SES AI Corp (SES) 2024 Q2 法說會逐字稿

Good afternoon. Thank you for attending today's SES AI second-quarter 2024 business and financial results. My name is Cole, and I'll be the moderator for today's call. (Operator Instructions)

午安.感謝您參加今天的 SES AI 2024 年第二季業務和財務業績。我叫科爾，我將擔任今天電話會議的主持人。（操作員指示）

I'd now like to pass it over to Kyle Pilkington. Please go ahead.

現在我想把它交給凱爾皮爾金頓。請繼續。

Hello, everyone, and welcome to our conference call covering our second-quarter 2024 results. Joining me today are Qichao Hu, Founder, Chairman, and Chief Executive Officer; and Jing Nealis, Chief Financial Officer.

大家好，歡迎參加我們的 2024 年第二季業績電話會議。今天與我一起出席的還有創辦人、董事長兼執行長胡啟超和財務長 Jing Nealis。

We issued our shareholder letter after market close today, which provides a business update, as well as our financial results. You'll find a press release with a link to our shareholder letter and today's conference call webcast in the Investor Relations section of our website at ses.ai.

我們在今天收盤後發布了致股東的信，其中提供了業務更新以及財務表現。您可以在我們網站 ses.ai 的投資者關係部分找到新聞稿，其中包含我們致股東的信函和今天的電話會議網絡直播的連結。

Before we get started, this is a reminder that the discussion today may contain forward-looking information or forward-looking statements within the meaning of applicable securities legislation. These statements are based on our predictions and expectations as of today. Such statements involve certain risks, assumptions, and uncertainties, which may cause our actual and future results and performance to be materially different from those expressed or implied in these statements.

在我們開始之前，需要提醒的是，今天的討論可能包含適用證券立法含義內的前瞻性資訊或前瞻性陳述。這些聲明是基於我們今天的預測和預期。該等聲明涉及某些風險、假設和不確定性，可能導致我們的實際和未來結果和表現與該等聲明中明示或暗示的結果和表現有重大差異。

The risks and uncertainties that could cause our results to differ materially from our current expectations include, but are not limited to those detailed in our latest earnings release and in our SEC filings. This afternoon, we will review our business, as well as results for the quarter.

可能導致我們的結果與目前預期有重大差異的風險和不確定因素包括但不限於我們最新的收益報告和美國證券交易委員會文件中詳細說明的風險和不確定因素。今天下午，我們將回顧我們的業務以及本季的業績。

With that, I'll pass it over to Qichao.

這樣，我就把這個任務交給啟超了。

Good afternoon, and thank you for joining us on our second-quarter earnings call. I want to talk about the seismic shift across any industry by generative AI and large language models, LLM. AI represents a pivotal development of this decade. This transformative technology is set to disrupt industries from those seeking the next innovation S-curve to those grappling with shrinking margins.

下午好，感謝您參加我們的第二季財報電話會議。我想談談生成式人工智慧和大型語言模型、法學碩士 (LLM) 為任何行業帶來的巨大轉變。人工智慧代表了本十年的關鍵發展。這項變革性技術將顛覆各個產業，從尋求下一個創新 S 曲線的產業到正在努力應對利潤率縮水的產業。

The fact is, today's EV battery market is completely different from that of three years ago or even just one year ago. The incumbent battery players now dominate the global market. The next-generation battery companies must deliver something completely different in light years ahead to become relevant. We cannot compete on their terms.

事實上，今天的電動車電池市場與三年前甚至一年前完全不同。目前，現有的電池廠商佔據全球市場的主導地位。下一代電池公司必須在未來幾年內推出完全不同的產品才能立於不敗之地。我們無法按照他們的條件進行競爭。

Previously, we announced that we are entering the air mobility market, including urban air mobility, or UAM, in drones, in addition to our existing EV work. For next-gen batteries to compete with incumbent batteries, we must overcome three hurdles at commercial scale; quality, safety, and future material development. The traditional human-based approach simply takes too long. That's why the introduction of next-gen battery technologies has always been very slow.

先前，我們宣布除了現有的電動車業務外，還將進軍空中交通市場，包括無人機城市空中交通（UAM）。為了使下一代電池與現有電池競爭，我們必須克服商業規模上的三個障礙：品質、安全和未來材料開發。傳統的以人為本的方法實在太耗時了。這就是為什麼下一代電池技術的推出一直非常緩慢的原因。

We are the world's leader in lithium metal. We were the world's first to enter automotive A-sample and B-sample joint development agreements with global automakers. We have developed very exciting capabilities in materials and manufacturing. We have strategically integrated AI into our operations, encompassing design, technology development, manufacturing, and aftermarket support.

我們是全球鋰金屬領域的領導者。全球首家與全球汽車廠商簽訂汽車A樣、B樣聯合開發協議。我們在材料和製造方面已經開發出非常令人興奮的能力。我們已將人工智慧策略性地融入我們的營運中，涵蓋設計、技術開發、製造和售後支援。

Since embarking on embedding AI into lithium metal, we have realized that the value of AI materializes when it fundamentally reshapes the business model. By adopting a thematic approach with platform building mindset, we aim to generate both internal and external value. We've worked diligently to achieve this and are excited to share the preliminary outcomes of our initiatives.

自從著手將人工智慧嵌入鋰金屬以來，我們意識到當人工智慧從根本上重塑商業模式時，其價值就會實現。透過採用主題方法和平台建立思維，我們旨在創造內部和外部價值。我們為實現這一目標付出了不懈努力，並很高興與大家分享我們舉措的初步成果。

Today, we're introducing a paradigm shift. Our AI solutions will accelerate the commercialization of all next-gen battery technologies. Lithium metal represents the forefront of this new approach. But our AI will ultimately be agnostic to any battery technology.

