A16zcrypto says active crypto users are now around 70 million, up 10 million from last year, showing more people are actually using crypto. And adoption is picking up everywhere, with wallets booming in emerging markets more than anywhere else. Andreessen…A16zcrypto says active crypto users are now around 70 million, up 10 million from last year, showing more people are actually using crypto. And adoption is picking up everywhere, with wallets booming in emerging markets more than anywhere else. Andreessen…
Crypto on-chain activity surges as active users hit 70M, a16zcrypto reveals
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A16zcrypto says active crypto users are now around 70 million, up 10 million from last year, showing more people are actually using crypto. And adoption is picking up everywhere, with wallets booming in emerging markets more than anywhere else.
Summary
- Andreessen Horowitz’s crypto arm estimates that active crypto users have grown to 40-70 million, up 10 million from last year, signaling a shift from passive ownership to regular on-chain activity.
- Adoption is rising globally, with mobile wallets booming in emerging markets and trading activity leading in developed nations, while Bitcoin, Ethereum, and Solana see renewed engagement from users and developers.
- Institutional adoption and stablecoins are transforming finance, with billions flowing through exchange-traded products and over $150 billion in U.S. Treasuries held on-chain, highlighting crypto’s expanding role in global money flows.
Andreessen Horowitz‘s crypto-focused investment arm, a16zcrypto, says the crypto market is quietly carving out more room at the global finance table than many outside the industry might expect.
In a recent “State of Crypto 2025” report, the firm estimated there’re roughly 40-70 million active crypto users, representing an increase of about 10 million over the past year, based on an updated methodology.
While this remains a small fraction of the 716 million people who own crypto, the crypto-focused arm suggested it signals the market is moving beyond passive ownership into more regular, on-the-ground activity.
Meanwhile, token-related web traffic points to a different type of user in developed nations. Australia and South Korea show heavy engagement with trading and speculation rather than pure transactional activity, the data reveals.
This growing user activity is mirrored in the market itself. Bitcoin (BTC) remains dominant, crossing $126,000 and capturing more than half of crypto’s market cap. Ethereum (ETH) and Solana (SOL) are clawing back from their post-2022 slumps.
Developers and institutions
On the developer side, a16zcrypto said Ethereum and its Layer-2 networks remain the top destination for new builders, while Solana has seen builder interest rise 78% over the past two years.
Institutional adoption, the firm added, is no longer a matter of speculation as major banks and fintechs are rolling out crypto products, from Circle’s billion-dollar IPO to JPMorgan and Visa integrating digital assets alongside equities and ETFs.
Exchange-traded products are unlocking billions of institutional dollars, while publicly traded “digital asset treasury” companies now collectively hold around 10% of both Bitcoin’s and Ethereum’s token supplies.
Stablecoins are now here
Stablecoins, a16zcrypto said, have become the backbone of the on-chain economy. The report indicated that stablecoins have done $46 trillion in total transaction volume in the last year, up 106% from the previous year.
Adjusted for organic activity, that amounts to $9 trillion, more than five times PayPal’s throughput and over half of Visa’s. Monthly adjusted stablecoin transaction volume reached $1.25 trillion in September alone, a figure largely uncorrelated with trading volume, underscoring real-world use beyond speculation.
The rise of stablecoins is reshaping dollar dynamics. They now hold more than $150 billion in U.S. Treasuries, more than many sovereign nations. Per the data, over 99% of stablecoins are denominated in U.S. dollars, and they are projected to grow 10x to more than $3 trillion by 2030, offering a new, on-chain source of demand for U.S. debt even as foreign central bank holdings falter.
So, in other words, crypto’s footprint is expanding. From the wallets in Argentina to Wall Street boardrooms, it’s starting to matter where it counts: in the flow of money itself.
Disclaimer: The articles reposted on this site are sourced from public platforms and are provided for informational purposes only. They do not necessarily reflect the views of MEXC. All rights remain with the original authors. If you believe any content infringes on third-party rights, please contact crypto.news@mexc.com for removal. MEXC makes no guarantees regarding the accuracy, completeness, or timeliness of the content and is not responsible for any actions taken based on the information provided. The content does not constitute financial, legal, or other professional advice, nor should it be considered a recommendation or endorsement by MEXC.
