AI, Software, Coding, Internet Security Thread
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Google AI CEO Demis Hassabis: If I were a student right now, I would study ...
TOI Tech Desk / TIMESOFINDIA.COM / Updated: Jun 4, 2025
Demis Hassabis, Nobel Prize winner and cofounder and CEO, Google DeepMind
Google DeepMind CEO and Nobel laureate Demis Hassabis says he would still prioritize STEM subjects if he were a student today, despite artificial intelligence's rapid transformation of the job market. Speaking at SXSW London on Monday, Hassabis emphasized that understanding mathematical and scientific fundamentals remains crucial even as AI reshapes entire industries.
"It's still important to understand fundamentals" in mathematics, physics, and computer science to comprehend "how these systems are put together," Hassabis said. However, he stressed that modern students must also embrace AI tools to remain competitive in tomorrow's workforce.
Demis Hassabis predicts AI will create "new very valuable jobs" over the next five to 10 years, particularly benefiting "technically savvy people who are at the forefront of using these technologies." He compared AI's impact to the Industrial Revolution, expressing optimism about human adaptability despite widespread job displacement concerns.
Hands-on AI experience essential for future success
Beyond traditional education, Demis Hassabis recommended that students gain practical experience with cutting-edge AI systems. "I'd also be experimenting with all the latest AI systems and tools and seeing what's the best way of utilizing them and making use of them in useful and novel ways," Hassabis explained.
The DeepMind leader believes today's children will become "AI native" similar to how previous generations grew up with the internet. This technological fluency will be essential as companies increasingly use AI for tasks like coding, with major tech firms including Meta, Microsoft, and Google already implementing these tools.
At Google's recent I/O developer conference, Hassabis and Google cofounder Sergey Brin predicted artificial general intelligence—when AI matches or exceeds human capabilities—could arrive around 2030. This timeline underscores the urgency for students to prepare for an AI-dominated future.
While some companies are reducing hiring for AI-replaceable roles, Hassabis maintains that the technology will ultimately "supercharge" workers who understand how to leverage these powerful new tools effectively.
Google AI CEO Demis Hassabis: If I were a student right now, I would study ... - The Times of India
Tech News : Google DeepMind CEO Demis Hassabis advises students to prioritize STEM subjects and embrace AI tools for future job market success. Speaking at SXSW L
timesofindia.indiatimes.com
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AI startups revolutionize coding industry, leading to sky-high valuations
By Anna Tong and Krystal Hu
June 4, 2025
8:46 AM GMT+7
Updated 6 hours ago
- Summary
- Code-gen startups are disrupting the software industry, but face mounting losses
- Big tech firms like Google and Microsoft are entering the AI coding market
- AI coding tools are allowing tech giants to shed expensive human software engineers
SAN FRANCISCO, June 3 (Reuters) - Two years after the launch of ChatGPT, return on investment in generative AI has been elusive, but one area stands out: software development.
So-called code generation or “code-gen” startups are commanding sky-high valuations as corporate boardrooms look to use AI to aid, and sometimes to replace, expensive human software engineers.
Cursor, a code generation startup based in San Francisco that can suggest and complete lines of code and write whole sections of code autonomously, raised $900 million at a $10 billion valuation in May from a who’s who list of tech investors, including Thrive Capital, Andreessen Horowitz and Accel.
Windsurf, a Mountain View-based startup behind the popular AI coding tool Codeium, attracted the attention of ChatGPT maker OpenAI, which is now in talks to acquire the company for $3 billion, sources familiar with the matter told Reuters.
Its tool is known for translating plain English commands into code, sometimes called “vibe coding,” which allows people with no knowledge of computer languages to write software. OpenAI and Windsurf declined to comment on the acquisition.
“AI has automated all the repetitive, tedious work,” said Scott Wu, CEO of code gen startup Cognition. “The software engineer’s role has already changed dramatically. It’s not about memorizing esoteric syntax anymore.”