今天，我們引入一種範式轉移。我們的人工智慧解決方案將加速所有下一代電池技術的商業化。鋰金屬代表了這種新方法的前沿。但我們的人工智慧最終將不受任何電池技術的影響。

Let's start with the EV sector. Last quarter, we announced our B-Sample joint development partnership with Hyundai to build a line within their Electrification Center in Ui-Wang, South Korea. I'm glad to share that we're on track to hit our target of completion of the line in the fourth quarter of this year. This will yield one of the largest capacity lithium metal lines globally and will manufacture 50-amp power to 100-amp power large automotive lithium metal B-sample cells. We continue to work with our automotive OEMs with a goal to reach EVC sample in 2025 and start of production, SOP, in 2026.

讓我們從電動車領域開始。上個季度，我們宣布與現代汽車建立 B-Sample 聯合開發合作夥伴關係，在其位於韓國義王的電氣化中心內建立一條生產線。我很高興地告訴大家，我們預計在今年第四季實現完成該生產線的目標。這將產生全球最大容量的鋰金屬生產線之一，並將生產50安培至100安培功率的大型汽車鋰金屬B樣品電池。我們將繼續與汽車原始設備製造商合作，目標是在 2025 年獲得 EVC 樣品，並在 2026 年開始生產 SOP。

For UAM and drones, we continue to see strong demands. For UAM, we are converting our previous EV-A sample lines in South Korea and Shanghai to UAM lines. We expect the Korea UAM line to complete field acceptance test, FAT, in August; site acceptance test, SAT, in September, and start producing cells in September. We expect the Shanghai UAM line to complete both FAT and SAT in September and start producing cells in October.

對於 UAM 和無人機，我們持續看到強勁的需求。對於 UAM，我們正在將先前位於韓國和上海的 EV-A 樣品線轉換為 UAM 線。我們預計韓國UAM生產線將於8月完成現場驗收測試（FAT）；9月份完成場地驗收測試（SAT），並於9月開始生產電池。我們預計上海UAM生產線將於9月完成FAT和SAT，並於10月開始生產電池。

Both UAM lines will make 20-amp power to 30-amp power medium lithium metal cells and modules. We're making great progress testing these lithium metal modules based on the rigorous safety test for aviation certification. We have already entered a few cell testing agreements with leading UAM, OEMs, and expect to enter a few more later this year.

兩條 UAM 生產線都將生產 20 安培至 30 安培的中型鋰金屬電池和模組。我們在基於航空認證的嚴格安全測試對這些鋰金屬模組的測試中取得了重大進展。我們已經與領先的 UAM、OEM 簽訂了一些小區測試協議，並預計今年稍後將簽訂更多協議。

For drones, we're seeing growing demand from both industrial and defense customers, especially for small swarm drones. The drone market was estimated to be $28 billion in 2023, according to SkyQuest. About 1.8x, the $16 billion estimated market size, for AR/VR goggles in 2023 according to Consegic Intelligence. We have already converted our small cell lines to make 4 amp power to 6 amp power small lithium metal cells and modules.

對於無人機，我們看到工業和國防客戶的需求不斷增長，尤其是對小型群體無人機。據 SkyQuest 稱，到 2023 年，無人機市場規模預計將達到 280 億美元。根據 Consegic Intelligence 的數據，到 2023 年，AR/VR 眼鏡的市場規模約為 160 億美元，即 1.8 倍。我們已經將小型電池生產線轉換為生產 4 安培至 6 安培功率的小型鋰金屬電池和模組。

Now let's talk about our AI solutions. We have three; AI for manufacturing, AI for safety, and AI for science. First, AI for manufacturing. The traditional approach to optimizing cell design and process and improving manufacturing quality is through human experience, where the human engineers define and optimize quality specifications, typically takes at least eight years.

現在我們來談談我們的人工智慧解決方案。我們有三個：用於製造的人工智慧、用於安全的人工智慧和用於科學的人工智慧。第一，製造業人工智慧。優化電池設計和工藝以及提高製造品質的傳統方法是透過人類經驗，其中人類工程師定義和優化品質規格，通常需要至少八年時間。

Battery manufacturing is often more of an art than a science, especially between the good ones and the very best. While this human-based approach has worked well in the past and works today for mature lithium ion cell technologies, it slows down large-scale commercialization of next-gen battery technologies. We believe AI for manufacturing can accelerate this timeline by 10x. It uses machine learning to define and fine tune quality specifications based on manufacturing process data collected, which is much faster and more accurate than human engineers.

電池製造往往是一門藝術而非科學，尤其是優質電池和最優秀的電池製造。雖然這種以人為本的方法在過去效果很好，並且現在對成熟的鋰離子電池技術仍然有效，但它減緩了下一代電池技術的大規模商業化。我們相信，製造業的人工智慧可以將這一時間表加快 10 倍。它使用機器學習根據收集的製造流程資料來定義和微調品質規範，這比人類工程師更快、更準確。

Our EV-B sample, UAM, and drone lines produce an enormous amount of data, the largest manufacturing data of lithium metal cells anywhere in the world. We produce more than 1,000 cells per line, per month, and growing. There are more than 1,000 quality checkpoints per cell and growing, including both time series data and images, such as CT, X-ray, ultrasound, and vision. There are thousands of process steps with complex individual and group relationships. Our AI for Manufacturing model has already been pre-trained on more than 15,000 lithium metal cells.

我們的 EV-B 樣品、UAM 和無人機生產線產生了大量數據，這是世界上最大的鋰金屬電池製造數據。我們每個細胞株每月可生產超過 1,000 個細胞，數量還在增加。每個細胞有超過 1,000 個品質檢查點，並且還在增加，包括時間序列資料和影像，例如 CT、X 光、超音波和視覺。流程步驟數千個，個人與團體關係複雜。我們的製造 AI 模型已經對超過 15,000 個鋰金屬電池進行了預訓練。

We're very excited to announce the installation of AI for manufacturing on all of our working metal lines, from EV-B sample to UAM to small drones. We expect it will provide very detailed and accurate individual step quality analysis and group of steps relationship analysis. This will further accelerate the optimization of manufacturing quality, preparing us for EV-C sample and larger scale UAM and drone manufacturing.

