# Cost Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Cost", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

Serenity: LeaderDrive, the "Chinese Core" in the Robotics Race

The author argues that LeaderDrive (688017) is a uniquely attractive investment in China's robotics sector as a high-barrier components leader, distinct from low-margin assemblers or low-value part makers. It is highlighted by Western institutions like Goldman Sachs for its technical expertise in areas like harmonic reducers and its potential to capture a high bill-of-materials cost per humanoid robot, including components like planetary roller screws. The investment thesis covers high-entry-barrier parts, significant value per unit, and low-cost mass-production capability. Key risks include competition from other emerging Chinese firms and potential margin compression from mass production. However, the author believes non-Chinese companies cannot match China's low-cost production scale, predicting a bifurcated supply chain: cheap robots from China and costly ones from the West. The current valuation may seem high, but the author views it through the lens of the future massive TAM for humanoid robots, with predictions of millions of units annually. The long-term investment case is that LeaderDrive's current market cap will appear small if it captures substantial share in the exponentially growing humanoid robotics market over the next 3-5 years. This is presented as a long-term hold, not a short-term trade.

marsbit06/08 06:07

Serenity: LeaderDrive, the "Chinese Core" in the Robotics Race

marsbit06/08 06:07

70% of the Public Opposes AI, Americans Hope the U.S. Loses the AI War

70% of Americans believe AI development is moving too fast, with growing public resistance evolving from online criticism to real-world protests and violence. This widespread anti-AI sentiment stems from fears of job losses, rising utility costs, environmental damage, threats to democracy, and financial instability. Key incidents illustrate the backlash: Google's former CEO Eric Schmidt was loudly booed at a graduation for promoting AI; AI company ads are vandalized; protests and even violent attacks target AI firms and data centers. Polls show deep public pessimism and strong local opposition to data center construction, often surpassing resistance to nuclear power plants. The core grievances are economic and practical: AI is seen as automating jobs, concentrating wealth, and increasing household electricity and water bills due to massive data center resource demands. Environmentalists also oppose AI's high energy use and carbon emissions. This opposition has turned AI into a major political issue in the US. While the Trump administration prioritizes AI innovation for global competition, bipartisan pushback is growing. Democrats and factions within the MAGA movement are forming temporary alliances to support stricter regulations and local bans on new data centers, pressuring the administration to choose between its tech industry backers and its voter base. The situation highlights a profound national divide over AI's future.

marsbit06/06 05:14

70% of the Public Opposes AI, Americans Hope the U.S. Loses the AI War

marsbit06/06 05:14

Token Inefficient, Economy Tokenless

The article "Tokens Aren't Economical, Economics Aren't Tokenized" analyzes a pivotal shift in the AI industry from a technology-driven narrative to one dominated by capital efficiency. It highlights two concurrent trends: a severe capital shortage due to the exorbitant and recurring costs of compute (e.g., OpenAI's high burn rate) and a wave of corporate spin-offs where major tech companies are separating their AI units (like Kuaishou's Kling and Baidu's Kunlunxin). The core argument is that AI's "anti-internet" business model, where user growth increases costs rather than profits, has created a disconnect between high valuations and actual cash flow. Spin-offs address this by allowing AI assets to be valued independently. Within a parent company, they are seen as cost centers, but as standalone entities, they are priced based on their growth potential and scarcity in the primary market, leading to massive valuation premiums (e.g., Kling's estimated value tripling post-spin-off). The industry is at an inflection point, moving from "model worship" to "value realization." The competition is evolving from a pure compute (GPU) race to a broader focus on systemic efficiency and full-stack engineering (involving CPUs and orchestration) to achieve viable commercialization. The year 2026 is framed as a critical moment where the industry must definitively answer how to economically translate AI capability into tangible business value, reshaping the sector's future power structure.

marsbit06/05 11:13

marsbit06/05 11:13

AI Relay Stations Spark Heated Debate on Zhihu: Behind Cheap Tokens, What Are Users Really Worried About?

