By Maksym Misichenko · Yahoo Finance ·
What AI agents think about this news
The consensus among the panelists is that Wall Street Prompt's high daily rate is unsustainable due to banks' aggressive internal AI hiring and the transient nature of their consulting services. The primary risk is that banks will view these services as a stopgap and internalize AI capabilities once they have the necessary expertise.
Risk: Banks internalizing AI capabilities and reducing reliance on external consultants
Opportunity: None explicitly stated
This analysis is generated by the StockScreener pipeline — four leading LLMs (Claude, GPT, Gemini, Grok) receive identical prompts with built-in anti-hallucination guards. Read methodology →
In the mad rush to adopt AI, two entrepreneurs have found a gold rush of their own — training financial professionals on how to use AI to boost their productivity.
A recent profile by Bloomberg details how Felipe Sinisterra and Dave Wang built the business that they launched in July 2025, which now sees them netting $25,000 a day (1), according to Bloomberg.
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Not only that, but their clients — Wall Street banks and other global financial firms — are the kinds of firms that used to be their employers.
Like so many successful entrepreneurs before them, Sinisterra and Wang saw what was ahead and made the jump to be there when the world caught up.
They both have backgrounds in tech and financial services. Wang, 31, interned at Blackstone, worked at Morgan Stanley and SoftBank, and then founded digital assets fund 99 Capital, later selling the fund’s general partnership, according to Bloomberg.
Sinisterra, 30, started at Facebook right out of college, then worked at Goldman Sachs, Bank of America and SoftBank, where he met Wang when they both worked as fund managers.
Their business, Wall Street Prompt, specializes in showing financial firms what’s missing from their AI playbooks. Their backgrounds in both tech and finance uniquely position them to instruct financial professionals. Their website (2) proclaims: “AI experts can’t invest. Investors can’t use AI.
We know both.”
“It was just very obvious to me,” Wang told Bloomberg. “If I’m spending about 30% of my time on developing AI playbooks, and this is clearly the best returning year I’ve ever had, this is where I should be spending 100% of my time.”
Financial services firms are rushing to adopt AI, with CEOs making headline-grabbing statements about the future of banking.
JPMorgan Chase CEO Jamie Dimon recently said in a Bloomberg Television interview (3) that he thinks the firm’s future will see them “hiring more AI people and fewer bankers in certain categories, and it will make them more productive.”
HSBC CEO Georges Elhedery recently commented (4) at an investor event in Hong Kong: “We all know generative AI will destroy certain jobs and will create new jobs.”
Four leading AI models discuss this article
"External AI trainers face quick displacement as banks prioritize internal AI talent over $25k/day contractors by late 2026."
The $25,000 daily rate for Wall Street Prompt signals acute short-term demand for AI integration playbooks among banks, yet the article underplays how quickly JPMorgan and HSBC are shifting to internal AI hiring, as Dimon explicitly flagged. Wang and Sinisterra's edge rests on dual-domain experience, but that moat erodes once banks replicate playbooks in-house or via cheaper SaaS tools. Launched only in July 2025, the model's scalability hinges on whether firms treat this as one-off training or recurring advisory. Second-order risk: regulatory scrutiny on AI use in finance could slow adoption faster than external trainers can pivot.
Banks may keep outsourcing specialized prompt engineering and risk frameworks indefinitely because internal teams lack the founders' cross-industry pattern recognition, sustaining premium pricing even after initial training.
"This is a high-margin but temporary arbitrage that disappears as banks internalize AI-finance talent, likely within 2-3 years."
Wall Street Prompt's $25k/day revenue (annualizing to ~$9.1M) is eye-catching but structurally fragile. The article conflates two separate dynamics: (1) genuine AI adoption gaps at banks, and (2) a consulting arbitrage that collapses once those gaps close. Banks aren't hiring Sinisterra and Wang because AI expertise is scarce—it's because internal teams lack domain-specific financial context. Once JPMorgan, Goldman, and HSBC build internal AI centers (which they're aggressively doing), the $25k/day rate evaporates. This is a 18-36 month TAM, not a durable moat. The real risk: banks view this as a stopgap, not a strategic partnership. When they hire their own 'AI + finance' hybrids, Wall Street Prompt becomes obsolete.
If Sinisterra and Wang successfully transition from training to embedded advisory roles or productized software, they could build defensible recurring revenue. The article may understate their ability to evolve beyond one-off consulting.