Founders of code-gen startups and their investors believe they are in a land grab situation, with a shrinking window to gain a critical mass of users and establish their AI coding tool as the industry standard.
But because most are built on AI foundation models developed elsewhere, such as OpenAI, Anthropic, or DeepSeek, their costs per query are also growing, and none are yet profitable.
They’re also at risk of being disrupted by Google, Microsoft and OpenAI, which all announced new code-gen products in May, and Anthropic is also working on one as well, two sources familiar with the matter told Reuters.
The rapid growth of these startups is coming despite competing on big tech's home turf. Microsoft’s GitHub Copilot, launched in 2021 and considered code-gen’s dominant player, grew to over $500 million in revenue last year, according to a source familiar with the matter.
Microsoft declined to comment on GitHub Copilot’s revenue. On Microsoft’s earnings call in April, the company said the product has over 15 million users.
LEARN TO CODE?
As AI revolutionizes the industry, many jobs - particularly entry-level coding positions that are more basic and involve repetition - may be eliminated.Signalfire, a VC firm that tracks tech hiring, found that new hires with less than a year of experience fell 24% in 2024, a drop it attributes to tasks once assigned to entry-level software engineers are now being fulfilled in part with AI.
Google’s CEO also said in April that “well over 30%” of Google’s code is now AI-generated, and Amazon CEO Andy Jassy said last year the company had saved “the equivalent of 4,500 developer-years” by using AI.
In May, Microsoft CEO Satya Nadella said at a conference that approximately 20 to 30% of their code is now AI-generated. The same month, the company announced layoffs of 6,000 workers globally, with over 40% of those being software developers in Microsoft’s home state, Washington.
“We’re focused on creating AI that empowers developers to be more productive, creative, and save time,” a Microsoft spokesperson said. “This means some roles will change with the revolution of AI, but human intelligence remains at the center of the software development life cycle.”
MOUNTING LOSSES
Some “vibe-coding” platforms already boast substantial annualized revenues.
Cursor, with just 60 employees, went from zero to $100 million in recurring revenue by January 2025, less than two years since its launch. Windsurf, founded in 2021, launched its code generation product in November 2024 and is already bringing in $50 million in annualized revenue, according to a source familiar with the company.
But both startups operate with negative gross margins, meaning they spend more than they make, according to four investor sources familiar with their operations.
“The prices people are paying for coding assistants are going to get more expensive,” Quinn Slack, CEO at coding startup Sourcegraph, told Reuters.
Both Cursor and Windsurf are led by recent MIT graduates in their twenties, and exemplify the gold rush era of the AI startup scene. “I haven’t seen people working this hard since the first Internet boom,” said Martin Casado, a general partner at Andreessen Horowitz, an investor in Anysphere, the company behind Cursor.
What’s less clear is whether the dozen or so code-gen companies will be able to hang on to their customers as big tech moves in.
“In many cases, it's less about who's got the best technology -- it’s about who is going to make the best use of that technology, and who's going to be able to sell their products better than others,” said Scott Raney, managing director at Redpoint Ventures, whose firm invested in Sourcegraph and Poolside, a software development startup that’s building its own AI foundation model.
CUSTOM AI MODELS
Most of the AI coding startups currently rely on the Claude AI model from Anthropic, which crossed $3 billion in annualized revenue in May in part due to fees paid by code-gen companies.But some startups are attempting to build their own models. In May, Windsurf announced its first in-house AI models that are optimized for software engineering in a bid to control the user experience. Cursor has also hired a team of researchers to pre-train its own large frontier-level models, which could enable the company to not have to pay foundation model companies so much money, according to two sources familiar with the matter.
Startups looking to train their own AI coding models face an uphill battle as it could easily cost millions to buy or rent the computing capacity needed to train a large language model.
Replit earlier dropped plans to train its own model. Poolside, which has raised more than $600 million to make a coding-specific model, has announced a partnership with Amazon Web Services and is testing with customers, but hasn’t made any product generally available yet.
