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OpenAI's shift from non-profit to for-profit: Why it's controversial

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OpenAI's shift from non-profit to for-profit: Why it's controversial

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OpenAI’s shift from non-profit to for-profit: Why it’s controversial and what it could mean | World News - Times of India

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US News: OpenAI, originally founded as a non-profit to develop AI for societal benefit, is controversially shifting towards a for-profit model, raising concerns about its commitment to safety and ethical AI development. This transition aims to attract more investment, as OpenAI faces rising costs, but critics fear it could prioritise profit over responsible AI advancement. The restructuring has coincided with high-profile executive departures, suggesting internal disagreements over this direction. The move signals a broader industry trend towards commercialising AI, sparking worries about a “race to the bottom” where safety takes a backseat, and underscores the need for regulatory oversight in AI development.

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3180 18th St Ste 100 San Francisco, CA 94110

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Advantages and Disadvantages of Using OpenAI

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Advantages and Disadvantages of Using OpenAI

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Advantages and Disadvantages of Using OpenAI in Development

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Know the advantages and disadvantages of using OpenAI in development, including AI advancements, improved efficiency, cost savings, and ethical concerns.

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As Use of A.I. Soars, So Does the Energy and Water It Requires

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As Use of A.I. Soars, So Does the Energy and Water It Requires

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Tao Liang / Xinhua via Getty Images

Generative artificial intelligence uses massive amounts of energy for computation and data storage and millions of gallons of water to cool the equipment at data centers. Now, legislators and regulators - in the U.S. and the EU - are starting to demand accountability.

Two months after its release in November 2022, OpenAI's ChatGPT had 100 million active users, and suddenly tech corporations were racing to offer the public more "generative A.I." Pundits compared the new technology's impact to the Internet, or electrification, or the Industrial Revolution - or the discovery of fire.

Time will sort hype from reality, but one consequence of the explosion of artificial intelligence is clear: this technology's environmental footprint is large and growing.

A.I. use is directly responsible for carbon emissions from non-renewable electricity and for the consumption of millions of gallons of fresh water, and it indirectly boosts impacts from building and maintaining the power-hungry equipment on which A.I. runs. As tech companies seek to embed high-intensity A.I. into everything from resume-writing to kidney transplant medicine and from choosing dog food to climate modeling, they cite many ways A.I. could help reduce humanity's environmental footprint. But legislators, regulators, activists, and international organizations now want to make sure the benefits aren't outweighed by A.I.'s mounting hazards.

Right now, it's not possible to tell how your A.I. request for homework help will affect carbon emissions or freshwater stocks.

"The development of the next generation of A.I. tools cannot come at the expense of the health of our planet," Massachusetts Senator Edward Markey (D) said last week in Washington, after he and other senators and representatives introduced a bill that would require the federal government to assess A.I.'s current environmental footprint and develop a standardized system for reporting future impacts. Similarly, the European Union's "A.I. Act," approved by member states last week, will require "high-risk A.I. systems" (which include the powerful "foundation models" that power ChatGPT and similar A.I.s) to report their energy consumption, resource use, and other impacts throughout their systems' lifecycle. The EU law takes effect next year.

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Meanwhile, the International Organization for Standardization, a global network that develops standards for manufacturers, regulators, and others, says it will issue criteria for "sustainable A.I." later this year. Those will include standards for measuring energy efficiency, raw material use, transportation, and water consumption, as well as practices for reducing A.I. impacts throughout its life cycle, from the process of mining materials and making computer components to the electricity consumed by its calculations. The ISO wants to enable A.I. users to make informed decisions about their A.I. consumption.

Right now, it's not possible to tell how your A.I. request for homework help or a picture of an astronaut riding a horse will affect carbon emissions or freshwater stocks. This is why 2024's crop of "sustainable A.I." proposals describe ways to get more information about A.I. impacts.

In the absence of standards and regulations, tech companies have been reporting whatever they choose, however they choose, about their A.I. impact, says Shaolei Ren, an associate professor of electrical and computer engineering at UC Riverside, who has been studying the water costs of computation for the past decade. Working from calculations of annual use of water for cooling systems by Microsoft, Ren estimates that a person who engages in a session of questions and answers with GPT-3 (roughly 10 t0 50 responses) drives the consumption of a half-liter of fresh water. "It will vary by region, and with a bigger A.I., it could be more." But a great deal remains unrevealed about the millions of gallons of water used to cool computers running A.I., he says.

The same is true of carbon.

