down at random, you don’t scale. You just burn food faster. Transaction flow and cost need to be boring. AI apps hate surprise. Humans can tolerate “gas spiked, try later.” Machines can’t. A model calling tools, buying data, or paying for inference needs stable fees and stable timing. When fees jump, the machine doesn’t “feel annoyed.” It breaks. Or it reroutes. Or it starts failing users. This is where market structure matters more than marketing. A chain can claim high TPS all day, but if real usage causes fee spikes, it’s not a platform. It’s a stress test.

Vanar’s angle, from what I care about, is the payments-first posture. Not just “payments exist.” Payments as a core habit of the network. You want low-friction transfers, but also the ability to attach conditions: pay only if data is delivered, pay only if compute finishes, pay per step. That’s basically automated settlement. Settlement just means “the chain is the referee that says who owns what after the action.” If AI is doing the action, the referee has to be fast, cheap, and consistent. No weird pauses. No random foul calls. AI needs trust signals, not vibes. People throw around “on-chain AI” like it’s magic. Reality: most AI compute is off-chain because GPUs are off-chain. So the real question is: can the chain verify what happened off-chain without swallowing the whole dataset? This is where terms like “proof” show up. A proof is just a receipt. Not a tweet. A receipt. There are different kinds of receipts. The fancy kind is a zero-knowledge proof. That’s a way to prove you followed rules without showing the private inputs. Like proving you’re old enough without handing over your full ID. For AI, it can mean proving a model ran with an allowed version, or that a result matches a committed process, without dumping the raw data on-chain. Hard to build. But it’s the direction serious systems move in.

Even without the fancy math, you still need integrity tools: signatures, hashes, and logs. A hash is a fingerprint of data. If the data changes, the fingerprint changes. That lets you anchor “this exact file” or “this exact model version” to the chain, without storing the whole thing on-chain. In AI systems, that matters because data is the asset. Models are the asset. If you can’t anchor and reference them reliably, you can’t build clean markets around them. You get disputes. You get “trust me bro.” And then you get users leaving. This is where Vanar’s ecosystem choices matter. An AI-ready chain isn’t trying to shove gigabytes into blocks. It’s building clean links between on-chain rules and off-chain storage/compute. On-chain: ownership, permissions, payments, and audit trails. Off-chain: the heavy lifting. If the bridge between those worlds is messy, the whole thing feels like duct tape. If it’s clean, devs can ship real products with minimal drama. Identity, access, and rights management. AI apps are not just “run model, get output.” They’re “who can call the model, who can use the data, who gets paid, who can revoke access, what happens if a key is leaked.” This is boring enterprise stuff. Which is exactly why it wins. A chain that’s serious about AI needs a usable permission layer. Permission layer just means rules about who is allowed to do what. Not in a PDF. In code. If I license a dataset for one purpose, I should be able to enforce that license in how keys, access tokens, and payments work. You can’t do this with vibes and a terms-of-service page.

Here token design also stops being a casino chip and becomes a resource tool. I’m not talking price. I’m talking utility. What does $VANRY actually do inside the system? If it’s the unit for fees and settlement, then it becomes the “oil” that keeps the machine running. But the token has to fit the workload. AI workloads are bursty. Spiky. Heavy. If fee design punishes bursts, builders will avoid the chain. If fee design can smooth costs and keep service predictable, builders stick around. That’s the difference between “theoretical decentralization” and “usable infrastructure.” Most new L1s will struggle because they’re still selling performance numbers instead of solving workflow pain. AI doesn’t care about your brand. It cares if the pipeline works at 3 a.m. It cares if payments settle. It cares if access is enforceable. It cares if proofs or receipts exist when things go wrong. AI apps are like factories, not art galleries. If the conveyor belt jams once a day, you don’t call it innovation. You call it downtime. Vanar Chain (VANRY) bet looks closer to what AI builders actually need: payments that feel native, a structure that can anchor data and model integrity, and a path to connect on-chain settlement with off-chain compute in a clean way. That’s not flashy. That’s good. Flashy is for fundraising decks. Infrastructure is for users who don’t want to think about the chain at all.
Not Financial Advice. Just my perspective as someone who cares more about architecture than narratives. If you’re building for AI, the bar is higher now. New L1s can either become boring, reliable plumbing… or they can stay loud. Loud doesn’t ship.
@Vanarchain #Vanar $VANRY #AI

The reliable operation of smart contracts—at the heart of blockchain applications—has grown all the more critical with the rapid rise of decentralized finance (DeFi) and NFT platforms. Traditionally, the auditing of these complex code structures relied on specialized teams conducting manual reviews—a process often fraught with delays, high costs, and the looming risk of human error. Recently, however, artificial intelligence (AI) powered automated systems have dramatically streamlined these security checks, ushering in a new era of efficiency for the industry.
Automated Security Checks and Behavior Simulations
AI has begun to play a pivotal role in the automated auditing of smart contract code, swiftly identifying potential vulnerabilities, bugs, or logical flaws. Thanks to machine learning and advanced algorithms, vast quantities of code can be scanned within minutes, allowing developers to quickly focus on high-risk areas. Intricate threats such as “reentrancy” attacks or overflow errors, previously challenging to unearth, are now detected with far greater accuracy through AI assessments.
Beyond mere code analysis, simulations powered by AI are providing valuable forecasts of how smart contracts would behave in a range of real-world scenarios. By modeling performance under heavy transaction loads, unusual data patterns, or network congestion, these tools can expose faults or weaknesses before contracts are even deployed—reducing the likelihood of costly mistakes.
Continuous Monitoring and Automated Risk Assessment
AI-driven platforms are also enhancing security through real-time, continuous monitoring of deployed smart contracts. These systems are equipped to instantly detect unusual behavior, suspicious transactions, or potential attack attempts. This proactive approach enables developers to respond rapidly, mitigating losses and safeguarding user trust before threats escalate.

Drawing upon historical attack data and known vulnerabilities, AI can assign predictive risk scores to individual contracts. This automated ranking system helps development teams prioritize urgent audits and allocate resources more effectively, particularly for those contracts deemed higher risk.
Benefits for Developers and Users
AI not only trims the financial burden of audits but also delivers timely, error-free assessments. By flagging transactions with excessive gas fees or redundant code segments, these tools offer optimization advice that boosts smart contract efficiency. The upshot is a reduction in transaction costs and an improved experience for end users.
Transparent, objective audit reports generated by AI bolster confidence among both developer teams and those providing funding. Greater clarity about platform security decision-making reduces hesitation and uncertainty, while users themselves benefit from enhanced transparency—enabling them to engage with blockchain platforms more comfortably.

Regulatory compliance is addressed by audit tools that can automatically verify adherence to established regulations and platform policies. This capacity is especially valuable in sensitive areas such as anti-money laundering (AML) procedures and token issuance, as it helps minimize legal risks.
The ability to conduct parallel audits is another boon, enabling teams to review numerous smart contracts simultaneously. As blockchain projects scale rapidly, this approach guarantees that rigorous security standards are maintained without slowing progress.

AI tools seamlessly integrated into development cycles provide immediate feedback to programmers as they write new code. This real-time support facilitates early error detection and boosts overall code quality, helping projects launch with greater confidence and security.
#AI