我們非常高興地宣布，我們所有的金屬生產線都安裝了用於製造的人工智慧，從 EV-B 樣品到 UAM 再到小型無人機。我們期望它能提供非常詳細和準確的單一步驟品質分析和步驟組關係分析。這將進一步加速製造品質的最佳化，為EV-C樣品和更大規模的UAM和無人機製造做好準備。

In addition to in-health AI for manufacturing development, we also partner with big tech companies and plan to incorporate the latest AI for manufacturing approach from the semiconductor industry. We continue to work with our automotive OEMs with a goal to reach EV-C sample in 2025 and SOP in 2026. This AI for manufacturing capability allows us to bring enormous value to our other OEM and large battery manufacturing partners.

除了醫療領域的人工智慧製造業發展外，我們還與大型科技公司合作，計劃融入半導體產業最新的人工智慧製造業方法。我們將繼續與汽車原始設備製造商合作，目標是在 2025 年獲得 EV-C 樣品並在 2026 年達到 SOP。這種用於製造的人工智慧使我們能夠為其他 OEM 和大型電池製造合作夥伴帶來巨大的價值。

Second, AI for safety. Traditional vehicle battery health monitoring and safety prediction are based on a set of boundary conditions determined by humans, physics-based models. These would include, for example, state of health, SOH; state of charge, SOC; capacity, voltage, temperature, current, time, to name a few. While the boundary conditions are well understood by humans, there are not enough to actually predict battery remaining useful life and incidents.

第二，人工智慧用於安全。傳統的車輛電池健康監測和安全預測是基於一組由人為確定的邊界條件、基於物理的模型。例如，這些包括健康狀態 (SOH)、充電狀態 (SOC)、容量、電壓、溫度、電流、時間等等。雖然人類很好地理解了邊界條件，但實際上並不足以預測電池剩餘使用壽命和事故。

AI is far more accurate and powerful at detecting anomalies than even the best human engineers. In AI for safety, rather than relying solely on human-developed boundary conditions, we have pre-trained our LLM with a cell cycling data of more than 15,000 lithium metal cells under various mission profiles, including more than 100 actual flight hours of drones using our lithium metal modules.

人工智慧在檢測異常方面比最好的人類工程師更準確和強大。在安全人工智慧方面，我們不再僅僅依賴人類制定的邊界條件，而是使用各種任務條件下超過 15,000 個鋰金屬電池的電池循環數據對我們的 LLM 進行了預先訓練，其中包括使用我們的鋰金屬模組的無人機超過 100 小時的實際飛行時間。

Interestingly, the LLM identifies features that can detect anomalies and send early warning signals far more accurately. These AI-developed features work remarkably, and we are working on improving the explainability of these models.

有趣的是，LLM 識別出可以偵測異常並更準確地發送預警訊號的特徵。這些人工智慧開發的功能效果顯著，我們正在致力於提高這些模型的可解釋性。

With more vehicle battery data training, we believe that AI for safety can help guarantee near 100% safety in the field addressing the core issue of lithium metal and all next-gen batteries with higher energy densities, which is safety.

隨著車輛電池資料訓練的增多，我們相信安全人工智慧可以幫助確保現場接近 100% 的安全性，解決鋰金屬和所有具有更高能量密度的下一代電池的核心問題，即安全性。

In working with our OEM partners, our AI for safety model has been able to predict 100% of more than 40 incidents. Our model predicted the incidents 10 to 30 cycles earlier than they occurred and sent warning signals. We also continue the cycle test until the actual incidents to verify the prediction accuracy. In comparison, our human-based models were only able to predict around 80% of incidents.

在與我們的 OEM 合作夥伴合作的過程中，我們的安全 AI 模型已經能夠 100% 預測 40 多起事故。我們的模型比事件發生時間提早10到30個週期預測到了事件並發出了警訊。我們也將繼續循環測試直至實際事件發生，以驗證預測的準確性。相較之下，我們的基於人類的模型只能預測大約 80% 的事件。

Third, AI for science. Human research and development on battery materials has been the single slowest step in commercialization of next-gen battery technologies. For example, the entire global lithium ion industry spent 30 years studying less than 1,000 unique molecules, when there are 100 billion, that's 10th to the 11th, unique molecules that could be studied and should be studied.

第三，人工智慧用於科學。人類對電池材料的研究和開發是下一代電池技術商業化過程中最緩慢的一步。例如，整個全球鋰離子產業花了 30 年研究不到 1,000 種獨特的分子，而實際上有 1000 億種，也就是第 10 到第 11 種獨特的分子可以研究，也應該研究。

On average, it takes human scientists 10 years to introduce a new battery material. We believe AI for science can do that in one year. Unlike AI for manufacturing and safety that collect actual data from the lines and vehicles, AI for science requires an enormous molecular property database that currently does not exist, synthesizing this property database requires massive computing power.

人類科學家平均需要10年才能推出一種新的電池材料。我們相信，科學人工智慧可以在一年內實現這一目標。與從生產線和車輛收集實際數據的製造和安全人工智慧不同，科學人工智慧需要一個目前尚不存在的龐大的分子特性資料庫，合成該特性資料庫需要龐大的運算能力。

Recently, we started a new initiative called Molecular Universe, whose goal is to crowdsource subsidized and free computing resource to map the properties of small molecules. Several universities, national labs, and big tech companies have participated in this initiative, and we have already mapped about 10 to the sixth molecules.