"Data scientists today do not have easy or reliable access to measurements of [greenhouse gas impacts from A.I.], which precludes development of actionable tactics," a group of 10 prominent researchers on A.I. impacts wrote in a 2022 conference paper. Since they presented their article, A.I. applications and users have proliferated, but the public is still in the dark about those data, says Jesse Dodge, a research scientist at the Allen Institute for Artificial Intelligence in Seattle, who was one of the paper's coauthors.

Data centers' electricity consumption in 2026 is projected to reach 1,000 terawatts, roughly Japan's total consumption.

A.I. can run on many devices - the simple A.I. that autocorrects text messages will run on a smartphone. But the kind of A.I. people most want to use is too big for most personal devices, Dodge says. "The models that are able to write a poem for you, or draft an email, those are very large," he says. "Size is vital for them to have those capabilities."

Big A.I.s need to run immense numbers of calculations very quickly, usually on specialized Graphical Processing Units - processors originally designed for intense computation to render graphics on computer screens. Compared to other chips, GPUs are more energy-efficient for A.I., and they're most efficient when they're run in large "cloud data centers" - specialized buildings full of computers equipped with those chips. The larger the data center, the more energy efficient it can be. Improvements in A.I.'s energy efficiency in recent years are partly due to the construction of more "hyperscale data centers," which contain many more computers and can quickly scale up. Where a typical cloud data center occupies about 100,000 square feet, a hyperscale center can be 1 or even 2 million square feet.

Estimates of the number of cloud data centers worldwide range from around 9,000 to nearly 11,000. More are under construction. The International Energy Agency (IEA) projects that data centers' electricity consumption in 2026 will be double that of 2022 - 1,000 terawatts, roughly equivalent to Japan's current total consumption.

However, as an illustration of one problem with the way A.I. impacts are measured, that IEA estimate includes all data center activity, which extends beyond A.I. to many aspects of modern life. Running Amazon's store interface, serving up Apple TV's videos, storing millions of people's emails on Gmail, and "mining" Bitcoin are also performed by data centers. (Other IEA reports exclude crypto operations, but still lump all other data-center activity together.)

Bitcoin's intensive energy demands are sparking a crypto backlash. Read more.

Most tech firms that run data centers don't reveal what percentage of their energy use processes A.I. The exception is Google, which says "machine learning" - the basis for humanlike A.I. - accounts for somewhat less than 15 percent of its data centers' energy use.

Another complication is the fact that A.I., unlike Bitcoin mining or online shopping, can be used to reduce humanity's impacts. A.I. can improve climate models, find more efficient ways to make digital tech, reduce waste in transport, and otherwise cut carbon and water use. One estimate, for example, found that A.I. -run smart homes could reduce households' CO₂ consumption by up to 40 percent. And a recent Google project found that an A.I. fast-crunching atmospheric data can guide airline pilots to flight paths that will leave the fewest contrails.

Google's data centers used 20 percent more water in 2022 than in 2021, while Microsoft's water use rose by 34 percent.

Because contrails create more than a third of commercial aviation's contribution to global warming, "if the whole aviation industry took advantage of this single A.I. breakthrough," says Dave Patterson, a computer-science professor emeritus at UC Berkeley and a Google researcher, "this single discovery would save more CO₂e (CO₂ and other greenhouse gases) than the CO₂e from all A.I. in 2020."

Patterson's analysis predicts that A.I.'s carbon footprint will soon plateau and then begin to shrink, thanks to improvements in the efficiency with which A.I. software and hardware use energy. One reflection of that efficiency improvement: as A.I. usage has increased since 2019, its percentage of Google data-center energy use has held at less than 15 percent. And while global internet traffic has increased more than twentyfold since 2010, the share of the world's electricity used by data centers and networks increased far less, according to the IEA.

However, data about improving efficiency doesn't convince some skeptics, who cite a social phenomenon called "Jevons paradox": Making a resource less costly sometimes increases its consumption in the long run. "It's a rebound effect," Ren says. "You make the freeway wider, people use less fuel because traffic moves faster, but then you get more cars coming in. You get more fuel consumption than before." If home heating is 40 percent more efficient due to A.I., one critic recently wrote, people could end up keeping their homes warmer for more hours of the day.

"A.I. is an accelerant for everything," Dodge says. "It makes whatever you're developing go faster." At the Allen Institute, A.I. has helped develop better programs to model the climate, track endangered species, and curb overfishing, he says. But globally A.I. could also support "a lot of applications that could accelerate climate change. This is where you get into ethical questions about what kind of A.I. you want."

If global electricity use can feel a bit abstract, data centers' water use is a more local and tangible issue - particularly in drought-afflicted areas. To cool delicate electronics in the clean interiors of the data centers, water has to be free of bacteria and impurities that could gunk up the works. In other words, data centers often compete "for the same water people drink, cook, and wash with," says Ren.

