Tech
Why building big AIs costs billions – and how Chinese startup DeepSeek dramatically changed the calculus
Ambuj Tewari, University of Michigan
State-of-the-art artificial intelligence systems like OpenAI’s ChatGPT, Google’s Gemini and Anthropic’s Claude have captured the public imagination by producing fluent text in multiple languages in response to user prompts. Those companies have also captured headlines with the huge sums they’ve invested to build ever more powerful models.
An AI startup from China, DeepSeek, has upset expectations about how much money is needed to build the latest and greatest AIs. In the process, they’ve cast doubt on the billions of dollars of investment by the big AI players.
I study machine learning. DeepSeek’s disruptive debut comes down not to any stunning technological breakthrough but to a time-honored practice: finding efficiencies. In a field that consumes vast computing resources, that has proved to be significant.
Where the costs are
Developing such powerful AI systems begins with building a large language model. A large language model predicts the next word given previous words. For example, if the beginning of a sentence is “The theory of relativity was discovered by Albert,” a large language model might predict that the next word is “Einstein.” Large language models are trained to become good at such predictions in a process called pretraining.
Pretraining requires a lot of data and computing power. The companies collect data by crawling the web and scanning books. Computing is usually powered by graphics processing units, or GPUs. Why graphics? It turns out that both computer graphics and the artificial neural networks that underlie large language models rely on the same area of mathematics known as linear algebra. Large language models internally store hundreds of billions of numbers called parameters or weights. It is these weights that are modified during pretraining. https://www.youtube.com/embed/MJQIQJYxey4?wmode=transparent&start=0 Large language models consume huge amounts of computing resources, which in turn means lots of energy.
Pretraining is, however, not enough to yield a consumer product like ChatGPT. A pretrained large language model is usually not good at following human instructions. It might also not be aligned with human preferences. For example, it might output harmful or abusive language, both of which are present in text on the web.
The pretrained model therefore usually goes through additional stages of training. One such stage is instruction tuning where the model is shown examples of human instructions and expected responses. After instruction tuning comes a stage called reinforcement learning from human feedback. In this stage, human annotators are shown multiple large language model responses to the same prompt. The annotators are then asked to point out which response they prefer.
It is easy to see how costs add up when building an AI model: hiring top-quality AI talent, building a data center with thousands of GPUs, collecting data for pretraining, and running pretraining on GPUs. Additionally, there are costs involved in data collection and computation in the instruction tuning and reinforcement learning from human feedback stages.
All included, costs for building a cutting edge AI model can soar up to US$100 million. GPU training is a significant component of the total cost.
The expenditure does not stop when the model is ready. When the model is deployed and responds to user prompts, it uses more computation known as test time or inference time compute. Test time compute also needs GPUs. In December 2024, OpenAI announced a new phenomenon they saw with their latest model o1: as test time compute increased, the model got better at logical reasoning tasks such as math olympiad and competitive coding problems.
Slimming down resource consumption
Thus it seemed that the path to building the best AI models in the world was to invest in more computation during both training and inference. But then DeepSeek entered the fray and bucked this trend.
Their V-series models, culminating in the V3 model, used a series of optimizations to make training cutting edge AI models significantly more economical. Their technical report states that it took them less than $6 million dollars to train V3. They admit that this cost does not include costs of hiring the team, doing the research, trying out various ideas and data collection. But $6 million is still an impressively small figure for training a model that rivals leading AI models developed with much higher costs.
The reduction in costs was not due to a single magic bullet. It was a combination of many smart engineering choices including using fewer bits to represent model weights, innovation in the neural network architecture, and reducing communication overhead as data is passed around between GPUs.
It is interesting to note that due to U.S. export restrictions on China, the DeepSeek team did not have access to high performance GPUs like the Nvidia H100. Instead they used Nvidia H800 GPUs, which Nvidia designed to be lower performance so that they comply with U.S. export restrictions. Working with this limitation seems to have unleashed even more ingenuity from the DeepSeek team.