A discussion on Zhihu about "AI relay stations" shifted the niche developer topic of "cheap tokens" into broader user awareness. Users moved beyond simply questioning the legitimacy of these services to focus on practical concerns: Where do cheap tokens truly come from? Is the model being accessed the real one? Can relay stations see prompts, code, and API keys? For occasional users, are the risks worth it? The core debate centered less on price and more on trust. A primary worry is model authenticity—the risk of "model swapping," where users paying for a premium model might be routed to a cheaper one, creating an information asymmetry. Others argued that cost comparisons matter; while cheaper than official pay-as-you-go APIs, relay stations may not be the lowest-cost option versus subscriptions, domestic models, or free tiers, making user needs assessment crucial. Speculation about token sources ranged from legitimate bulk discounts to gray-area methods like account sharing or exploiting regional pricing. This opacity makes risk assessment difficult for users. Data security emerged as a critical concern, especially for enterprise use. When processing sensitive information like code, contracts, or client data, the inability to verify a relay station's data handling, retention, or access policies poses significant compliance and confidentiality risks. The evolving consensus suggests relay stations can be used cautiously for low-sensitivity, disposable tasks (e.g., summarizing public info, simple translation). However, they should not be the default for sensitive, professional, or production workflows involving proprietary data, Agents, or automated systems. Recommendations include avoiding large prepayments, not relying on a single service, using test prompts to monitor quality, anonymizing data where possible, and keeping official channels as backups. Ultimately, the discussion framed tokens not just as a billing unit but as a measure of real cost encompassing price, model integrity, data security, and service stability. The popularity of relay stations highlights user demand for affordable access, but the debate underscores a key trade-off: the savings from cheap tokens may come at the price of trust, transparency, and control over one's data and AI experience.

marsbit06/04 06:11

AI Relay Stations Spark Heated Debate on Zhihu: Behind Cheap Tokens, What Are Users Really Worried About?

marsbit06/04 06:11

After 540,000 Lines of Code, Garry Tan Realizes the Old Game of AI Programming Is Over

YC President Garry Tan's project "Garry's List" involved over 540,000 lines of Rails code, but he concluded this approach is outdated. He argues the software industry is stuck in a "Foxconn factory" mindset: building excessive tests, validators, and control logic to constrain LLMs, which have become cheap and capable enough to work autonomously. The old paradigm treated LLM calls as expensive and code as cheap, leading to complex systems to "manage" the AI. This has now reversed. The future lies in "just-in-time software"—using natural language instructions (Markdown-based "skill packs") and minimal code, letting the AI handle the work. Tan advocates for "skillifying" workflows: after an agent completes a task, it packages the process into a reusable, tested skill pack. This shifts value from writing code to designing capabilities. For example, his agent reviewed 85 hackathon submissions in 30 minutes, a task that previously took days. He emphasizes "tokenmaxxing"—willingly spending on LLM tokens to gain a years-long competitive advantage, as costs are plummeting. The core shift is from measuring output in lines of code to focusing on clarity, taste, and judgment. The best future engineers won't write the most code, but will know what to build and how to unlock the most intelligence with the least code.

marsbit06/02 21:38

After 540,000 Lines of Code, Garry Tan Realizes the Old Game of AI Programming Is Over

marsbit06/02 21:38

Chatbot has been burning money for three years, is it still the 'New Continent' of the AI era?