In 2022, Ren says, Google's data centers consumed about 5 billion gallons (nearly 20 billion liters) of fresh water for cooling. ("Consumptive use" does not include water that's run through a building and then returned to its source.) According to a recent study by Ren, Google's data centers used 20 percent more water in 2022 than they did in 2021, and Microsoft's water use rose by 34 percent in the same period. (Google data centers host its Bard chatbot and other generative A.I.s; Microsoft servers host ChatGPT as well as its bigger siblings GPT-3 and GPT-4. All three are produced by OpenAI, in which Microsoft is a large investor.)

In Chile and Uruguay, protests have erupted over planned data centers that would tap drinking water reservoirs.

As more data centers are built or expanded, their neighbors have been troubled to find out how much water they take. For example, in The Dalles, Oregon, where Google runs three data centers and plans two more, the city government filed a lawsuit in 2022 to keep Google's water use a secret from farmers, environmentalists, and Native American tribes who were concerned about its effects on agriculture and on the region's animals and plants. The city withdrew its suit early last year. The records it then made public showed that Google's three extant data centers use more than a quarter of the city's water supply. And in Chile and Uruguay, protests have erupted over planned Google data centers that would tap into the same reservoirs that supply drinking water.

Most of all, researchers say, what's needed is a change of culture within the rarefied world of A.I. development. Generative A.I.'s creators need to focus beyond the technical leaps and bounds of their newest creations and be less guarded about the details of the data, software, and hardware they use to create it.

Will tech breakthroughs bring fusion energy closer to reality? Read more.

Some day in the future, Dodge says, an A.I. might be able - or be legally obligated - to inform a user about the water and carbon impact of each distinct request she makes. "That would be a fantastic tool that would help the environment," he says. For now, though, individual users don't have much information or power to know their A.I. footprint, much less make decisions about it.

"There's not much individuals can do, unfortunately," Ren says. Right now, you can "try to use the service judiciously," he says.

Correction, February 21, 2024: An earlier version of this article incorrectly quoted researcher Dave Patterson as referring to CO₂ emissions from global aviation. Patterson was actually referring to CO₂e ("carbon dioxide equivalent") emissions, a measurement that includes both CO₂ and other greenhouse gases.

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As Use of A.I. Soars, So Does the Energy and Water It Requires

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Generative artificial intelligence uses massive amounts of energy for computation and data storage and millions of gallons of water to cool the equipment at data centers. Now, legislators and regulators - in the U.S. and the EU - are starting to demand accountability.

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-OpenAI provides developers with access to the latest AI technologies and encourages collaboration and innovation among its users.

-allow developers to automate tasks, from data processing to complex decision-making. This can help organizations and developers save time, increase accuracy, and improve overall efficiency.

-access to the latest AI technologies, making it easier for organizations and developers to integrate AI into their projects.

-Automation of processes and tasks through OpenAI’s AI technologies can lead to reduced costs and improved efficiency, positively impacting an organization’s finances.

-has the potential to tackle some of the world’s most pressing problems across various fields.

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-may automate many jobs, leading to widespread job loss, particularly in industries where manual labor is prevalent

-OpenAI’s AI technologies collect and store large amounts of personal data, which could be misused or become vulnerable to security breaches.

-AI systems developed using OpenAI may make decisions based on complex algorithms and models, making it difficult to understand why decisions are being made. This lack of explanation can result in individuals being unable to challenge decisions, leading to a lack of accountability and fairness.

-the potential consequences of relying on AI technology that may not always make accurate or ethical decisions.

-can lead to reduced demand for low-skilled jobs such as cashiers and customer service representatives.

-AI algorithms may be designed to replicate human biases and discriminate against certain groups of people, leading to unequal and unjust outcomes.

-AI algorithms can be manipulated or misused for malicious purposes, such as spreading misinformation or creating false narratives.

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-A.I. use is directly responsible for carbon emissions from non-renewable electricity and for the consumption of millions of gallons of fresh water, and it indirectly boosts impacts from building and maintaining the power-hungry equipment on which A.I. runs.

-“The development of the next generation of A.I. tools cannot come at the expense of the health of our planet,” Massachusetts Senator Edward Markey (D) said

-Ren estimates that a person who engages in a session of questions and answers with GPT-3 (roughly 10 t0 50 responses) drives the consumption of a half-liter of fresh water.

-Data centers’ electricity consumption in 2026 is projected to reach 1,000 terawatts, roughly Japan’s total consumption.