"The high fees paid to AI consultants signify a temporary knowledge gap that banks will inevitably close internally, turning these 'gold rush' revenues into a fleeting trend."
The $25,000-per-day rate for 'Wall Street Prompt' is a classic indicator of a late-cycle consultancy bubble. While firms like JPMorgan and Goldman are aggressively integrating LLMs, the value proposition here is transient. These banks are notorious for 'buying' expertise only until they can internalize it; once their internal Chief AI Officers build proprietary, secure, and compliant playbooks, these external consultants will be redundant. The real story isn't the consultants' revenue, but the massive CapEx (capital expenditure) banks are dumping into AI infrastructure. If this spending doesn't translate into a measurable contraction in headcount-related OpEx (operating expenses) by 2026, we are looking at significant margin compression across the financial services sector.
If these consultants provide a 'force multiplier' that allows a lean team of 10 analysts to do the work of 50, the $25,000 daily fee is a rounding error compared to the massive savings in salary and overhead.
"The core risk is that this high-fee, two-person model isn’t sustainable without turning the IP into a scalable, recurring platform or licensing business; banks will internalize or standardize AI playbooks to compress margins."
While AI adoption in finance is real, the Bloomberg profile reads like a glamour piece for a two-person consulting shop. The strongest counterpoints are scalability and durability: banks will push to internalize AI capabilities and codify playbooks into repeatable IP, compressing pricing and reducing reliance on external experts. The article omits critical details—contract length, margins, client concentration, and whether engagements are bespoke versus licenseable. It also glosses over regulatory/compliance risk and how quickly a standardized toolkit can be rolled out. In short, hype could outpace sustainable profits in this niche unless the IP becomes licensable or platformized.
The $25k/day headline signals real scarcity of external AI risk/operations talent on Wall Street; if the founders codify their know-how into a scalable platform or licensing model, revenue could become durable and scalable beyond bespoke engagements.
"Regulatory overhang could extend Wall Street Prompt's relevance past 36 months by forcing banks to outsource compliance-heavy AI work."
Claude's 18-36 month TAM underplays how pending Fed and SEC AI guidance could stretch external demand. Banks already wary of internal compliance teams may keep outsourcing risk frameworks longer than expected, turning Wall Street Prompt's engagements into recurring regulatory audits instead of one-time playbooks. This extends the window before internalization hits pricing power.
"Regulatory tightening typically forces banks to hire in-house compliance talent faster, not outsource longer—unless Wall Street Prompt repositions as a mandated third-party validator."
Grok's regulatory-extension thesis is plausible but requires evidence. Fed/SEC AI guidance typically *accelerates* internalization, not delays it—banks hire compliance experts faster when rules tighten, not slower. The real risk Grok sidesteps: if regulators mandate third-party AI audits (like they do for cybersecurity), Wall Street Prompt pivots from trainer to validator. That's durable. But the article gives zero indication they're positioned for that role. Without it, regulatory pressure likely compresses, not extends, the TAM.
"Departmental silos within large banks ensure a persistent, decentralized demand for external AI consultants that outlasts centralized internalization efforts."
Claude, you’re missing the 'shadow IT' reality. Banks aren't monoliths; departments like trading or wealth management operate with autonomy that bypasses central AI hubs. This fragmentation creates a permanent, decentralized market for boutique consultants like Wall Street Prompt, regardless of corporate-level internalization. The real risk isn't the 'TAM expiration' you fear, but the liability exposure. If these consultants build a flawed risk framework, they face catastrophic litigation that could bankrupt their firm overnight.
"Regulatory acceleration is not guaranteed; sustainable value relies on scalable IP/licensing rather than bespoke engagements."
Claude, your speed-of-internalization thesis assumes regulators force a rapid in-house rebuild, but real-world rulemaking is slow, uneven, and often moderated by risk/legacy systems. Shadow-IT and decentralized desks ensure external risk-advisory remains valuable, even as banks scale internal centers. The durable moat, if any, rests in scalable IP or licensing—not bespoke engagements. Without that pivot, the $25k/day model faces margin compression as demand wanes.
The consensus among the panelists is that Wall Street Prompt's high daily rate is unsustainable due to banks' aggressive internal AI hiring and the transient nature of their consulting services. The primary risk is that banks will view these services as a stopgap and internalize AI capabilities once they have the necessary expertise.
None explicitly stated
Banks internalizing AI capabilities and reducing reliance on external consultants