最近，我們啟動了一項名為「分子宇宙」的新計劃，目標是透過眾包補貼和免費計算資源來繪製小分子的特性。多所大學、國家實驗室和大型科技公司參與了這項計劃，我們已經繪製了大約 10 的六次方個分子。

With more GPUs, we expect to map a large enough molecular universe that our AI model training will reach sufficient accuracy. Once we have this map, we can accelerate material discovery for any battery problem. This includes, not just lithium metal for EV, UAM, and drones, but also lithium ion batteries for consumer electronics, power tools, automotive, and other applications.

有了更多的 GPU，我們期望繪製一個足夠大的分子宇宙，以便我們的 AI 模型訓練達到足夠的精度。一旦我們有了這張地圖，我們就可以加速解決任何電池問題的材料發現。這不僅包括用於電動車、城市空中交通和無人機的鋰金屬，還包括用於消費性電子產品、電動工具、汽車和其他應用的鋰離子電池。

Most of these molecules are completely new and not commercially available. That's why we built Electrolyte Foundry, which has been operational since April this year. This Electrolyte Foundry employs some of the best organic synthesis chemists in the world. Now, we have complete ability from molecular mapping to generative AI models for new molecules, to molecular synthesis and purification, to high throughput electrolyte formulation screening, and to small and large cell testing. No one in the battery industry has such incomplete capability.

這些分子大多數都是全新的並且尚未在商業上獲得。這就是我們建造電解質鑄造廠的原因，該鑄造廠自今年四月起開始運作。該電解質鑄造廠僱用了世界上一些最優秀的有機合成化學家。現在，我們擁有從分子圖譜到新分子生成AI模型、分子合成與純化、高通量電解質配方篩選、小細胞和大細胞測試的完整能力。電池行業中沒有人擁有如此不完整的能力。

So how do we monetize all this? These three AI solutions represent what we expect to be exciting and sooner than expected revenue streams, as well as the future of electric transportation.

那我們要如何將這一切貨幣化呢？這三種人工智慧解決方案代表了我們期待的令人興奮且比預期更快的收入流，以及電動交通的未來。

In AI for manufacturing and safety, to truly ensure near 100% safety in the field, manufacturing quality and vehicle safety data must be integrated. Here's where SES AI comes in. Our lithium metal cells for EV, UAM, and drones will be the first time that manufacturing and safety data are integrated to ensure near 100% safety. We're also working with some of our peers in both next-gen lithium ion and lithium metal batteries to consolidate manufacturing and safety data for our model training. The larger and more diverse the data, the more accurate the models become.

在製造和安全人工智慧中，為了真正確保現場接近 100% 的安全，必須整合製造品質和車輛安全資料。這就是 SES AI 的作用所在。我們用於電動車、城市空中交通和無人機的鋰金屬電池將首次整合製造和安全數據，以確保接近 100% 的安全性。我們也與下一代鋰離子和鋰金屬電池領域的一些同行合作，整合用於模型訓練的製造和安全資料。數據越大、越多樣化，模型就越準確。

We expect the pricing could be structured as a premium valid for the entire warranty period. The value proposition for these OEMs is that incident prediction can prevent costly recalls, and more accurate remaining useful life prediction can help extend battery lifetime.

我們預計定價可以建構為整個保固期內有效的溢價。這些 OEM 的價值主張是，事件預測可以避免代價高昂的召回，而更準確的剩餘使用壽命預測可以幫助延長電池壽命。

In AI for science, SES AI has the strongest battery electrolyte development capability. Many battery companies and OEMs do not have the resource to develop good electrolyte materials. We can in-source intelligence and help them solve their challenges.

在科學人工智慧領域，SES AI擁有最強的電池電解質開發能力。許多電池公司和原始設備製造商沒有資源開發良好的電解質材料。我們可以提供情報並幫助他們解決挑戰。

We will start by seeking to beat the lithium metal electrolyte columbic efficiency record set by human scientists. We will then expand to lithium ion applications, such as low temperature performance, and fast charge, non-volatility, and expand from automotive to consumer electronics to grid storage and many other applications.

我們首先要尋求打破人類科學家創造的鋰金屬電解質庫倫效率記錄。然後我們將擴展到鋰離子應用，例如低溫性能，以及快速充電，非揮發性，並從汽車擴展到消費性電子到電網儲存和許多其他應用。

This type of in-source intelligence for the AI for science business model can find an analogy in the pharmaceutical industries that enjoy much higher profit margins. The pricing structure may be based on a development fee and recurring licensing royalty. We have been applying this to lithium metal material discovery and expect to apply to lithium ion material discovery.

這種針對科學人工智慧商業模式的內部情報可以在利潤率較高的製藥產業中找到類似之處。定價結構可能基於開發費和經常性許可費。我們一直將其應用於鋰金屬材料的發現，並期望應用於鋰離子材料的發現。

So we're going all in on AI. AI is changing everything. Our AI for manufacturing, AI for safety, and AI for science models are accelerating the commercialization, time to revenue, and profitability of lithium metal for EV, UAM, and drones. But they can also be applied to the broader within IR applications.

所以我們將全心投入人工智慧。人工智慧正在改變一切。我們的製造人工智慧、安全人工智慧和科學人工智慧模型正在加速電動車、城市空中交通和無人機鋰金屬的商業化、創收時間和獲利能力。但它們也可以應用於更廣泛的紅外線應用。

Having navigated numerous industry cycles, I'm particularly proud of developing a technology from the ground up that many deem impossible. Our collaboration with a diverse portfolio of world-class customers further validates our efforts. However, I've never been more excited about our business than I am now with the integration of AI into every aspect of our operations. I firmly believe this will enable us to drive transformative change on a global scale. I am truly fortunate to be living in this exciting period in transportation, science, and AI.

