The post Over $17 trillion missing when on-chain “proof of reserve” standards are applied to Trump’s tariff data appeared on BitcoinEthereumNews.com. President The post Over $17 trillion missing when on-chain “proof of reserve” standards are applied to Trump’s tariff data appeared on BitcoinEthereumNews.com. President

Over $17 trillion missing when on-chain “proof of reserve” standards are applied to Trump’s tariff data

President Donald Trump said this week that the United States has taken in roughly $18 trillion because of tariffs, framing the figure as evidence that his trade policy reshaped the global economy and redirected capital back into the country.

The claim immediately drew scrutiny because it far exceeds any recorded measure of US tariff revenue and eclipses the scale of federal receipts tied to trade by multiple orders of magnitude.

Tariff revenue in the United States is recorded as customs duties and reported monthly and annually by the Treasury Department. Even after a sharp increase following expanded tariffs in 2025, customs duties remain measured in the hundreds of billions, not trillions.

Why the $18 trillion tariff claim doesn’t hold up to the data

Treasury statements show that customs duties totaled about $195 billion in fiscal year 2025, up from the prior year, while monthly collections in late 2025 exceeded $30 billion.

At that pace, total collections would require decades, not years, to approach even a fraction of the figure Trump cited.

The gap stems from what appears to be a definitional shift rather than a dispute over the underlying data.

Trump and senior officials have repeatedly described tariffs as a mechanism that forces companies to invest in domestic manufacturing to avoid higher import costs.

In that framing, tariffs are credited not only with revenue collected at the border but also with announced capital spending plans, long-term purchase commitments, and trade volumes that companies or foreign governments have said they intend to direct toward the United States.

Independent reviews of those claims have noted that such tallies blend unlike categories. According to PolitiFact, administration figures aggregating “investment commitments” combine multiyear pledges, prospective spending plans, and trade agreements that do not represent cash received by the federal government and are not recorded as revenue.

Customs duties, by contrast, reflect funds actually paid to the Treasury and booked in federal accounts.

That distinction matters more in 2025 because the same administration promoting expansive interpretations of tariff outcomes has also moved to modernize how government financial data and assets are tracked and disclosed, including through blockchain-based systems designed to emphasize verifiability and auditability.

Why tariff math, accounting standards, and blockchain transparency matter in 2025

In January, Trump signed Executive Order 14178, which created a presidential working group on digital asset markets and directed agencies to examine how distributed ledger technology could be integrated into federal financial infrastructure.

In March, the White House followed with an executive order establishing a US Strategic Bitcoin Reserve and a broader Digital Asset Stockpile, formally recognizing digital assets on the government balance sheet.

The working group released a 160-page report in July outlining a federal roadmap for digital assets and data modernization. While the report does not move federal budgeting or taxation onto public blockchains, it emphasizes improving the integrity, traceability, and accessibility of public financial information.

Separately, the Commerce Department has partnered with blockchain oracle providers to distribute official macroeconomic data, such as Bureau of Economic Analysis indicators, in an on-chain format that allows users to verify provenance and timing against immutable records.

Taken together, these steps reflect an effort to make specific categories of government data harder to dispute by anchoring them to systems that timestamp, cryptographically sign, and publicly audit figures.

They do not constitute a complete on-chain government accounting system, but they do promote a model where the difference between collected revenue and projected economic effects is clear rather than merely rhetorical.

Applied to tariffs, that model would leave little room for ambiguity. Treasury already publishes customs duty receipts through its Monthly Treasury Statement and related datasets.

On-chain verification separates tariff revenues from projected economic impact

Publishing those figures with on-chain attestations would not change their substance. Still, it would further clarify that tariff revenue consists of amounts actually paid, not downstream economic activity attributed to policy.

Investment announcements, factory construction plans, and trade commitments would stay visible in other datasets, but they would not be shown alongside receipts as money collected by the government.

The administration’s own digital asset framework implicitly reinforces that separation. Blockchain-based reporting does not prevent leaders from arguing that a policy altered incentives or redirected capital flows, but it does constrain how those outcomes are labeled.

Receipts, reserves, and balances are discrete categories, while expectations and pledges occupy another.

Legislation moving through Congress, including the Deploying American Blockchains Act, would further encourage federal agencies to explore distributed ledger technology for public sector use, potentially expanding the scope of verifiable government data in the coming years.