DeepSeek also innovated to make inference cheaper, reducing the cost of running the model. Moreover, they released a model called R1 that is comparable to OpenAI’s o1 model on reasoning tasks.
They released all the model weights for V3 and R1 publicly. Anyone can download and further improve or customize their models. Furthermore, DeepSeek released their models under the permissive MIT license, which allows others to use the models for personal, academic or commercial purposes with minimal restrictions.
Resetting expectations
DeepSeek has fundamentally altered the landscape of large AI models. An open weights model trained economically is now on par with more expensive and closed models that require paid subscription plans.
The research community and the stock market will need some time to adjust to this new reality.
Ambuj Tewari, Professor of Statistics, University of Michigan
This article is republished from The Conversation under a Creative Commons license. Read the original article.
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News
Joby Aviation and Toyota kick off manufacturing alliance to scale electric air taxi production
Joby Aviation and Toyota launch a joint venture to improve productivity, quality, and cost as they prepare to scale electric air taxi production.
Joby Aviation and Toyota Motor Corporation have launched the initial phase of a strategic manufacturing alliance aimed at accelerating commercial production of electric air taxis—an early step the companies say is designed to make “air mobility for all” a practical, everyday reality.
Announced June 30, 2026, the partnership formalizes a new joint venture that will combine Joby’s electric aviation development with Toyota’s production systems and operational expertise. The near-term focus: building the groundwork for commercial production while pushing improvements in productivity, quality, and cost—key factors as the industry moves from prototypes to scaled manufacturing.
What the joint venture is designed to do
According to the companies, the alliance will initially concentrate on:
- Establishing the foundation for commercial production capability
- Advancing manufacturing excellence with an emphasis on productivity, quality, and cost
- Supporting expansion of Joby’s production capacity as it works toward aircraft certification and prepares for anticipated demand
The announcement positions Toyota’s manufacturing playbook—known globally for lean production and continuous improvement—as a lever to help Joby move from development into repeatable, high-quality output at scale.
Why it matters: eVTOLs need scale, not just flight tests
Electric vertical take-off and landing (eVTOL) aircraft have become one of the most closely watched bets in next-generation transportation, but the path to viable air taxi services depends on more than successful test flights. Certification timelines, supply chain readiness, and the ability to produce aircraft consistently (and affordably) are often what separates promising technology from commercial reality.
By forming a joint venture focused on manufacturing readiness, Joby and Toyota are signaling that the next competitive frontier is industrialization—how quickly and reliably eVTOL aircraft can be built to meet safety standards and market demand.
Related Links for Further reading
- Joby Aviation (official): https://www.jobyaviation.com
- Joby Investor Relations / News (official updates & filings): https://ir.jobyaviation.com
- Toyota Newsroom (official): https://www.toyotanewsroom.com
- Toyota Global (corporate overview): https://global.toyota/en
- FAA Advanced Air Mobility / Air Taxis (context): https://www.faa.gov/air-taxis
What executives are saying
Joby founder and CEO JoeBen Bevirt emphasized the long-running relationship between the companies, calling the joint venture a reflection of shared confidence in the opportunity ahead.
“Toyota has been by Joby’s side for nearly a decade, providing invaluable guidance and support as we built the foundation for manufacturing our aircraft,” Bevirt said. “Together, we share a vision of making aerial mobility an everyday reality.”
Toyota Motor Corporation Chairman Akio Toyoda framed air mobility as an extension of the company’s broader mission.
“Since our founding, we’ve been guided by the philosophy of providing mobility for all,” Toyoda said, adding that Toyota views air mobility as “a natural extension of that philosophy—from the ground into the sky.”
About the companies
Joby Aviation (NYSE: JOBY) is a California-based transportation company developing an all-electric eVTOL air taxi. The company intends to operate its own air taxi service in cities worldwide and sell aircraft to other operators and partners.