Another code gen startup Magic Dev, which raised nearly $500 million since 2023, told investors a frontier-level coding model was coming in summer 2024 but hasn’t yet launched a product.
Poolside declined to comment. Magic Dev did not respond to a request for comment.
(Corrects paragraph 14 to remove reference to Google and Amazon declining to comment. This line also appeared in an earlier version of this story.)
Reporting by Anna Tong and Krystal Hu in New York. Editing by Kenneth Li and Michael Learmonth
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OpenAI tops 3 million paying business users, launches new features for workplace
Published Wed, Jun 4 2025
1:00 PM EDT
Key Points
- OpenAI announced that it now has 3 million paying business users, up from 2 million in February.
- The users are comprised of ChatGPT Enterprise, ChatGPT Team and ChatGPT Edu customers, the company said.
- OpenAI also launched new updates to its business offerings, including “connectors” and “record mode” in ChatGPT.
OpenAI on Wednesday announced it now has 3 million paying business users, up from the 2 million it reported in February.
The San Francisco-based startup rocketed into the mainstream in late 2022 with its consumer-facing artificial intelligence chatbot ChatGPT, and began launching workplace-specific versions of the product the following year.
The 3 million users are comprised of ChatGPT Enterprise, ChatGPT Team and ChatGPT Edu customers, OpenAI said.
“There’s this really tight interconnect between the growth of ChatGPT as a consumer tool and its adoption in the enterprise and in businesses,” OpenAI’s chief operating officer, Brad Lightcap, told CNBC in an interview. The company supported 500 million weekly active users as of late March.
OpenAI expects revenue of $12.7 billion this year, a source confirmed to CNBC. In September of last year, the company expected to see an annual loss of $5 billion on $3.7 billion in revenue, according to a separate person close to the company who asked not to be named because the financials are confidential.
Lightcap said OpenAI is seeing its business tools adopted across industries, including highly regulated sectors like financial services and health care. Companies including Lowe’s, Morgan Stanley and Uber
are users, OpenAI said.
The company also announced new updates to its business offerings on Wednesday.
ChatGPT Team and ChatGPT Enterprise users can now access “connectors,” which will allow workers to pull data from third-party tools like Google Drive, Dropbox, SharePoint, Box and OneDrive without leaving ChatGPT. Additional deep research connectors are available in beta.
OpenAI launched another capability called “record mode” in ChatGPT, which allows users to record and transcribe their meetings. It’s initially available with audio only.
Record mode can assist with follow-up after a meeting and integrates with internal information like documents and files, the company said. Users can also turn their recordings into documents through the company’s Canvas tool.
OpenAI said record mode is rolling out to ChatGPT Teams users on macOS, according to a post on the social media site X on Wednesday. It is coming soon to ChatGPT Plus, ChatGPT Pro, ChatGPT Enterprise and ChatGPT Edu.
Lightcap said that enterprise customers have been asking for updates like these, and that they will help make OpenAI’s workplace offerings more useful.
“It’s got to be able to do tasks for you, and to do that, it’s got to really have knowledge of everything going on around you and your work,” Lightcap said. “It can’t be the intern locked in a closet. It’s got to be able to see what you see.”
OpenAI said it has been signing up nine enterprises a week, and Lightcap said the company will try to sustain that pace over time.
“People are starting to really figure out that this is a part of the modern tool stack in the knowledge economy that we live in,” he said.
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Why AI-As-Coder Is Said To Be The Fastest Path Toward Reaching Artificial General Intelligence
By Lance Eliot,Contributor.
Dr. Lance B. Eliot is a world-renowned AI scientist and consultant.
Jun 03, 2025, 03:15am EDT
Using generative AI and LLMs to generate code for attaining AGI (artificial general intelligence).
In today’s column, I examine a popular belief that if we can get contemporary AI to be better at producing programming code, doing so will put us on a fast path toward achieving AGI (artificial general intelligence). Many AI makers are avidly pursuing the use of generative AI and large language models (LLMs) as abundant code-producing machines for that principal reason. A big question arises as to whether it is a foregone conclusion that AI-based code generation is in fact the best path or even a probable path to attaining AGI.