As those efforts progress, the tension between precise accounting and expansive political claims is likely to become more visible, particularly when large figures are invoked to describe outcomes that the underlying records do not support.

Mentioned in this article

Source: https://cryptoslate.com/applying-the-proof-of-reserve-standard-to-trumps-tariff-data-shows-nearly-18-trillion-unaccounted-for/

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Turn lengthy earnings call transcripts into one-page insights using the Financial Modeling Prep APIPhoto by Bich Tran Earnings calls are packed with insights. They tell you how a company performed, what management expects in the future, and what analysts are worried about. The challenge is that these transcripts often stretch across dozens of pages, making it tough to separate the key takeaways from the noise. With the right tools, you don’t need to spend hours reading every line. By combining the Financial Modeling Prep (FMP) API with Groq’s lightning-fast LLMs, you can transform any earnings call into a concise summary in seconds. The FMP API provides reliable access to complete transcripts, while Groq handles the heavy lifting of distilling them into clear, actionable highlights. In this article, we’ll build a Python workflow that brings these two together. You’ll see how to fetch transcripts for any stock, prepare the text, and instantly generate a one-page summary. Whether you’re tracking Apple, NVIDIA, or your favorite growth stock, the process works the same — fast, accurate, and ready whenever you are. Fetching Earnings Transcripts with FMP API The first step is to pull the raw transcript data. FMP makes this simple with dedicated endpoints for earnings calls. If you want the latest transcripts across the market, you can use the stable endpoint /stable/earning-call-transcript-latest. 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Cleaning and Preparing Transcript Data Raw transcripts from the API often include long paragraphs, speaker tags, and formatting artifacts. Before sending them to an LLM, it helps to organize the text into a cleaner structure. Most transcripts follow a pattern: prepared remarks from executives first, followed by a Q&A session with analysts. Separating these sections gives better control when prompting the model. In Python, you can parse the transcript and strip out unnecessary characters. A simple way is to split by markers such as “Operator” or “Question-and-Answer.” Once separated, you can create two blocks — Prepared Remarks and Q&A — that will later be summarized independently. This ensures the model handles each section within context and avoids missing important details. 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Summarize the following earnings call section for {symbol} ({quarter} {year}). Be factual and concise. Return: 1) TL;DR (3–5 bullets) 2) Results vs. guidance (what improved/worsened) 3) Forward outlook (specific statements) 4) Risks / watch-outs 5) Q&A takeaways (if present) Text: <<< {section_text} >>> """ return textwrap.dedent(template).format( symbol=symbol, quarter=quarter, year=year, section_text=section_text )def summarize_section(section_text, symbol="NVDA", quarter="Q2", year="2024"): if not section_text or section_text.strip() == "": return "(No content found for this section.)" prompt = build_prompt(section_text, symbol, quarter, year) return call_groq(prompt)# Example usage with the cleaned splits from Section 3prepared_summary = summarize_section(prepared, symbol="NVDA", quarter="Q2", year="2024")qna_summary = summarize_section(qna, symbol="NVDA", quarter="Q2", year="2024")final_one_pager = f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks — Key Points{prepared_summary}## Q&A Highlights{qna_summary}""".strip()print(final_one_pager[:1200]) # preview Tips that keep quality high: Keep temperature low (≈0.2) for factual tone. 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Here’s how it comes together in Python: def summarize_earnings_call(symbol, quarter, year, api_key, groq_key): # Step 1: Fetch transcript from FMP url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={api_key}" resp = requests.get(url) resp.raise_for_status() data = resp.json() if not data or "content" not in data[0]: return f"No transcript found for {symbol} {quarter} {year}" text = data[0]["content"] # Step 2: Clean and split clean_text = re.sub(r'\s+', ' ', text).strip() if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1) else: prepared, qna = clean_text, "" # Step 3: Summarize with Groq prepared_summary = summarize_section(prepared, symbol, quarter, year) qna_summary = summarize_section(qna, symbol, quarter, year) # Step 4: Merge into final one-pager return f"""# {symbol} Earnings One-Pager — {quarter} {year}## Prepared Remarks{prepared_summary}## Q&A Highlights{qna_summary}""".strip()# Example runprint(summarize_earnings_call("NVDA", 2, 2024, API_KEY, GROQ_API_KEY)) With this setup, generating a summary becomes as simple as calling one function with a ticker and date. 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