Toyota (NYSE: TM) has operated in North America for nearly 70 years and says it is focused on sustainable, next-generation mobility through Toyota and Lexus brands. Toyota reports nearly 64,000 employees in North America, 14 manufacturing plants, and more than 1,800 dealerships. The company also noted that its North Carolina plant began assembling automotive batteries for electrified vehicles in 2025.
What to watch for next
For readers tracking the air taxi sector, the next milestones will likely center on:
- Details on how the joint venture will be structured operationally
- Updates on Joby’s certification progress and production ramp timelines
- Signs of how manufacturing improvements translate into cost reductions and throughput
- Additional agreements or expanded collaboration as the alliance progresses
While the companies highlighted expected benefits, they also noted the usual forward-looking risks—such as regulatory certification timelines, market conditions, and the ability to finalize additional agreements.
Source: Toyota Motor North America / PRNewswire (June 30, 2026)
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Consumer Corner
The Evolution of Retail Technology: Connecting Consumers to Valuable Product Information
The Evolution of Retail Technology: Connecting Consumers to Valuable Product Information
(Feature Impact) For more than 50 years, traditional universal product codes (UPCs), better known as barcodes, have automated checkout, powered retail and kept the world’s products moving one scan at a time.
Watch this video to learn more
Now it’s time for the next chapter. Brands and retailers are transitioning to QR codes powered by GS1 to enhance everyday shopping experiences, unlock more information and empower customers to make informed purchase decisions with a simple smartphone scan – while still going “beep” at the register.
For decades, UPC barcodes simply provided the price of an item, but today’s shoppers are looking for more information. This retail-labeling transformation will include advanced QR codes that unlock information about ingredients, allergens, freshness, product origin, sustainability details, recipes and more. Retailers have set a 2027 target to accept these QR codes at checkout, which can help them better operate and serve their customers in numerous ways, such as preventing recalled products from being sold.
They can also help reduce food waste, save consumers money and help people make smarter purchases by encouraging shoppers to have a richer experience with the products they’re putting in their carts.
Visit gs1us.org/smarter to discover more about the future of shopping and checkout.
SOURCE:
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STM Blog
From Hand Signals to Smart Crosswalks: The Evolution of the Modern Pedestrian Signal
Discover the history of the modern pedestrian signal, from Garrett A. Morgan’s groundbreaking traffic signal to today’s smart, accessible crosswalks.
Last Updated on July 12, 2026 by Daily News Staff
Every day, millions of people rely on pedestrian signals to cross busy street safely. A glowing white walking figure, an orange-red hand, and a countdown timer have become familiar sights around the world. While these signals may seem like simple pieces of infrastructure, they are the result of more than a century of innovation, engineering, and public safety improvements.
The modern pedestrian signal did not appear overnight. Instead, it evolved through the contributions of inventors, engineers, city planners, and transportation officials who continually refined traffic control systems as cities grew and automobiles became more common.
The Early Days of Traffic Control
Before electric traffic signals, intersections were controlled by police officers, railway-style semaphores, or even hand signals. As horse-drawn wagons gave way to automobiles in the early 1900s, traffic congestion and accidents increased dramatically, creating an urgent need for better traffic management.
One of the earliest electric traffic lights was installed in Cleveland, Ohio, in 1914. It used red and green lights and was manually operated. While it improved vehicle movement, pedestrians still had to judge for themselves when it was safe to cross.
Garrett A. Morgan’s Breakthrough
One of the most important milestones came in 1923 when inventor and entrepreneur Garrett Augustus Morgan received U.S. Patent No. 1,475,024 for an improved traffic signal.
Morgan’s design introduced a third position in addition to “Stop” and “Go.” This intermediate phase temporarily stopped traffic in every direction before allowing vehicles to proceed. The brief pause reduced confusion at intersections and provided additional time for pedestrians to cross safely.
Morgan reportedly developed his design after witnessing a serious traffic accident. His invention demonstrated how thoughtful engineering could improve public safety while making increasingly busy streets more efficient.
