The three-stage delivery structure in the U.S. for the FX Super One is expected to begin in Q2 of this year, primarily for FX Par; Phase-Two deliveries to industryThe three-stage delivery structure in the U.S. for the FX Super One is expected to begin in Q2 of this year, primarily for FX Par; Phase-Two deliveries to industry

Faraday Future Announces the FX Super One Roadmap for Mass Production, Sales, Delivery, Service and Ramp-Up and Its Entry into Embodied AI Robotics, along with Its Execution Plan for FF’s Five-Year Business Plan

  • The three-stage delivery structure in the U.S. for the FX Super One is expected to begin in Q2 of this year, primarily for FX Par; Phase-Two deliveries to industry leaders and B2B Partners are expected to begin in Q3 of this year, with limited volume and production ramp-up, targeting positive contribution margin in this phase; Phase-Three full-scale deliveries to the consumer market in Q4 this year or Q1 next year, targeting sustained positive contribution margin.
  • The U.S. FX Super One final launch is scheduled to be held in Q2, alongside the establishment of the necessary after-sales and charging service network, with access to Tesla’s Supercharger Network in North America, Japan and South Korea.
  • The Company announced that after a long period of preparation, its Global EAI Industry Bridge Strategy is expanding to introduce Embodied AI Robotics. FF aims to become first company in the U.S. to deliver humanoid robot products to the market with positive contribution margin, and leading U.S.-based AI robotics company. The Company also hosted private previews of its first planned robotics products at the event site.
  • FF’s five-year cumulative production and sales target is 400,000–500,000 vehicles, primarily driven by FX Super One, the future FX 4 and planned models, aligned with its model introductions and production volumes.
  • FF’s goal is to achieve positive operating cash flow within about three years with gross margins of around 20%.
  • FF plans to hold the U.S. launch and begin sales for its first Embodied AI robotics products at the NADA Show in Las Vegas on February 4, alongside the FX Partner Summit. 

LAS VEGAS, Jan. 7, 2026 /PRNewswire/ — Faraday Future Intelligent Electric Inc. (NASDAQ: FFAI) (“Faraday Future”, “FF” or the “Company”), a California-based global shared intelligent electric mobility ecosystem company, today announced several new business initiatives and updates as it outlined its 2026 New Year Outlook at its first-ever Stockholders’ Day in Las Vegas, coinciding with the Consumer Electronics Show (CES). The updates included sharing comprehensive milestone targets for the FX Super One, along with the Company’s five-year business execution strategy, that plans for between 400,000-500,000 cumulative units, mainly driven by the FX Super One, a planned future FX 4 model, and other planned models. The Company also announced an EAI (Embodied AI) robotics strategy under its upgraded Global EAI Industry Bridge. This dual-track growth model could provide a new growth curve for FF.

The Company’s updates were announced by numerous members of the FF and FX leadership teams including YT Jia, founder and Global Co-CEO of FF, Matthias Aydt, Global Co-CEO of FF, Jerry Wang, Global President of FF, Koti Meka, CFO of FF, and Xiao (Max) Ma, Global CEO of the FX brand.

“First and foremost, I want to thank all of our stockholders both in attendance and who joined the livestream online for taking the time to hear these comprehensive updates on the Company’s future from myself and other members of our leadership team,” said Mr. Aydt. “We’ve said it before, but I want to say it again today; this event and all of our forward-facing communications to our stockholders reflects Faraday Future’s commitment to transparency, stockholder engagement, and long-term value creation as the Company accelerates toward its mission of revolutionizing the intelligent electric vehicle space.”

Replay of the event is available at: https://www.ff.com/us/2026-CES/ 

FX Super One Roadmap for 2026

The production stages for the FX Super One will be laid out as follows: The first phase of delivery is expected to occur in Q2 of this year. It will primarily focus on deliveries to FX Par partners with a cap of 50 units. The second phase of delivery is expected to occur in Q3 of this year, delivering to industry leaders and B2B partners with a cap of 200 units. The third phase of delivery is expected to occur in Q4 of this year or Q1 of next year. This stage will initiate full-scale deliveries, marking the transition to FX’s broad market rollout in the U.S.

From 2027 onwards, sales targets of Super One are anticipated to include both BEV and HEREV (hybrid extended range EV) models, driving its volume to about 4,900 units in 2027, 18,000 units in 2028, 38,000 units in 2029, and 55,000 units in 2030.

Regulatory and compliance certifications for Super One are progressing smoothly and on schedule. Key component certifications have almost been completed, laying out a solid foundation for full vehicle certification. The Company will complete vehicle-level EPA, CARB, and FMVSS-related homologation as planned by the third quarter of this year.