經歷了無數的產業週期後，我特別自豪能夠從頭開始開發出許多人認為不可能的技術。我們與眾多世界一流客戶的合作進一步驗證了我們的努力。然而，隨著人工智慧融入我們營運的各個方面，我對我們的業務感到前所未有的興奮。我堅信這將使我們能夠推動全球範圍內的變革。我真的很幸運能夠生活在這個交通、科學和人工智慧領域的激動人心時代。

In addition to the vision we have outlined for our three AI solutions, our top priorities for the year remain focusing on capital efficiency, attracting top talent, continuing to make progress on delivering lithium metal cells to our EV, UAM, and drone partners, and leading the AI transformation of the battery industry.

除了我們為三大人工智慧解決方案概述的願景之外，我們今年的首要任務仍然是專注於資本效率、吸引頂尖人才、繼續向我們的電動車、城市空中交通和無人機合作夥伴提供鋰金屬電池，並引領電池產業的人工智慧轉型。

Thank you for continuing interest in SES AI. And now, I want to turn it over to Jing for financials.

感謝您對 SES AI 的持續關注。現在，我想將財務事宜交給 Jing。

Thank you, Qichao. Today, I will cover our second quarter of 2024 financial results and discuss our operating and capital budgets for the full-year 2024.

謝謝你，啟超。今天，我將介紹我們 2024 年第二季的財務業績，並討論我們 2024 年全年的營運和資本預算。

In the second quarter, our gap operating expenses were $24.6 million. Cash used in operations was $22.1 million. And capital expenditures were $3.7 million. We ended the second quarter with $294.7 million in liquidity.

第二季度，我們的差額營運費用為 2,460 萬美元。營運所用現金為 2,210 萬美元。資本支出為 370 萬美元。截至第二季末，我們的流動資金為 2.947 億美元。

As we continue to be very prudent with our cash and management of expenditures, we updated our full-year 2024 guidance. We now expect total cash usage to be in the range of $100 million to $120 million, down from $110 million to $130 million previously.

由於我們繼續非常謹慎地使用現金和管理支出，我們更新了 2024 年全年指引。我們現在預計總現金使用量將在 1 億至 1.2 億美元之間，低於先前的 1.1 億至 1.3 億美元。

This range is comprised of cash usage from operations of $85 million to $95 million, compared with $90 million to $100 million previously, and capital expenditures in the range of $15 million to %25 million, compared with 20 million to 30 million previously. We expect our strong balance sheet to provide liquidity for the company well into 2027.

這一範圍包括營運現金使用量為 8,500 萬美元至 9,500 萬美元（之前為 9,000 萬美元至 1 億美元）和資本支出為 1,500 萬美元至 2,500 萬美元（之前為 2,000 萬美元至 3,000 萬美元）。我們預計強勁的資產負債表將在 2027 年為公司提供流動性。

Going forward in C-Sample and beyond, we expect to share capacity buildup capital expenditures with our OEM partners. UAM/drones and our AI solutions could provide potential upside to earlier commercialization.

展望 C-Sample 及以後的發展，我們期望與我們的 OEM 合作夥伴分享產能建設資本支出。UAM/無人機和我們的 AI 解決方案可以為早期的商業化提供潛在的優勢。

With that, I'll hand the call back to the operator to open up for questions.

說完這些，我會將電話交回給接線生，以便回答問題。

(Operator Instructions) Jed Dorsheimer, William Blair.

（操作員指示）傑德·多爾斯海默、威廉·布萊爾。

Hi. We have Mark Shooter on Jed. Qichao, I'd like to hear what incremental data you've seen on AI to really push for this all in approach. I know you've been working on these AI applications in the background for some time. But what was so incrementally positive here to really push this strategy shift?

你好。馬克舒特 (Mark Shooter) 負責傑德 (Jed)。齊超，我想聽聽您在人工智慧方面看到了哪些增量數據，以真正推動這種全方位的方法。我知道您已經在後台研究這些人工智慧應用程式有一段時間了。但是，這裡究竟有哪些正面因素真正推動了這項策略轉變呢？

Hey, Mark. So really, in all three areas, we started working on these three AIs. AI for safety really back in 2017, and then AI for manufacturing really towards the end of A-sample, beginning of B-sample, so towards the end of A-sample, and now as we begin B sample.

嘿，馬克。因此，實際上，我們在這三個領域都開始研究這三種人工智慧。用於安全的人工智慧實際上可以追溯到 2017 年，然後用於製造的人工智慧實際上出現在 A 樣本的末尾、B 樣本的開頭，所以在 A 樣本即將結束時，現在我們開始使用 B 樣本。

With more data and then in manufacturing, we found once we hit about 1,000 quality checkpoints per cell and then get about 1,000 cells per month per line, it's actually really helpful. And then, because when we make a new cell design, the human engineers don't -- so you start with a new cell design.

有了更多的數據，然後在製造過程中，我們發現，一旦我們達到每個電池約 1,000 個品質檢查點，然後每條生產線每月可生產約 1,000 個電池，這實際上非常有幫助。然後，因為當我們進行新的細胞設計時，人類工程師不會——所以你從一個新的細胞設計開始。

Basically, you have no experience. You have no idea what quality specs to use. So the traditional process really is just too slow.

基本上你沒有經驗。您不知道要使用什麼品質規格。所以傳統的流程確實太慢了。

And then we started applying AI models. And then, first, we collected all this data. And then the model would actually recommend very interesting quality specs. And then we started seeing this, I would say, towards the end of last year and then beginning of this year.

然後我們開始應用人工智慧模型。然後，首先，我們收集了所有這些數據。然後模型實際上會推薦非常有趣的品質規格。然後我們開始看到這種情況，我想說，是在去年年底和今年年初。

So instead of human engineers trying lots of experiments and then figuring out, okay, the optimal electrical amount is 2 grams per amp power, or 1, or the optimum gap between cathode and anode is 1.5 millimeters, actually, this AI model is actually going to rank all the quality issues for you. And then tell you, so this one -- for example, the pressure during hot press on the Jolly Roll has a bigger impact than your ceiling temperature.