Although Morgan did not invent the illuminated “WALK” and “DON’T WALK” pedestrian signal used today, his three-position signal became a foundational step in the evolution of modern traffic control.
The Birth of Dedicated Pedestrian Signals
As cities expanded after World War II, pedestrian safety became an even greater concern. More people were walking in increasingly crowded downtown districts, and separating pedestrian movements from vehicle traffic became a priority.
During the early 1950s, several American cities began experimenting with dedicated pedestrian signals. New York City became one of the first major municipalities to install illuminated “WALK” and “DON’T WALK” signs at busy intersections.
These early systems gave pedestrians their own designated crossing phase, reducing conflicts with turning vehicles and improving safety at some of the nation’s busiest intersections.
Standardization Across America
By the 1960s and 1970s, traffic engineers recognized the importance of creating consistent traffic control devices nationwide.
The Manual on Uniform Traffic Control Devices (MUTCD) established national standards for traffic signs, pavement markings, and pedestrian signals. Standardized designs helped ensure that pedestrians could understand crossing signals regardless of where they traveled in the United States.
Eventually, words gave way to internationally recognized symbols—a walking person to indicate it was safe to cross and an upraised hand to indicate pedestrians should wait. These symbols transcended language barriers and improved accessibility for visitors and non-English speakers.
The Countdown Era
One of the most significant modern improvements arrived with pedestrian countdown timers.
Rather than simply flashing a warning, countdown displays show exactly how many seconds remain before the crossing phase ends. Research has shown that countdown timers help pedestrians make better crossing decisions and improve compliance with traffic signals.
Today, countdown timers have become standard equipment at intersections across much of the United States.
Accessibility Takes Center Stage
Modern pedestrian signals are designed to serve everyone.
Accessible Pedestrian Signals (APS) now provide audible tones, spoken messages, vibrating push buttons, and locator sounds that assist pedestrians who are blind or have low vision. These features allow more people to navigate intersections independently and safely.
The continued development of accessible technology reflects a broader commitment to making transportation systems inclusive for all users.
The Future of Pedestrian Safety
Pedestrian signals continue to evolve.
Many cities now use smart traffic systems that detect pedestrians waiting to cross, automatically adjust signal timing based on traffic conditions, and prioritize people walking during busy periods.
Researchers are exploring artificial intelligence, connected vehicle technology, and sensor-based systems capable of communicating directly with autonomous vehicles. Future pedestrian crossings may adapt in real time to weather conditions, crowd sizes, emergency vehicles, and even the needs of older adults or individuals with disabilities.
A Legacy Built by Many Innovators
The pedestrian signal we know today is the product of more than a century of collaboration and innovation.
Early traffic engineers created the first electric traffic lights. Garrett A. Morgan improved intersection safety with his groundbreaking three-position traffic signal. Transportation agencies standardized traffic control devices, while engineers continued refining pedestrian technology through countdown timers, accessible features, and intelligent traffic systems.
Every safe crossing today reflects the work of countless inventors, planners, researchers, and public officials dedicated to protecting lives.
As cities continue to grow and transportation technology advances, the humble pedestrian signal remains one of the most effective—and often overlooked—public safety innovations ever developed.
At STM Daily News, we celebrate the inventors, engineers, and visionaries whose everyday innovations quietly improve life for millions of people. Sometimes the most important inventions aren’t the ones that grab headlines—they’re the ones we depend on every single day without giving them a second thought.
Related Reading
- Federal Highway Administration – Manual on Uniform Traffic Control Devices (MUTCD)
- National Museum of African American History and Culture – Garrett Augustus Morgan
- United States Patent and Trademark Office
- Federal Highway Administration – Accessible Pedestrian Signals
- National Highway Traffic Safety Administration (NHTSA)
🧠 Discover the remarkable innovators, inventors, and trailblazers who helped shape our world but rarely receive the recognition they deserve. Share your thoughts in the comments and subscribe to the STM Daily News newsletter to catch every new Forgotten Genius Friday feature and more inspiring stories delivered to your inbox.
