OpenAI Cuts Inference Costs in Half: AI Competition Shifts from 'Who's Strongest' to 'Who's Cheapest'

The AI Industry's Paradigm Shift: From 'Who's Most Capable' to 'Who's Most Cost-Efficient'

According to sources familiar with the matter, OpenAI engineers told select colleagues earlier this month that they have developed several new optimization techniques capable of cutting model inference costs by more than half — information that had not previously been made public.

This development carries significant weight. Large language model inference refers to the process by which a model receives user input and generates output after training is complete — distinct from the far more compute-intensive training phase. Inference costs are typically priced per million tokens, where a token is the basic unit of text a model processes, roughly equivalent to 0.75 English words or 0.5 to 1 Chinese characters. Each response generation requires running matrix operations across billions or even hundreds of billions of parameters on GPU clusters, incurring enormous energy and hardware depreciation costs — which is why top-tier LLM inference remains expensive, with enterprise customers often paying upwards of $15 per million tokens. If OpenAI can reduce inference costs by more than 50%, the competitive strength of its business model would improve dramatically.

Around the same time, Anthropic announced an AI workbench for researchers, designed to help scientists automate repetitive tasks in the research workflow — including literature reviews, data cleaning, experimental design assistance, and draft paper generation. The tool is powered by NVIDIA's GB300 Blackwell Ultra GPUs. Blackwell is NVIDIA's next-generation data center GPU architecture following Hopper (the H100 series). The GB300 Ultra integrates two GPU chips and one Grace CPU, connected via NVLink-C2C high-bandwidth interconnects, delivering memory bandwidth exceeding 8TB/s — a 4x to 5x inference performance improvement over the previous-generation H100, representing the current state of the art in commercial AI compute infrastructure.

The near-simultaneous push by two leading AI labs to lower the barriers to AI adoption signals a deep paradigm shift in the global LLM industry: from competing on capability to competing on affordability and usability.

Inference Efficiency Replaces Training Scale as the New Competitive Frontier

Now that GPT-series and Claude-series models have reached a threshold of "good enough," the central bottleneck to large-scale AI deployment has shifted from model capability to economic cost and ease of use. Inference efficiency is replacing training scale as the industry's new high ground.

Rapidly falling inference costs will substantially expand the commercially viable frontier for AI applications. As call costs drop low enough for independent developers and small-to-medium businesses to afford AI integration, the range of viable use cases will expand sharply — a long-term structural tailwind for global AI adoption rates.

Notably, this trend also introduces new competitive pressure on Chinese AI companies. The cost-performance moats built by DeepSeek, Tongyi, Baidu Wenxin, and others — historically 30–50% cheaper to train than their American counterparts — will face mounting pressure as U.S. firms accelerate their own cost reductions.

Hardware + Software: A Two-Pronged Cost Reduction Approach Takes Shape

Software optimization is not the only path to lower costs. A U.S. AI chip startup recently announced it has raised a cumulative $800 million, backed by prominent figures including Peter Thiel, Geoffrey Hinton, and Fei-Fei Li, at a valuation of $5 billion. The company focuses on developing purpose-built inference chips for the Transformer architecture.

Transformer is the underlying architecture of virtually all mainstream large language models today — including GPT, Claude, and Gemini — first introduced by Google in the 2017 paper Attention Is All You Need. Its core "self-attention" mechanism enables parallel processing of relationships across all positions in a sequence, but also introduces computational overhead that scales quadratically with context length. General-purpose GPUs were not designed specifically for Transformer inference and involve significant compute waste. Purpose-built inference chips (AI ASICs) can implement hardware-level optimizations for Transformer-specific bottlenecks such as attention computation and KV caching, achieving higher tokens-per-second throughput at the same power envelope — and thereby significantly reducing per-inference costs.

This complements OpenAI's software-layer optimization efforts, forming a "hardware + software" dual-track cost reduction strategy. From algorithmic optimization to specialized silicon, the entire supply chain is evolving toward making AI cheaper. It is reasonable to expect that the cost decline curve for AI inference over the next few years will be steeper than most anticipate.

Geopolitics: Ukrainian Public Opinion Shifts

A recent Gallup survey has revealed profound shifts in public sentiment on both sides of the Russia-Ukraine conflict. Among 1,000 Ukrainian respondents, only 24% said they believed fighting should continue until victory, while 66% said the conflict should be ended through negotiations as soon as possible.