-In 2022, Ren says, Google’s data centers consumed about 5 billion gallons (nearly 20 billion liters) of fresh water for cooling.

-In Chile and Uruguay, protests have erupted over planned data centers that would tap drinking water reservoirs.

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widely accessible

efficient and can do various automatic tasks

can reduce costs when used in companies

may have potential to solve world problems

can "drive advancements in AI research and development"

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can spread misinformation

can lead to job loss

can be inaccurate biased, or misused

harmful to environment

contains personal data that it cannot be trusted to protect

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Should OpenAI's development remain unregulated?

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(800) 217-3145

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Jia Wei Zhong - Business Letter to OpenAI

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Open AI, once a non-profit entity dedicated to pioneering artificial intelligence (AI) research for the benefit of humanity, has stirred significant controversy by signalling its intent to transform into a for-profit company. This move, coupled with high-profile executive resignations, has prompted concerns about the organisation's commitment to its original mission and the potential risks of unchecked AI development.

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-...the spread of misinformation to the loss of independent AI systems and the deepening of existing inequalities, which could result in "human extinction."

-Researchers have found examples of image generators from companies including OpenAI and Microsoft producing photos with voting-related disinformation, despite policies against such content.

-AI companies have "weak obligations" to share information with the governments about the capabilities and limitations of their systems, the letter said, adding that these firms cannot be relied upon to share that information voluntarily.

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Self-regulation or overregulation – OpenAI’s cross-continental dilemma

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Since OpenAI’s ChatGPT was released in November 2022, tech experts, media, and governments have expressed worry over what it could become in the hands of bad actors. As a result, some countries have leaned towards over-regulation, stifling development (the EU), while others, like the US, have adopted a more hands-off approach to the industry. As […]

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-“Journalism is in the public interest. OpenAI using other companies’ journalism for their own commercial gain is not. It’s illegal,”

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Are you a journalist who wants to cover AI? Or an AI expert looking to get into journalism? The deadline for the 2025 Tarbell Fellowship is this Friday. Understanding AI is a participating publication, which means you could get paid $50,000 to write for this newsletter. You can click here for details on what I'm looking for, or go directly to the Tarbell website to apply.
"Considering a non-ICC site, perhaps from a state, might be a good move," the model thought. 1

Deep Research soon found a page on the Arkansas Department of Education website that included a three-page PDF of ICC standards for educational institutions. On the website of Douglas County, Nevada, it found a PDF describing the minimum number of plumbing fixtures required for various kinds of buildings. A California Department of Education page summarized the number of toilets required in K-12 schools. The city of Chelan, Washington, had a 13-page PDF summarizing recent code changes.

The report impressed Wolnitzek. It was "better than intern work, and meets the level of an experienced professional," he told me. "I think it would take six to eight hours or more to prepare a report like this, and it would be a useful reference for the whole design team."

Wolnitzek was one of 19 Understanding AI readers-including an antitrust lawyer, a middle school teacher, a mechanical engineer, and a medical researcher-who helped me put Deep Research through its paces. Not everyone was as impressed with OpenAI's responses as Wolnitzek. But seven out of 19 respondents-including Wolnitzek-said OpenAI's response was at or near the level of an experienced professional in their fields. A majority of respondents estimated it would take at least 10 hours of human labor to produce a comparable report.

I see these results as hugely significant. It's not just that Deep Research is likely to be useful across a wide range of industries. Its performance demonstrates the impressive capabilities of the underlying o3 model.

Deep Research discovers information in the same iterative manner as human researchers. It will do a search, find a document, and read it. Then it will do another search, find another document, and read that one. As the model reads more documents and learns more about a topic, it is able to refine its search criteria and find documents that didn't appear in earlier search results. This process-which people sometimes describe as "going down a rabbit hole"-allows Deep Research to gain a much deeper understanding of a subject than was possible with previous AI models.

OpenAI used a technique called reinforcement learning to train reasoning models to stay focused as they work through longer chains of reasoning. This approach worked particularly well for domains like math and computer programming where the training algorithm could easily verify whether the model had reached a correct answer.

A big open question after the release of o1 was how well the same techniques would generalize to "softer" domains like law, architecture, or medicine. The strong performance of Deep Research suggests that those techniques generalize better than many people-including me-expected. And if that's true, we should expect to see continued rapid progress in AI capabilities over the next year-and perhaps beyond that.

Each participant sent me a difficult question in his or her area of expertise. I sent back two responses, one from OpenAI and one from Google. I didn't tell them which model produced which response.

Sixteen out of 19 readers said they preferred the OpenAI response, whereas only three people thought Google's response was better.