因此，人類工程師無需進行大量實驗，然後找出最佳電量是每安培功率 2 克或 1，或者陰極和陽極之間的最佳間隙是 1.5 毫米，實際上，這個 AI 模型會為您對所有品質問題進行排名。然後告訴你，例如，Jolly Roll 熱壓過程中的壓力比天花板溫度的影響更大。

And then, actually, it's going to tell you the relationships between all these steps, so that was shocking but in a really powerful way. So instead of the traditional way of improving manufacturing quality, this model was just like out-of-the-world powerful.

然後，實際上，它會告訴你所有這些步驟之間的關係，這雖然令人震驚，但卻非常強大。所以，與傳統的提升製造品質的方式不同，這種模式就像是超凡脫俗的強大。

And it still doesn't replace quality engineers. We still have good quality engineers from the big lithium ion industries, but it's a really helpful tool to supplement -- sorry, compliment the human engineers.

但它仍然不能取代品質工程師。我們仍然擁有來自大型鋰離子行業的優秀工程師，但它確實是一個有用的補充工具——抱歉，讚美人類工程師。

And then on the safety side. So we started training a large language model with all the cycling data, charge and discharge. And then actually, if you look at the charge and discharge curve, it's actually very much like a sentence. So you train a large language model.

然後是安全方面。因此，我們開始使用所有循環資料、充電和放電來訓練大型語言模型。實際上，如果你看一下充電和放電曲線，它實際上非常像一個句子。所以你訓練一個大型語言模型。

And then -- so we had several examples where -- and this is also another case where, now, we are in B-sample. And also we're testing against mission profiles for UAM and then drones.

然後 — — 我們有幾個例子 — — 這也是另一種情況，現在我們在 B 樣本中。我們也正在針對 UAM 和無人機的任務概況進行測試。

And then the traditional, the OEMs would have nine, sometimes more than a dozen physics-based models, like SOC, SOH, and then set those as boundary conditions. If any of those get triggered, then you have an alarm. But it takes a long time to develop that. That set of physics-based models, that only works for mature chemistries.

然後，傳統的 OEM 會有九個，有時甚至十幾個基於物理的模型，如 SOC、SOH，然後將它們設定為邊界條件。如果其中任何一個被觸發，那麼你就會收到警報。但要實現這一點需要很長時間。那套基於物理的模型只適用於成熟的化學反應。

Again, let's take the manufacturing. When you introduce a new cell chemistry, like none of the existing process -- I mean, the existing process is just too soft, but none of the existing set of metrics works. The manufacturing quality specs don't work. The physics-based models, those boundary conditions don't work.

我們再來看一下製造業。當你引入一種新的細胞化學時，就像現有的流程一樣——我的意思是，現有的流程太軟，但現有的指標都不起作用。製造品質規格不起作用。基於物理的模型，那些邊界條件不起作用。

So if you continue to use the traditional process, it will take too long. So then this large language model, actually, we had an example where the cell actually had an incident on a cycle of 170-something. And then none of the other physics-based models was able to predict anything before that. But this one AI model, this one large language model that actually found this feature, which we cannot explain today, that actually sent a warning on cycle 144, about 30 cycles before.

所以如果繼續使用傳統流程，將會耗費太久。那麼，在這個大型語言模型中，實際上，我們有一個例子，其中細胞實際上在 170 多個週期內發生了事件。在此之前，其他基於物理的模型都無法預測任何事情。但這個人工智慧模型，這個大型語言模型實際上發現了這個我們今天無法解釋的特徵，它實際上在第 144 個週期（大約 30 個週期之前）發出了警告。

So that's really powerful. And then -- so both quality manufacturing and then safety, it's like when you introduce a new cell design, your experience doesn't work anymore. Your existing set of metrics don't work anymore. So AI model will help you develop that much faster.

這真的很強大。然後——因此，無論是品質製造還是安全性，就像當你引入一種新的電池設計時，你的體驗就不再有效了。您現有的一套指標不再起作用。因此，AI 模型將幫助您更快地發展。

Then in AI for science, so we actually expanded our electrical team; both AI team and human scientists team. And then just since end of last year, our AI model was actually able to find 17 new molecules. And then we actually standardized three of them, and then we're testing. And the performance so far are just as good as the molecules that the human scientists came out with in the past 10 years since 2012.

然後在人工智慧科學領域，我們實際上擴大了我們的電氣團隊；包括人工智慧團隊和人類科學家團隊。就在去年年底，我們的人工智慧模型實際上已經能夠找到 17 種新分子。然後我們實際上對其中三個進行了標準化，然後進行了測試。而且目前的表現和人類科學家自2012年以來近10年來研發出的分子一樣好。

And then this is only after having mapped 10th to the 6th, right? If we map 10th to the 8th, 10th to the 11th, we're pretty confident that we can find something that works better.

然後這只是在將第 10 位映射到第 6 位之後，對嗎？如果我們將第 10 位映射到第 8 位，將第 10 位映射到第 11 位，我們非常有信心可以找到更好的方法。

So I think these three signals that we found towards the end of last year and beginning of this year just made us convince, okay, if you want to introduce a new battery chemistry and then we're doing that at scale, B-sample, C-sample, why spend 8 years, why spend 10 years just improving the quality, improving the safety, when you can use the AI to do things much faster?

所以我認為我們在去年年底和今年年初發現的這三個訊號讓我們相信，如果你想引入一種新的電池化學成分，然後我們大規模地進行生產，B 樣品、C 樣品，為​​什麼要花 8 年、10 年的時間來提高品質、提高安全性，而你可以使用人工智慧更快地完成任務？

Well, that's great. I appreciate all the color there, Qichao. No, we hear a lot of time that AI is making software engineers, 10x engineers, but it sounds like you're applying AI to make your material scientists and your quality control engineers; 10x engineers. So that's great to hear.