This stands in stark contrast to the early days of the conflict — when the war escalated to full-scale invasion in 2022, more than 70% of Ukrainians supported fighting to the end. Over three years of war, mounting casualties, and economic exhaustion have fundamentally altered Ukraine's national mood.

Perhaps more striking is that Ukrainian approval of U.S. performance has fallen to just 7%, with 79% of respondents expressing disapproval. Gallup noted that in more than 20 years of surveying over 140 countries, no nation has seen U.S. approval ratings drop so sharply within a five-year period. From "most steadfast ally" at the start of the conflict to being viewed as an "unreliable partner" today, American credibility in Ukraine has undergone a precipitous collapse.

The situation in Russia is also complex: 60% of Russian respondents said their regional economy is deteriorating, with only 27% saying conditions have improved. The convergence of shifting public sentiment and economic pressure on both sides may create some conditions for talks — but Ukraine's insistence on restoring 1991 borders remains deeply at odds with Russia's position.

Energy Markets: UAE Boosts Output After Exiting OPEC

According to shipping data firms Kpler and Vortexa, UAE crude oil exports rose to approximately 3.7 million barrels per day in June following the country's exit from OPEC — a record high, well above the pre-conflict norm of 3.1 to 3.3 million barrels per day.

As OPEC's former second-largest producer, the UAE's departure and subsequent output increase signal the practical collapse of the OPEC+ production-cut framework. OPEC+ is a loose alliance formed in 2016 between the traditional Organization of the Petroleum Exporting Countries (OPEC, 13 members) and non-OPEC producers led by Russia, designed to stabilize international oil prices through coordinated production quotas — enforced on a voluntary compliance basis. The UAE's exit represents a culmination of long-running frustration over quota constraints that suppressed its investment in capacity expansion, and sharply illustrates the inherent fragility of multilateral production coordination when member interests diverge. No longer bound by quotas, and possessing the unique risk-resistant export channel of the Fujairah port terminal outside the Strait of Hormuz, the UAE has gained a significant strategic advantage.

Analysts note that the UAE's daily exports have increased by roughly 400,000 to 600,000 barrels compared to pre-conflict levels — an increment that exceeds the total of most previous OPEC+ production cuts combined. Against a backdrop of modest global demand growth, sustained output increases have been a key supply-side driver pushing oil prices down from recent highs to around $69.50 per barrel, delivering an unexpected disinflationary force to the global economy.

A-Shares and New Energy Vehicles: Structural Divergence Deepens

On the first trading day of July, China's three major indices showed marked divergence. At the close, the Shanghai Composite Index rose 0.44% to 4,112.45 points, while the ChiNext Index fell 1.89% and the STAR 50 Index surged intraday before reversing sharply, falling 2.48%. Total turnover across both markets reached approximately 3.68 trillion yuan, with market liquidity remaining ample.

The session exhibited a classic "rotation from high to low" pattern. Technology sector leaders that had rallied significantly — including AI compute, semiconductor equipment, and robotics — came under broad pressure. The STAR 50 Index carries a forward price-to-earnings ratio of 251x — a metric using projected 12-month earnings as the denominator, reflecting the market's growth expectations. A ratio of 251x means investors are willing to pay 251 yuan for every 1 yuan of expected profit, far exceeding the Nasdaq 100's historical average of roughly 35–40x, and well above the valuation benchmarks of major global tech indices. Extreme valuations can be sustained in the short term by upward earnings revisions or loose liquidity conditions, but once earnings growth disappoints, valuation mean-reversion pressure tends to release in a nonlinear fashion. Following a 64% surge in the first half of the year, the market needs time to digest accumulated gains. Meanwhile, low-valuation traditional sectors including banking, coal, and utilities held up relatively well.

In the new energy vehicle space, trends reflected "steady month-over-month growth with structural divergence." Xiaomi Auto delivered over 30,000 vehicles for the third consecutive month in June. Huawei-ecosystem models (AITO, Luxeed, etc.) delivered 50,624 units in June, up 9.7% month-over-month, with cumulative first-half deliveries reaching 240,000 units, up 18.6% year-over-year.

Looking ahead to the second half of the year, A-share companies will begin releasing interim earnings pre-announcements in mid-July. Whether actual results can justify current valuations will be the decisive factor for whether tech stocks can sustain their elevated multiples. The day's divergence was a timely reminder of one simple market truth: no market only goes up.

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