开源模型逼AI巨头打补贴战，OpenAI和Anthropic高估值承压
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Palantir CEO: Enterprises are dissatisfied with "cutting-edge labs" like OpenAI and Anthropic, which only pursue token maximization.
According to BlockBeats, on July 2nd, Palantir CEO Alex Karp, in an interview with CNBC's "Squawk Box," strongly criticized leading AI model companies, calling the way AI is sold "completely wrong." Karp emphasized that companies are already dissatisfied with "cutting-edge labs" like OpenAI and Anthropic, believing they only pursue token maximization, wasting companies' time and money while handing over proprietary value and IP. Karp stated that companies are "angry" and will commit to owning their own AI production resources rather than relying on third parties. On June 29th, Palantir partnered with Nvidia to deploy Nvidia Nemotron open AI models in sovereign environments, primarily serving the US government and critical infrastructure customers. The collaborative system reportedly integrates Nvidia AI technology with Palantir's AIP, Foundry, Ontology, and Apollo platforms, helping organizations train, customize, and deploy AI locally while maintaining complete control over data, intellectual property, and models.
AI giants like OpenAI and Anthropic are offering startups large amounts of free computing power in an effort to seize market share.
According to a report by the Wall Street Journal on July 7th, as reported by Mars Finance, AI companies such as OpenAI and Anthropic are offering substantial free computing power and discounts to startups to compete for enterprise customers. The report states that Silicon Valley startup founders are receiving computing credits, token usage limits, and auction-style discounts from AI model companies. Some early-stage companies have received over $3 million in cloud computing and token credits, approaching the median of seed funding in the US according to PitchBook. AI companies hope to acquire customers in the early stages of startups, making their tools an integral part of their business as these companies grow. Cursor offered a 75% discount until July 5th; Google Cloud offered up to $500,000 in cloud computing credits to some startups, along with early access to the Gemini model and, in some cases, support from DeepMind engineers. Microsoft and Amazon Web Services also offer special privileges to startups. OpenAI and Anthropic have recently been particularly focused on Y Combinator startups. In May, Sam Altman announced that OpenAI would offer $2 million in tokens to each startup participating in its accelerator program in exchange for equity. Around the same time, Anthropic increased its free token allocation to Y Combinator startups from $30,000 to $500,000, without requiring equity. OpenAI subsequently adjusted its offer, providing startups with $500,000 in free tokens, without requiring equity, and offering the option to exchange equity for an additional $1.5 million in tokens. These offers reflect the fierce competition among model providers for future large clients. Y Combinator runs four cohorts annually, with recent cohorts featuring approximately 200 companies each, meaning that OpenAI and Anthropic could potentially offer a combined $800 million in AI tokens over the next year. Christopher Acker, co-founder of SuperPenguin, stated, "The AI world is being driven by OpenAI and Anthropic because they are giving startups money to pay for usage costs."
Financial AI operates outside of regulation; the UK's FCA plans to expand its jurisdiction over AI giants such as OpenAI and Anthropic.
According to Beating's monitoring, Sheldon Mills, Executive Director of the UK Financial Conduct Authority (FCA), warned that regulators are facing an "arms race" to keep pace with the rapid adoption of AI in the financial services industry as businesses and individuals accelerate their adoption. Mills' report on the financial impact of AI indicates that 20% of UK adults are already willing to let large models make their savings or borrowing decisions. While this service offers an experience equivalent to regulated traditional financial advice, its lack of regulatory oversight means users are unable to obtain any financial compensation when they suffer losses. The report recommends an urgent review of the risks of unregulated financial AI and an application for expanded legislative authorization to strengthen oversight of core technology providers such as Anthropic, OpenAI, Amazon, Google, and Microsoft through a "key third party" mechanism (the UK government has not yet finalized the specific list). It also recommends collaboration to launch free public financial literacy and decision-making guidance services assisted by AI.
Amazon AWS establishes a $1 billion AI FDE team to emulate the enterprise AI deployment models of OpenAI and Anthropic.
BlockBeats reported on June 30th that Amazon Web Services (AWS) announced the creation of a new Forward-Deployed Engineer (FDE) organization and will invest $1 billion in internal resources to help enterprise customers deploy customized AI agents and related systems. AWS stated that FDE engineers will be directly stationed at customer companies, responsible for building AI applications, optimizing workflows, and helping customers build the ability to independently develop and operate AI systems, rather than simply delivering and maintaining them. The FDE model was first promoted by Palantir and has rapidly gained popularity in recent years due to the growing demand for enterprise AI deployment. Previously, OpenAI and Anthropic also launched related FDE joint ventures, with sizes of approximately $4 billion and $1.5 billion respectively. Unlike the two companies, which formed joint ventures with private equity firms, AWS's $1 billion investment this time primarily comes from internal resources and is not an independent investment project. --------------------------------- Click the original link below to join the Beating · Lark AI news channel and monitor global AI hot topics and news 24/7.
Sources say OpenAI, Anthropic, and Google are offering hefty computing power subsidies to startups to compete for enterprise clients.
According to a report by Odaily Odaily, citing sources cited by The Wall Street Journal, OpenAI, Anthropic, and Google are offering startups hundreds of thousands of dollars worth of computing resources and other incentives to attract new enterprise customers. (Jinshi)
More severe than the dot-com bubble: Token consumption plummeted by 20%, and the gap between AI investment and sales growth reached 46%.
According to Beating's monitoring, the Silicon Data LLM Token consumption index, which tracks users' actual computing power expenditure, has fallen nearly 20% from its May high. This sudden halt in high growth sends a crucial warning to investors: large model vendors may be losing pricing power with cost-sensitive clients, and it has also raised doubts about the ultimate return on investment for the hundreds of billions of dollars in AI capital expenditure. The divide between bulls and bears has intensified. Bears point out that Allianz Research data shows the growth gap between AI investment and sales has reached 46%, exceeding the 32% imbalance seen during the 2001 telecom bubble burst. Bulls counter that while the average token price has plummeted by 90% since 2023, total expenditure has still nearly doubled, meaning the index decline is merely a structural digestion after price cuts stimulated consumption, and the long-term return on investment in the inference phase is far more optimistic than in the training phase. Increased policy regulation is translating into hidden compliance costs for enterprise users. Washington has imposed stronger policy scrutiny on the distribution and cross-border access of cutting-edge models (such as the release review of OpenAI and geopolitical export controls on Anthropic models). Coupled with the EU's Artificial Intelligence Act's stringent compliance requirements for top-tier models, this has placed a heavy policy burden on leading platforms. To mitigate geopolitical and compliance risks, corporate CFOs have a more rational reason to proactively shift their workloads towards lightweight models that are less subject to regulatory constraints. Subtle changes are also emerging in the hardware chip sector. Although orders for top-tier GPUs and high-bandwidth memory (HBM) are booked until 2026, with substantial supply-demand easing not expected until 2028, the market's main procurement focus has shifted from training chips to inference optimization hardware, and the winners are being reshuffled.