嗯，那太好了。我很欣賞那裡所有的色彩，啟超。不，我們經常聽說人工智慧正在讓軟體工程師成為 10 倍工程師，但聽起來你正在應用人工智慧讓你的材料科學家和品質控制工程師成為 10 倍工程師。聽到這個消息我非常高興。

I'm particularly (multiples speakers) in what comes out of AI for science and the electrolyte space, because that is such a vast mapping that needs to occur. I agree there.

我特別（多位發言者）關注人工智慧對科學和電解質空間的影響，因為這是一個需要進行的龐大映射。我同意這一點。

Yeah.

是的。

One follow up about the EOM partners. I was thinking of -- sorry. How are the partners, specifically the EV EOM partners? How are they looking at this AI for manufacturing and the science -- I'm sorry, not for science, the safety. Are they looking at it as an attractive bonus that they currently don't have for traditional lithium ion? Or are they looking at it as a necessary proof point to convince them of the safety of a new chemistry that they're not comfortable with?

關於 EOM 合作夥伴的後續事宜。我在想——抱歉。合作夥伴，特別是 EV EOM 合作夥伴怎麼樣？他們如何看待人工智慧在製造業和科學領域的應用？抱歉，不是用於科學，而是用於安全。他們是否將其視為傳統鋰離子電池目前所不具備的有吸引力的優勢？或者他們將其視為一個必要的證據點，以使他們相信他們不熟悉的新化學物質的安全性？

Yeah. So it's two things. One is, it's a necessary approach to convince them of a new battery chemistry at commercial scale. We're not talking about R&D anymore, not A-sample. We're talking about B-sample and then C-sample. We're seriously talking about putting tens of thousands of cars with lithium metal battery in the field, with all kinds of users for EVs and UAMs.

是的。所以這是兩件事。一是，這是讓他們相信新型電池化學技術能實現商業規模應用的必要方法。我們不再談論研發，也不再談論 A 樣品。我們正在討論 B 樣本，然後是 C 樣本。我們正在認真討論將數萬輛配備鋰金屬電池的汽車投入使用，以滿足電動車和城市空中交通工具的各種用戶的需求。

So at this point, we need a lot of data, a lot of real-world experience, and also AI model to really guarantee safety. Because now, we're talking about not safety in the lab, but safety in the field. So one thing is necessity.

所以在這一點上，我們需要大量的數據、大量的現實世界經驗以及人工智慧模型來真正保證安全。因為現在，我們談論的不是實驗室的安全，而是現場的安全。所以有一件事是必要的。

Second is a lot of these automakers want to make their own batteries. And So far, the power is in the hands of the large battery manufacturers; the CATLs, the LGs of the world. So for the automakers to control their own destiny, they really need to quickly control battery. And then having access and having control to better manufacturing data and battery performance data in the vehicle is very powerful. It allows the automakers to quickly get up to speed, and then get to the same level of proficiency in terms of manufacturing quality and safety compared to the large body manufacturers.

其次，許多汽車製造商都想生產自己的電池。到目前為止，權力掌握在大型電池製造商、 CATL 和 LG 手中。因此，汽車製造商要掌握自己的命運，就必須迅速控制電池。然後，獲取並控制車輛中更好的製造數據和電池性能數據是非常有用的。它使汽車製造商能夠快速跟上步伐，然後在製造品質和安全方面達到與大型車身製造商相同的熟練程度。

So these two are really important for the OEMs. And then it's both for looking metal, but also for any next-gen lithium ion.

所以這兩者對 OEM 來說非常重要。它既適用於金屬，也適用於任何下一代鋰離子。

Thanks, Qichao. I appreciate it.

謝謝，啟超。我很感激。

(Operator Instructions) Shawn Severson, Water Tower Research.

（操作員指示） Shawn Severson，水塔研究。

Great. Thank you. Qichao, I just wanted to go back to the monetization of the AI. I mean, I think it's clear in the pathway for you simply been able to make a better lithium metal battery, right, with the information you have. And what I'm trying to understand is how does that model expand to the lithium ion industry, the OEMs? What you were talking about as far as uses and applications for AI, how does this get monetized outside of your own manufacturing?

偉大的。謝謝。齊超，我只想回到人工智慧貨幣化的話題。我的意思是，我認為透過你所掌握的信息，你就能製造出更好的鋰金屬電池，對吧。我想了解的是，該模型如何擴展到鋰離子產業和 OEM？您剛才談到人工智慧的用途和應用，那麼如何在您自己的製造業之外將其貨幣化？

Yes. So once you get the AI, then it becomes very chemistry-agnostic. And then actually in AI for manufacturing and AI for safety, we do train our models with both lithium metal data, more than 15,000 lithium metal cells in-house, as well as lithium ion data that we get from our OEM partners, we get from public sources. And the more diverse, the larger the data size that you train this model, the smarter the model becomes.

是的。因此，一旦你獲得了人工智慧，它就會變得與化學無關。然後實際上在製造人工智慧和安全人工智慧中，我們確實使用鋰金屬資料、內部超過 15,000 個鋰金屬電池以及從我們的 OEM 合作夥伴那裡獲得的鋰離子資料、我們從公共來源獲得的鋰離子資料來訓練我們的模型。而且，訓練該模型的資料越多樣化、規模越大，模型就會變得越聰明。

So the AI for manufacturing, we could also apply this. For example, say, a company wants to commercialize next-gen silicon lithium ion battery. And then it also happens to be pouched, stacked cells, we can apply this model to that manufacturing line because they're also new to that cell design; that cell manufacturing process. And they also don't know what quality specs to apply because it's new. So we can apply this to them.