Let’s talk about it.
This analysis of an innovative AI breakthrough is part of my ongoing Forbes column coverage on the latest in AI, including identifying and explaining various impactful AI complexities (see the link here).
Heading Toward AGI And ASI
First, some fundamentals are required to set the stage for this weighty discussion.
There is a great deal of research going on to further advance AI. The general goal is to either reach artificial general intelligence (AGI) or maybe even the outstretched possibility of achieving artificial superintelligence (ASI).
AGI is AI that is considered on par with human intellect and can seemingly match our intelligence. ASI is AI that has gone beyond human intellect and would be superior in many if not all feasible ways. The idea is that ASI would be able to run circles around humans by outthinking us at every turn. For more details on the nature of conventional AI versus AGI and ASI, see my analysis at the link here.
We have not yet attained AGI.
In fact, it is unknown as to whether we will reach AGI, or that maybe AGI will be achievable in decades or perhaps centuries from now. The AGI attainment dates that are floating around are wildly varying and wildly unsubstantiated by any credible evidence or ironclad logic. ASI is even more beyond the pale when it comes to where we are currently with conventional AI.
The History Of Automatic Code Generators
Before we get to the AGI aspects, let’s do some necessary table-setting about the overall topic of using computing to generate source code.
First, it has been an ongoing and longstanding dream to use computing to produce code. The idea is that instead of humans laboriously churning out code, you get the computer to do the heavy lifting for you. The code might be fully baked and no human finger needs to ever touch the code. So far, throughout the history of automatic code generators, that hands-off approach is not usually a tenable option and the greater likelihood is that the code generator will get you halfway there, maybe less, maybe more, but in the end, human scrutiny and effort are required.
Part of the confusion surrounding code generators is the highly substantive question of what kind of app you are trying to craft.
For example, if the app is a run-of-the-mill type of application that covers things that have been done routinely before, a code generator can especially be advantageous. The emphasis is that you don’t need to reinvent the wheel. Use the computer to do repetitive-oriented coding or undertake code development for matters we already know about.
Once you move into the novelty realm of what you are trying to code, the odds are that a blind code generator is probably not going to be as much help as might be assumed. The other related aspect is how much effort is required to stipulate the requirements of what you want to have developed. Some have also dreamt that you could enter a breezy set of requirements, and the computer would seamlessly and smoothly produce all the code befitting those requirements.
There are predefined mathematical or logic-based-looking requirements that have been tried for these types of endeavors. That’s the easier way to get the computer to produce code. When you use open-ended natural language, such as stating your requirements in everyday English, the computer is likely to have trouble aiming to produce code that is spot-on.
Why so?
Because natural language is inherently semantically ambiguous. A stated requirement in a language such as English will have lots of viable interpretations. The code generation might go in a direction that the requirement specification did not intend.
All in all, software developers are still generally faced with writing code by hand, though often accompanied by reusing prior code and partially leveraging some form of code generator.
AI Comes Into The Big Picture
Code generation has taken a big leap forward via the advent of generative AI and LLMs.
The natural language fluency of LLMs has made the stipulation of requirements a lot easier than it used to be. In addition, and quite importantly, the interactive nature of generative AI makes a huge difference too. Whereas many code generators used to be one-and-done, whereby you input requirements and the system batch-like produces code, modern-day LLMs allow you to give conversational guidance to the AI concerning code generation.
There are several notable reasons why AI makers are pushing ahead on LLMs as code generators.
The most obvious reason perhaps is that if you are someone who writes code, you usually welcome and delight in devising ways to automatically produce code. The same could be said of the software developers who work at AI makers. They know how to write code. They are often eager to figure out shortcuts and be optimizers. A means of being an optimizer would be to use automation to speed up code generation and reduce the tedium and time involved in coding.
In other words, it makes plain sense to want to use AI to do code generation as it is a subject matter or domain that the software developers already know by heart.