To support the delivery milestones, FX will complete the build-out of the after-sales and charging services in Q2, including the underlying systems and operational framework. FF recently signed an agreement with Tesla that enables future FX vehicles to have direct access to Tesla’s Supercharger network.

Major Forecast for Product Deliveries – Five Year Business Plan

FF’s cumulative five-year production and sales target is 400,000-500,000 vehicles, primarily driven by FX Super One, a planned FX 4 model, and other planned models. Key target markets include the United States and the Middle East, where FX Super One deliveries have already begun. Other high-value markets and additional models under the FX brand are also currently under consideration. 

Based on its baseline business plan, the Company is targeting to produce and sell approximately 250 units in 2026, scaling to approximately 5,000 units in 2027, and a ramp up more than approximately 22,000 units in 2028, approximately 130,000 units in 2029, and approximately 250,000 units in 2030 aligned with its model introductions and production volumes.    

The Company aims to achieve positive earnings before interest, taxes, depreciation and amortization within three years, with an estimated target gross margin rate of 20%. 

The Company believes it can achieve these goals underpinned by strong market demand. In addition, FF has made substantial progress across operational and regulatory milestones. This also includes advanced engagement and readiness with key suppliers; meaningful completion of initial homologation activities; and significant assembly readiness progress at its Hanford manufacturing facility, engineering validation, initial crash testing, battery safety certification, software compliance, and other regulatory requirements. These activities have been executed via a combination of in-house capabilities and independent third-party validation.

Over the next twelve months, a primary focus will be disciplined execution and timely delivery of vehicles to the Company’s customers, while maintaining flexibility. FF believes this approach positions itself to mitigate risk and advance steadily toward the objectives outlined in its five-year business outlook.

The Company believes that the successful validation of FF’s business model through scaled production volumes will enable further improvements in operational efficiency, potential access to diversified funding sources, and the development of additional strategic partnerships to support long-term growth.

Embodied AI Robotics

Today, FF also announced the unveiling of a new product category under its upgraded Global EAI Industry Bridge Strategy—Embodied AI Robotics. This expansion could further solidify the foundation of FF’s EAI ecosystem and raise its long-term growth ceiling, creating greater value for its stockholders and retail investors.

AI is entering large-scale deployment and commercialization. EAI is emerging as a strategic direction with the most profound industrial impact and long-term value in this cycle. FF firmly believes that the upcoming FF Global EAI Industry Bridge Strategy will deliver unique and differentiated value to FF and the entire industry.

With this move, FF looks to take the lead in opening a new AI frontier—starting in the United States. The goal is clear: to become a leading U.S. robotics company and one of the first U.S. companies to deliver humanoid robot products to the market. This dual-track growth model, driven by both EAI vehicles and EAI robotics, could define a new growth curve for Faraday Future. 

On February 4, in Las Vegas, FF will hold the FF AI Robotics U.S. Final Launch at the National Automobile Dealers Association (NADA) Show. FF plans to unveil its first embodied AI robotics products, open public product experiences, and begin sales at the same time. 

During the NADA Show, FF will also host the first FX Partner (FX Par) Summit. This will give partners the opportunity not only to join the FX vehicle sales and co-creation network, but also to become early FX Par partners of FF AI Robotics. FF welcomes dealers from the automotive and technology industries to register for the event, and join the Company’s FF Par network, as it works together to open the grand future for EAI EV and AI Robotics.

“The year 2026 is the Year of the Horse, and I hope that it will also be the year for FF to gallop full speed ahead and ride to victory,” said YT Jia. “We will work extremely diligently and fight tooth and nail for the successful production, delivery, and ramp-up of FX Super One as we mark a new era of EAI vehicles and EAI robotics.”

ABOUT FARADAY FUTURE 

Faraday Future is a California-based global shared intelligent electric mobility ecosystem company. Founded in 2014, the Company’s mission is to disrupt the automotive industry by creating a user-centric, technology-first, and smart driving experience. Faraday Future’s flagship model, the FF 91, exemplifies its vision for luxury, innovation, and performance. The FX strategy aims to introduce mass production models equipped with state-of-the-art luxury technology similar to the FF 91, targeting a broader market with middle-to-low price range offerings. FF is committed to redefining mobility through AI innovation. Join us in shaping the future of intelligent transportation. For more information, please visit https://www.ff.com/ 

FORWARD LOOKING STATEMENTS 

This press release includes “forward looking statements” within the meaning of the safe harbor provisions of the United States Private Securities Litigation Reform Act of 1995. When used in this press release, the words “plan to,” “can,” “will,” “should,” “future,” “potential,” and variations of these words or similar expressions (or the negative versions of such words or expressions) are intended to identify forward-looking statements. These forward-looking statements, including those relating to FFAI’s five-year business plan and targets (including but not limited to annual targets, milestones and contribution margins), the FX Super One and related production and delivery, and entry into the Embodied AI Robotics market involve a number of known and unknown risks, uncertainties, assumptions and other important factors, many of which are outside the Company’s control, which could cause actual results or outcomes to differ materially from those discussed in the forward-looking statements.  