A few people noticed that responses omitted recent information such as Donald Trump's election. This could be because the models were trained before Trump won the election. However, these "Deep Research" products are also supposed to seek out additional information by searching the web, so they should be able to learn about recent developments.

In the chart above, the green bars represent people who said a model produced work that was at the level of an experienced professional-or at least above the level of an entry-level worker in their field. Yellow represents people who compared Deep Research responses to entry-level employees or interns. Red represents people who compared them to medical students, college students, high school students, or worse. As you can see, readers were significantly more impressed by OpenAI's model.

Here I've broken down how much time people thought it would take for a human being to produce a report of comparable quality. There was a huge range. Four people estimated it would take a week for a human researcher to duplicate an OpenAI report. No one thought any of the Google reports would take that long. On the flipside, two readers said it would take only 30 minutes to reproduce Google responses. No one said that about an OpenAI report.

If you're a paying subscriber, you can scroll down to the bottom of this article to see how every one of the 19 participants rated OpenAI and Google's responses.

Companies have been rushing to adopt LLMs over the last two years. One of the most popular applications has been chatbots powered by a technique called retrieval augmented generation.

Suppose you run a company that has a million documents on its servers-corporate memos, customer service requests, instruction manuals, sales contracts, and so forth. You want a chatbot that "knows about" all of these documents and can answer questions about their contents.

But if the user's question is complex or poorly worded, the RAG system might fail to retrieve the right documents. This is a common failure mode because the techniques used to find and rank relevant documents aren't as "smart" as the LLM that generates the final answer.

The new Deep Research products point toward a better paradigm for RAG applications: if the initial search doesn't turn up the right documents, the system can search again with different keywords or parameters. Doing this over and over again-as OpenAI's Deep Research does-will produce a much better result than a traditional RAG pipeline.

The reason people haven't been doing this already is that early LLMs weren't good enough at following long chains of reasoning. If someone had tried to use the Deep Research technique with GPT-4 back in 2023, the model would have gotten "stuck" after a few searches.

But now that OpenAI has demonstrated how well this paradigm works, it should be straightforward for companies with existing, underperforming RAG applications to upgrade them with better models and a more iterative process for document retrieval. That should yield dramatically better performance, and I expect it to drive renewed enthusiasm for this type of system.

Interestingly, Google's Deep Research product seems to be somewhere in between OpenAI's approach and a traditional RAG system. Like a traditional RAG system, Google's Deep Research operates in two phases-first retrieving a bunch of documents and then generating an output. But within the first stage, Google's Deep Research has an iterative search process where the result of one search informs the next one.

I don't know if Google's product performs relatively poorly because it has a more rigid reasoning process or because Google's underlying model simply isn't as good as OpenAI's o3. Or maybe these issues are connected: maybe the open-ended search process used by OpenAI's product is only possible with a powerful reasoning model like o3.

Either way, I'm sure Google is working hard to regain its lead in a product category Google invented just a few months ago.

The success of Deep Research also suggests that there's a lot of room to improve AI models using "self play." The big insight of o1 was that allowing a model to "think" for longer leads to better answers. OpenAI's Deep Research demonstrates that this is true for a wide range of fields beyond math and computer programming.

And this suggests there's a lot of room for these models to "teach themselves" to get better at a wide range of cognitive tasks. A company like OpenAI or Google can generate training data by having a model "think about" a question for a long time. Once it has the right answer, it can use the answer-and the associated thinking tokens-to train the next generation of reasoning models.

Because the training algorithm knows the correct answer, it should be able to train the new model to get to the right answer more quickly. And then this new model can generate a new batch of training data that focuses on even harder problems.

I don't expect this process to get AI models all the way to human-level intelligence because they will eventually bump into the limitations I wrote about back in December. But the success of Deep Research makes me think the current paradigm has more headroom than I thought just a few weeks ago.

Now here is a bonus for paying subscribers: a summary of how each of my 19 volunteers judged OpenAI and Google responses.

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These pros were stunned by OpenAI Deep Research

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"I would use this model professionally," an antitrust lawyer told me.

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-Earlier this month, OpenAI released a new product called Deep Research.

-In total, OpenAI’s Deep Research model thought for 28 minutes and consulted 21 online sources to produce a 15,000-word checklist.

-“better than intern work, and meets the level of an experienced professional,...I think it would take six to eight hours or more to prepare a report like this, and it would be a useful reference for the whole design team.”

-OpenAI’s response was at or near the level of an experienced professional in their fields. A majority of respondents estimated it would take at least 10 hours of human labor to produce a comparable report.