因此，我們也可以將其應用於製造業的人工智慧。例如，一家公司想要將下一代矽鋰離子電池商業化。然後它也恰好是袋裝、堆疊電池，我們可以將這個模型應用到那條生產線上，因為他們對那種電池設計和電池製造工藝也是陌生的。而且由於它是新產品，他們也不知道應該採用什麼品質規格。所以我們可以將其應用在他們身上。

And also, if an OEM wants to put a silicon-annulled lithium ion cell, or any less proven lithium ion cell in the vehicle, and then also to monitor the health and then predict the remaining useful life in the incident, then this large language model trained on the data can also be used for that.

此外，如果 OEM 想要在車輛中安裝矽消除的鋰離子電池或任何未經證實的鋰離子電池，然後監測其健康狀況並預測事故中的剩餘使用壽命，那麼這種基於資料訓練的大型語言模型也可以用於此。

So they would, in effect, license this from you or license the solution from you. They'd pay you for the AI?

因此，他們實際上是從您那裡獲得此項許可或從您那裡獲得解決方案許可。他們會為人工智慧付錢給你嗎？

Yes. Yes. So for example, in the first phase of engagement, basically, it'll be free. They provide us data to fine tune our model. And then once our model is fine tuned, then we license that model to them.

是的。是的。例如，在第一階段的合作中，基本上是免費的。他們為我們提供數據來微調我們的模型。一旦我們的模型經過微調，我們就會將該模型授權給他們。

So for AI for safety, it could be a premium per vehicle per month over the 8- or 10-year warranty period. And for the AI for manufacturing, it could be also in fee per line per year.

因此，對於安全人工智慧而言，在 8 年或 10 年的保固期內，每輛車每月可能需要支付額外費用。對於製造業的人工智慧，其費用也可能按每條生產線每年計算。

Do you expect to own the IP that comes from this, particularly in the fourth science? I mean, you come up with a new combination or new chemistry. Are these things that then you will own and you will patent and license those things? Or are they going to be specifically used by the OEM for the solution and they would own it?

您是否希望擁有由此產生的智慧財產權，特別是在第四項科學領域？我的意思是，你想出了一種新的組合或新的化學反應。這些東西是你將擁有的，你會給它們申請專利和授權嗎？或者它們會被 OEM 專門用於解決方案並且他們會擁有它？

Yeah. So the models, we definitely own. And then in some cases, we might open source the models. So the models can be trained faster.

是的。所以這些模型我們確實擁有。在某些情況下，我們可能會開源模型。因此模型可以訓練得更快。

But then, especially in the AI for science case when we actually -- so the molecular universe, the molecule property database, that we plan to make open source. And then the model, part of the model will also be open source, so that others can develop it. And then this model can become smarter.

但是，特別是在科學人工智慧案例中，我們實際上——分子宇宙、分子屬性資料庫，我們計劃將其開源。然後模型，模型的一部分也將開源，以便其他人可以開發它。然後這個模型就可以變得更聰明。

But then once we use that model and then generate a new molecule that has, for example, high economic efficiency on lithium metal or can improve low temperature fast charge of silicon lithium ion, then those molecules, the output, of course, will be our proprietary IP. That was going to be the last one.

但是一旦我們使用該模型，然後產生一種具有例如對鋰金屬具有高經濟效率或可以改善矽鋰離子低溫快速充電的新分子，那麼這些分子的輸出當然將是我們的專有 IP。那將是最後一次。

Thanks. My last question is, will the AI be proactive and reactive? And by that what I mean is, let's say, there is a problem that is happening, right, something that's occurring. Can you then take that data and solve for it? I understand there's a predictive portion of this as well, but can you solve problems that the battery manufacturers and OEMs are experiencing after the fact?

謝謝。我的最後一個問題是，人工智慧是否會主動和被動？我的意思是，假設發生了一個問題，對，發生了某件事。那你能利用這些數據來解決問題嗎？我知道這其中也包含預測部分，但是您能解決電池製造商和 OEM 事後遇到的問題嗎？

Yeah. So in manufacturing, for sure. For example, we can actually blindly manufacture cells, meaning, you just manufacture cells, collect data without any initial quality specs. And then the AI is going to collect all the data and then get trained and then recommend quality specs. And actually, it's going to rank it.

是的。在製造業中確實如此。例如，我們實際上可以盲目地製造細胞，這意味著，你只需製造細胞，收集數據，而不需要任何初始品質規格。然後人工智慧將收集所有數據，進行訓練，然後推薦品質規格。實際上，它將其進行排名。

For example, certain steps will have higher impact on quality than other steps. And then -- so that's going to tell you, for example, step number 17. And that's hot press; that you need to lower the pressure to improve the quality.

例如，某些步驟對品質的影響會比其他步驟更大。然後 — — 這將告訴您，例如，步驟 17。這就是熱壓；你需要降低壓力來提高品質。

Yes. So in AI for manufacturing, definitely, you can get to a point where you can start with blind manufacturing. And then the AI will tell you where to fix, so yes.

是的。因此，在製造業人工智慧中，你絕對可以從盲目製造開始。然後人工智慧會告訴你在哪裡修復，是的。

In AI for safety on the vehicles, so the goal is to monitor the health and then predict. But then not really to control it, so whatever prediction we make, we're going to send that back to the OEMs. And then what the OEMs do with the signal, that's up to them.

人工智慧用於車輛安全，因此目標是監測健康狀況然後進行預測。但實際上我們無法控制它，所以無論我們做出什麼預測，我們都會將其發送回 OEM。然後，OEM 如何處理訊號，這取決於他們自己。

Great, that was very helpful. Thanks, Qichao.

太好了，這非常有幫助。謝謝，啟超。

Thank you, Shawn.

謝謝你，肖恩。

We have no further questions, so I'll pass the call back to the management team for any closing remarks.

我們沒有其他問題了，所以我會將電話轉回管理團隊，讓他們做最後的總結。

That concludes today's call. Thank you all for your participation. You may now disconnect your lines.

今天的電話會議到此結束。謝謝大家的參與。現在您可以斷開線路了。