Important factors, among others, that may affect actual results or outcomes include, among others: the Company’s ability to maintain its listing on Nasdaq; the availability of sufficient share capital to execute on its strategy, which the Company currently lacks; the agreement of stockholders to substantially increase the Company’s share capital, which could result in substantial additional dilution; the Company’s Board of Directors’ approval of various production and sales plans and proposals, which the Company may fail to obtain; the Company’s ability to homologate FX vehicles for sale; the Company’s ability to secure the necessary funding to execute on the FX strategy, which will be substantial; the Company’s ability to enter into an engineering services agreement, which will be required for the Super One in the U.S.; the ability of B2B preorder companies to identify purchasers for the Super One; overall demand for the Super One; the ability to secure the necessary agreements to produce an FX 4 vehicle or any other planned future FX vehicles, none of which have been secured; the Company’s ability to secure an occupancy certificate for its Hanford facility; the Company’s ability to continue as a going concern and improve its liquidity and financial position; the Company’s ability to pay its outstanding obligations; the Company’s ability to remediate its material weaknesses in internal control over financial reporting and the risks related to the restatement of previously issued consolidated financial statements; the Company’s ability to successfully compete in the robotics business against other companies that have substantially greater funding, experience and name recognition; the Company’s limited operating history and the significant barriers to growth it faces; the Company’s history of losses and expectation of continued losses; the success of the Company’s payroll expense reduction plan; the Company’s ability to execute on its plans to develop and market its vehicles and the timing of these development programs; the Company’s estimates of the size of the markets for its vehicles and cost to bring those vehicles to market; the rate and degree of market acceptance of the Company’s vehicles; the Company’s ability to cover future warranty claims; the success of other competing manufacturers; the performance and security of the Company’s vehicles; current and potential litigation involving the Company; the Company’s ability to receive funds from, satisfy the conditions precedent of and close on the various financings described elsewhere by the Company; the result of future financing efforts, the failure of any of which could result in the Company seeking protection under the Bankruptcy Code; the Company’s indebtedness; the Company’s ability to cover future warranty claims; the Company’s ability to use its “at-the-market” program; insurance coverage; general economic and market conditions impacting demand for the Company’s products; potential negative impacts of a reverse stock split; potential cost, headcount and salary reduction actions may not be sufficient or may not achieve their expected results; circumstances outside of the Company’s control, such as natural disasters, climate change, health epidemics and pandemics, terrorist attacks, and civil unrest; risks related to the Company’s operations in China; the success of the Company’s remedial measures taken in response to the Special Committee findings and certain of its key executives’ receipt of “Wells Notices” from the SEC and any potential SEC enforcement action related thereto; the Company’s dependence on its suppliers and contract manufacturer; the Company’s ability to develop and protect its technologies; the Company’s ability to protect against cybersecurity risks; and the ability of the Company to attract and retain employees, any adverse developments in existing legal proceedings or the initiation of new legal proceedings, and volatility of the Company’s stock price. You should carefully consider the foregoing factors and the other risks and uncertainties described in the “Risk Factors” section of the Company’s Form 10-K filed with the SEC on March 31, 2025, and Form 10-Qs for the quarters ended June 30, 2025 and September 30, 2025 filed with the SEC on May 9, 2025, August 19, 2025 and November 21, 2025, respectively, and other documents filed by the Company from time to time with the SEC. 

CONTACTS:
Investor Relations (English): steven.park@ff.com 
Investors (Chinese): cn-ir@faradayfuture.com  
Media: john.schilling@ff.com 

Cision View original content to download multimedia:https://www.prnewswire.com/news-releases/faraday-future-announces-the-fx-super-one-roadmap-for-mass-production-sales-delivery-service-and-ramp-up-and-its-entry-into-embodied-ai-robotics-along-with-its-execution-plan-for-ffs-five-year-business-plan-302655972.html

SOURCE Faraday Future

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Summarize Any Stock’s Earnings Call in Seconds Using FMP API

Summarize Any Stock’s Earnings Call in Seconds Using FMP API

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: import re# Example: using the transcript_text we fetched earliertext = transcript_text# Remove extra spaces and line breaksclean_text = re.sub(r'\s+', ' ', text).strip()# Split sections (this is a heuristic; real-world transcripts vary slightly)if "Question-and-Answer" in clean_text: prepared, qna = clean_text.split("Question-and-Answer", 1)else: prepared, qna = clean_text, ""print("Prepared Remarks Preview:\n", prepared[:500])print("\nQ&A Preview:\n", qna[:500]) 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: 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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