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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. For a specific stock, the v3 endpoint lets you request transcripts by symbol, quarter, and year using the pattern:
https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={q}&year={y}&apikey=YOUR_API_KEY
here’s how you can fetch NVIDIA’s transcript for a given quarter:
import requestsAPI_KEY = "your_api_key"symbol = "NVDA"quarter = 2year = 2024url = f"https://financialmodelingprep.com/api/v3/earning_call_transcript/{symbol}?quarter={quarter}&year={year}&apikey={API_KEY}"response = requests.get(url)data = response.json()# Inspect the keysprint(data.keys())# Access transcript contentif "content" in data[0]: transcript_text = data[0]["content"] print(transcript_text[:500]) # preview first 500 characters
The response typically includes details like the company symbol, quarter, year, and the full transcript text. If you aren’t sure which quarter to query, the “latest transcripts” endpoint is the quickest way to always stay up to date.
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.
Here’s a small example of how you might start preparing the data:
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With the transcript cleaned and divided, you’re ready to feed it into Groq’s LLM. Chunking may be necessary if the text is very long. A good approach is to break it into segments of a few thousand tokens, summarize each part, and then merge the summaries in a final pass.
Summarizing with Groq LLM
Now that the transcript is clean and split into Prepared Remarks and Q&A, we’ll use Groq to generate a crisp one-pager. The idea is simple: summarize each section separately (for focus and accuracy), then synthesize a final brief.
Prompt design (concise and factual)
Use a short, repeatable template that pushes for neutral, investor-ready language:
You are an equity research analyst. Summarize the following earnings call sectionfor {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-outs5) Q&A takeaways (if present)Text:<<<{section_text}>>>
Python: calling Groq and getting a clean summary
Groq provides an OpenAI-compatible API. Set your GROQ_API_KEY and pick a fast, high-quality model (e.g., a Llama-3.1 70B variant). We’ll write a helper to summarize any text block, then run it for both sections and merge.
import osimport textwrapimport requestsGROQ_API_KEY = os.environ.get("GROQ_API_KEY") or "your_groq_api_key"GROQ_BASE_URL = "https://api.groq.com/openai/v1" # OpenAI-compatibleMODEL = "llama-3.1-70b" # choose your preferred Groq modeldef call_groq(prompt, temperature=0.2, max_tokens=1200): url = f"{GROQ_BASE_URL}/chat/completions" headers = { "Authorization": f"Bearer {GROQ_API_KEY}", "Content-Type": "application/json", } payload = { "model": MODEL, "messages": [ {"role": "system", "content": "You are a precise, neutral equity research analyst."}, {"role": "user", "content": prompt}, ], "temperature": temperature, "max_tokens": max_tokens, } r = requests.post(url, headers=headers, json=payload, timeout=60) r.raise_for_status() return r.json()["choices"][0]["message"]["content"].strip()def build_prompt(section_text, symbol, quarter, year): template = """ You are an equity research analyst. 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.
If a section is extremely long, chunk at ~5–8k tokens, summarize each chunk with the same prompt, then ask the model to merge chunk summaries into one section summary before producing the final one-pager.
If you also fetched headline numbers (EPS/revenue, guidance) earlier, prepend them to the prompt as brief context to help the model anchor on the right outcomes.
Building the End-to-End Pipeline
At this point, we have all the building blocks: the FMP API to fetch transcripts, a cleaning step to structure the data, and Groq LLM to generate concise summaries. The final step is to connect everything into a single workflow that can take any ticker and return a one-page earnings call summary.
The flow looks like this:
Input a stock ticker (for example, NVDA).
Use FMP to fetch the latest transcript.
Clean and split the text into Prepared Remarks and Q&A.
Send each section to Groq for summarization.
Merge the outputs into a neatly formatted earnings one-pager.
Here’s how it comes together in Python:
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With this setup, generating a summary becomes as simple as calling one function with a ticker and date. You can run it inside a notebook, integrate it into a research workflow, or even schedule it to trigger after each new earnings release.
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Conclusion
Earnings calls no longer need to feel overwhelming. With the Financial Modeling Prep API, you can instantly access any company’s transcript, and with Groq LLM, you can turn that raw text into a sharp, actionable summary in seconds. This pipeline saves hours of reading and ensures you never miss the key results, guidance, or risks hidden in lengthy remarks. Whether you track tech giants like NVIDIA or smaller growth stocks, the process is the same — fast, reliable, and powered by the flexibility of FMP’s data.
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