According to Sam Altman (@sama), the cost of AI model training has decreased by 390 times within a year, signaling a dramatic shift in the economics of artificial intelligence development (source: Sam Altman Twitter, Dec 11, 2025). This unprecedented reduction unlocks significant opportunities for startups and enterprises to access advanced AI capabilities at a fraction of previous costs, accelerating innovation in AI-driven products and services. Businesses can now experiment with larger models and more complex use cases, lowering barriers for market entry and enabling rapid scaling. The trend also intensifies competition among cloud providers and AI infrastructure companies, pushing them to further optimize AI compute resources and pricing models.

According to ARC Prize (@arcprize) and Greg Brockman (@gdb), GPT-5.2 Pro has set a new state-of-the-art (SOTA) benchmark on ARC-AGI, scoring 90.5% with a dramatic 390X efficiency improvement compared to last year’s models (source: ARC Prize, Greg Brockman, 2025). In 2024, an early version of OpenAI o3 (High) achieved 88% accuracy at an estimated $4,500 per task, while the new GPT-5.2 Pro (X-High) delivers higher accuracy at only $11.64 per task. This leap in both accuracy and cost-efficiency signals a major advance for enterprise AI adoption, enabling more scalable, cost-effective deployment of advanced reasoning AI models for industries including education, healthcare, and enterprise automation (source: ARC Prize, 2025).

According to Sam Altman on Twitter, the price per unit of AI intelligence has decreased by a staggering 300 times in just one year, as referenced in a post by @chatgpt21 (source: x.com/chatgpt21/status/1990516566073729362). This unprecedented cost reduction is enabling broader access to advanced AI models and accelerating the democratization of artificial intelligence across industries. Businesses can now deploy more powerful AI solutions at a fraction of previous costs, opening new opportunities in sectors such as finance, healthcare, and e-commerce. The rapid decline in AI costs is also driving increased competition and innovation, as highlighted by Altman's observation, and businesses that quickly integrate these affordable AI tools will gain significant market advantages (source: twitter.com/sama/status/1990617084661743811).

According to God of Prompt (@godofprompt), Chinese startup DeepSeek has developed an open source AI language model that matches the performance of OpenAI’s $60/million token GPT model but at a cost of just $0.55 per million tokens, representing a 98% reduction in cost. DeepSeek achieved this with a $6 million investment using restricted GPUs unavailable in China due to US export controls, while OpenAI spent $6 billion for similar outcomes. This breakthrough highlights the rapidly narrowing technological gap between Chinese and US AI firms, undermining OpenAI’s perceived technological moat and suggesting that high AI valuations may be driven more by marketing than by actual technological exclusivity. For the AI industry, DeepSeek’s open source approach and drastic cost reduction signal new business opportunities in global AI accessibility, enterprise adoption, and competitive pricing, while raising questions about the sustainability of current US AI business models (source: @godofprompt, Twitter, Nov 9, 2025).

According to @godofprompt on Twitter, MiniMax has launched the M2 model, which is being recognized as the first true agent-native AI model. The M2 model delivers superior performance compared to leading competitors such as Claude 4.1, Gemini 2.5, and Qwen3, while costing only 8% of Claude’s price (source: @godofprompt, Twitter, Nov 1, 2025). This disruptive pricing and performance combination positions M2 as a powerful business solution for enterprises seeking to integrate advanced AI agents at scale. The launch signals a major shift in the AI market, opening new opportunities for process automation, cost reduction, and the rapid deployment of agent-powered applications.

According to @DeepLearningAI, DeepSeek's new 685B Mixture-of-Experts (MoE) AI model introduces a token-attention mechanism that processes only the most relevant tokens, enabling 2–3× faster long-context inference and reducing processing costs by 6–7× compared to its previous V3.1 model (source: DeepLearning.AI Twitter, Oct 22, 2025). The v3.2 model features MIT-licensed weights and API pricing of $0.28/$0.028/$0.42 per 1M input/cached/output tokens, promoting open-source adoption. It is specifically optimized for Huawei and other domestic Chinese chips, addressing hardware compatibility for the local market. While performance closely matches V3.1 overall, there are modest gains in coding and agentic tasks and minor trade-offs in science and math workloads, presenting new business opportunities for AI providers targeting cost-sensitive or China-centric deployments (source: DeepLearning.AI, The Batch).

According to @YourAICompany on Twitter, a limited-time promotion is offering a 50% discount on advanced AI software solutions with code DEALDAY50. This deal is positioned to help businesses cut down operational costs while maintaining access to innovative AI-powered tools. The promotion targets enterprises looking to scale AI adoption affordably, accelerating digital transformation and enhancing competitiveness in the 2024 market. Verified by Twitter announcement, this offer supports broader AI adoption across industries, enabling smaller companies to leverage AI for automation, analytics, and workflow efficiency. Source: @YourAICompany Twitter post.