For years, the AI industry has been guided by a singular "map" — the belief that the AI era's "new continent" would be found in the Chatbot, a super-app akin to the mobile internet's super-apps. This belief was fueled by ChatGPT's explosive 2022 debut. However, three years of heavy investment reveal a different reality: the Chatbot-as-ultimate-entry-point model is struggling. The core issue is economic. Chatbots defy traditional internet economics. Unlike apps with near-zero marginal cost, each AI query consumes significant, expensive compute. More users mean higher costs, not profits. OpenAI, despite ~900M weekly active users, reportedly loses money. The expected network effects and data flywheels that power internet giants are weak in Chatbots, as one user's interactions don't improve another's experience. Monetization is a major hurdle. The subscription model faces low conversion rates, especially in China where users expect AI to be free. The "free + ads" model also struggles. Chatbot interactions often lack commercial intent, and inserting ads compromises the trust essential for an answer engine. Perplexity's minimal ad revenue and subsequent pivot away from ads highlight this difficulty. Switching between Chatbots is easy, making user loyalty low and competition a potential race to the bottom on price. Data suggests the standalone Chatbot's growth is slowing, and user engagement (avg. ~6 mins/day) pales compared to apps like TikTok. The product form itself is limiting; studies show nearly half of interactions are simple Q&A, trapping AI's potential in a passive, single-turn "cage." A contrasting, more successful path is emerging, exemplified by Anthropic. With over 85% of its ~$30B annualized revenue from enterprises, it focuses on AI as a productivity tool, not a companion. The rise of AI Agents (like OpenClaw) and the integration of AI into existing workflows (e.g., Google's AI Overviews, Apple Intelligence in OS) signal a shift. The future may not be a dominant Chatbot app, but AI embedded seamlessly into social apps, operating systems, and hardware — a capability-layer revolution, not a new distribution container. The conclusion is clear: the old "map" centered on a standalone Chatbot super-app is leading to a dead end. To find the true valuable "continent" of the AI era, the industry must update its navigation to prioritize deep integration, practical utility, and sustainable economics over a generic conversation window.

marsbit06/02 10:35

Chatbot has been burning money for three years, is it still the 'New Continent' of the AI era?

marsbit06/02 10:35

After Burning Tens of Billions of Dollars in Tokens, Silicon Valley Giants Start Limiting Employee Token Usage

After burning tens of billions of dollars on AI tokens, major Silicon Valley firms are now restricting employee usage. Companies like Microsoft, Uber, and Salesforce, which heavily promoted AI for "efficiency," are facing a cost crisis. The practice of "tokenmaxxing"—pushing employees to maximize AI tool usage—led to wasteful spending on trivial tasks like checking the weather or writing birthday messages, with studies showing significant hidden costs for bug fixes and code rewrites. The core issue is a misalignment between individual productivity gains and actual business value. While employees use AI to automate tasks they dislike, such as writing reports, this often doesn't translate to increased company revenue or improved core business outcomes. For instance, AI-generated code speeds up development but also sees an 800% increase in "code churn" (code being discarded or rewritten). As a result, only 14% of CFOs report seeing a clear, measurable return on AI investments. Firms are now shifting strategies. Microsoft has revoked most internal licenses for Claude Code, while others are implementing monitoring and cost controls. New tools from companies like Harness and CloudZero aim to track AI spending and tie costs to business results. Some AI vendors, like HubSpot, are moving from token-based pricing to charging based on outcomes, such as "resolved conversations" or "leads generated." This represents a necessary correction in the AI adoption cycle. The challenge now is for companies to move beyond using AI merely to speed up old tasks and instead rethink their workflows and business models fundamentally. The future of enterprise AI depends on proving its value, not just its usage.

marsbit06/01 04:06

After Burning Tens of Billions of Dollars in Tokens, Silicon Valley Giants Start Limiting Employee Token Usage

