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Banks are right, for now, to block third-party AI agents from taking action inside consumer bank accounts. An agent paying with a capped card, as $META's Muse does, is a different and narrower permission. Axios reported this week that major U.S. banks are finding unapproved third-party agents inside customer accounts, most let in with the customer's own login. Some tried actions that looked independent of what the customer asked. $MS is blocking such agents, per Axios sources. $JPM, $BAC and $C are restricting them. Banks are slow to change how a deposit account works for a reason: the duty runs to every customer and to the bank's own systems, not only to the early adopter who opted in. An agent logged in with real customer credentials puts fraud, liability and security risk on the bank, and that risk reaches customers who never opted in. The bank is right to keep that action out of the account for now, even if some customers want it. An agent holding the login can see the whole account and send money out. An accepted wire is generally final and sits outside Regulation E, the consumer rule for electronic transfers. An ACH transfer the account sends has no chargeback. Getting it back depends on the receiving bank agreeing. Tell an agent to pay, and an ACH payment inside that instruction can count as authorized under that rule. Money lost inside that grant is the customer's. Jamie Dimon called this a liability transfer: "If people are using them, they should understand the risk." That settles the customer's loss, not the bank's exposure. Muse pays through Stripe Link or a single-use virtual card capped at the total the buyer approves. The customer can dispute a wrong charge. The issuer can cancel that card so the next attempt fails. The agent never touches the deposit account. Agentic commerce has to prove itself on the capped card before that access reaches the deposit account.
@convequity

Convequity on X

Neoclouds Now Trade as a Hedge Against AI Demand Growth Convequity’s latest ABBX review shows $CRWV, $NBIS and $IREN are inversely correlated with AI demand growth. Stronger demand widens the working-capital hole and keeps investors risk-off, even as order books expand. Insatiable demand is usually the reason to own the supplier. Here it does the opposite. Jensen Huang told CNBC at GTC Berlin that neocloud demand is “off the charts” and that working capital is the missing piece. Working capital is current assets minus current liabilities. A cash-conversion mismatch squeezes it even when demand is huge. Liabilities hit first. A neocloud pays $NVDA and networking suppliers in roughly 30–60 days. Asset-backed hardware loans add heavy near-term debt service. Assets lag. A signed customer contract sits off the balance sheet as backlog. It only becomes receivables or cash once the cluster is deployed, energized and billed — months later. Cash leaves before revenue arrives. A faster pipeline simply widens the short-term deficit. Latest ABBX figures NBIS: $8.5B gross debt vs $8.0B cash; guidance for >$9B customer prepayments this year. IREN: $7.6B debt vs $5.9B cash; $13.8B hardware and site commitments; 20% prepay on the Microsoft contract. CRWV: $35.6B debt vs $5.5B cash, plus $16.3B operating leases and $35.5B rent not yet commenced. Build-out relies far more on debt than customer prepayments. Even with those prepayments, NBIS and IREN keep raising large extra funding. CRWV faces the same hole with a far larger debt stock. Handing contracts back to resell at higher spot prices would ease CRWV’s cash hole only if customers and lenders agreed. Customers pay even if they do not use the capacity, CRWV must deliver it, and we found no public right to change the price or exit early. Break a customer contract and the hardware lenders can lock up CRWV’s cash. If that lockup holds, they take the cash to repay the loan. Borrowing later would cost more, and the switch only pays while spot stays high. If spot fell, CRWV would lose both the committed revenue and the contract value backing those loans. For investors to turn risk-on again, AI demand growth needs to slow enough for infrastructure supply to catch up.
@convequity

Convequity on X

$NVDA is in a bind. In our view it needs enterprise AI adoption to grow quickly, but a typical enterprise cannot afford Nvidia's GPUs and does not have the resources to build and run an Nvidia stack directly. So enterprises take the cheaper and more manageable option and access AI through the hyperscalers. They skip the large upfront cost, and they can run inference on the hyperscalers' custom ASICs, or at least have that option versus Nvidia's GPGPUs. In our view, those ASICs are the more efficient inference choice where a hyperscaler offers them. To curtail that, we think Nvidia would have to cut GPGPU prices sharply enough to make direct enterprise adoption realistic. That means lower margins. Dell ($DELL) was supposed to be the way around the bind, a channel that puts Nvidia compute in front of enterprises that will not build the stack themselves. We think Dell has mostly used the relationship to secure GPU supply and grow its own revenue and margins, not to open the smaller enterprise market Nvidia needs. Palantir ($PLTR) is still the exception on enterprise AI adoption, not on buying Nvidia compute directly. Its US commercial revenue grew 149% year on year last quarter, accelerating from 133% the quarter before. Outside Palantir and coding agents, we see little enterprise success. The bind stands. Direct enterprise adoption of Nvidia compute is still progressing very slowly, and we do not see a way out that leaves margins where they are.

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