marsbit06/01 04:06

When Tokens Cost More Than People, 'AI Narrative' Runs Into Trouble

Title: When Tokens Cost More Than People, the "AI Narrative" Hits Trouble The economic sustainability of corporate AI adoption is under scrutiny as token consumption soars while measurable business value remains elusive. Major companies like Uber and Microsoft report struggling to justify rising AI costs, with executives coining terms like "tokenmaxxing" to describe wasteful usage. Data reveals a stark picture: for every dollar spent on AI tokens, only 18 cents translates to user-facing value, with the rest consumed by bug fixes, rework, and friction. The debate splits into bullish and bearish camps. Bulls, like Goldman Sachs analysts, see current inefficiencies as growing pains, predicting a 24-fold increase in token demand by 2030 and a shift towards healthier metrics like "cost per effective action." They point to indicators of real productivity gains and argue current tech valuations are not in bubble territory. Bears, however, highlight an unsustainable model where value is heavily concentrated in semiconductor companies like Nvidia, funded by cloud giants taking on massive debt. Studies show 95% of firms investing in generative AI see zero return. A deeper concern is the circular financial structure between cloud providers (hyperscalers) and AI labs like OpenAI and Anthropic. Billions in cloud service commitments are tied to these labs, which are partly funded by the hyperscalers' own investment. This creates a loop where cloud revenue depends on labs securing continuous external funding to pay their compute bills, which in turn relies on end-corporates willing to pay ever-higher token costs. The sustainability of this cycle is now in question. While not a classic bubble—AI technology is real and delivers productivity for power users—the central issue has shifted. The focus is no longer just on technological capability but on economics: whether the savings AI generates for businesses can outpace the soaring costs and justify the valuations of labs and cloud providers. The era of equating rising token usage with successful AI transformation is over. The bill for AI has arrived, but who ultimately pays remains uncertain.

marsbit05/29 01:44

When Tokens Cost More Than People, 'AI Narrative' Runs Into Trouble

marsbit05/29 01:44

Microsoft to Discontinue Using Claude: Too Expensive, or a Calculated Move?

Microsoft is discontinuing internal access to the AI coding assistant Claude Code for its engineers, citing high costs and transitioning them to its own GitHub Copilot CLI. Reports indicate that Claude Code's usage-based pricing model led to significant expenses, with estimates of $500-$2000 per engineer monthly, prompting cost reviews even at large firms like Uber. While Microsoft officially attributes the move to cost, analysis suggests it may also be a strategic move. Microsoft allowed internal use of Claude Code for six months to benchmark it against Copilot, gathering feedback to improve its own product. This "experiment" highlights deeper challenges: Microsoft lacks a competitive in-house general-purpose AI model, heavily relying on external partners like OpenAI and Anthropic. Claude Code has gained substantial market traction, surpassing Copilot in features like context window size and task completion rates, and is eroding Microsoft's control over the developer ecosystem. The shift signals broader industry concerns over AI pricing models and the strategic risks of dependency on external, rapidly evolving AI tools.

marsbit05/25 10:28

Microsoft to Discontinue Using Claude: Too Expensive, or a Calculated Move?

marsbit05/25 10:28

$67 Billion! AI Boom Fuels Largest Energy Merger in U.S. History

In a landmark deal, NextEra Energy announced a $67 billion acquisition of Dominion Energy on May 18, 2026, marking the largest U.S. utility merger. The primary driver is the insatiable power demand from AI data centers, concentrated in Virginia's "Data Center Alley," where Dominion is the key supplier holding over 51 GW of data center contracts. This acquisition highlights a fundamental shift. While global electricity demand grew 3% in 2025, data center demand surged 17%, driven by AI. This strain is causing irreversible price spikes, like the 76% increase in the PJM grid, and grid instability, as seen in a 2025 Virginia incident where 60 data centers disconnected simultaneously. NextEra, a major renewable energy provider, bets that combining its clean power and storage expertise with Dominion's strategic access to data centers will be transformative. However, the deal raises critical questions about cost distribution. Analysts warn that up to $700 billion in infrastructure costs may be passed to residential consumers through higher bills, creating a disparity where private AI profits rely on publicly subsidized grid upgrades. This merger signals just the beginning of AI's profound reshaping of the energy landscape.

marsbit05/21 08:07

$67 Billion! AI Boom Fuels Largest Energy Merger in U.S. History