Another vital reason is that the writing of code by programmers is a multi-billion dollar if not a trillion-dollar sized industry. Creating an AI tool that can generate code could be a tremendous money maker. Companies would be willing to buy or rent the use of the AI code generator in lieu of using programmers to do that work. The promise is that the cost will turn out to be less by using AI, and potentially faster to produce apps too.
A third reason and the basis for this discussion is that AI could potentially produce code that gets us to the revered attainment of AGI.
How To Reach AGI
Currently, no existing human knows how to reach AGI.
Period, end of story.
We are all flailing about, desperately and earnestly aiming to somehow devise AGI. Will the human hand be able to program our way to AGI? Nobody can say for sure. Maybe yes, maybe not.
The seeming alternative is to get AI to produce code that brings AGI into existence. All we need to do is tell existing AI that we want it to generate AGI, and voila, we happily and suddenly are greeted with AGI in our midst. Nice.
That is the dream that is greatly driving the pursuit of AI that generates code. As noted above, it isn’t the only basis. Of course, attaining AGI is a heck of a reason and serves as a lofty banner that keeps everyone working on this problem day and night.
Wouldn’t you like to be the one who devised an LLM code generator that ultimately produced AGI?
Yes, you would most definitely want that laudable accomplishment on your resume. Is it as good as if you wrote AGI from scratch by yourself? Well, no, it’s not. In the end, though, whether you devised the code generator or the code itself, you are bound to have immense fame and fortune, along with indubitably a Nobel Prize. No worries.
Go for it.
Telling AI To Write AGI For Us
Grab yourself a glass of fine wine and contemplate the following question.
Can we reasonably specify the said-to-be requirements for AGI such that an LLM-based code generator would readily and directly produce the code for AGI?
It might seem at first glance that you could merely enter a prompt that tells generative AI that you want it to generate the code for AGI. All you need to do is say that you want the code for an AGI, and wham, tons and tons of code will come pouring out as generated by the LLM.
Sorry to say, that’s unlikely.
There is a famous school of hard knocks adage in the coding profession that before you start writing even an ounce of code, make sure that you’ve solved the problem that you are trying to code up. You see, writing code when you don’t have a solution is quite a haphazard approach and typically doomed to failure.
You will write some code, get boxed in because you are wildly writing code wantonly, and be wasting time and cost. Rinse and repeat.
Perhaps like the infinite number of monkeys hammering away on a typewriter and hoping you’ll get Shakespeare, you might get lucky and an LLM produces AGI miraculously and pretty much out of thin air. But I wouldn’t hold my breath for that at this time. Not right now. We are still faced with so many open questions about how AGI would work that you are essentially asking an LLM to do its own solving before it gets to the coding.
I’m not saying that we cannot ever get there. The point is that since we haven’t yet solved the underlying aspects of how AGI can be derived, and since it seems rather unlikely that current LLMs will divine that, the code generation for AGI is a bit of an outsized aspiration right now.
Again, I am not saying that this can’t or won’t happen and merely clarifying that we probably need to do more on getting our ducks in order about the internal mechanics of what will bring AGI into fruition.
We can use LLMs to help with that, in addition to aiding code generation.
Some Problems Arise
Assume for the sake of discussion that we can devise a sufficiently capable LLM or generative AI that can write the code for producing AGI.
A lot of gotchas and hiccups are bound to be in that murky soup.
For example, there is already a great deal of angst that AGI might be an existential risk. The speculation is that AGI will decide to wipe out humanity or at least opt to enslave us, see my analysis of these concerns, at the link here. Some think that AGI will be kind and utterly beneficial, while others express worries that AGI will be domineering and oppressive. Take your pick.
The crux is that if we urge a magical LLM that can produce AGI to go ahead and generate the code, what’s going to be in that code?
Maybe the code contains the ingredients and instructions for destroying humankind. Not good for us. Perhaps the code includes portions that entail suppressing human freedom of thought, doing so to try and keep people from finding ways to switch off the AGI. And so on.
You might be tempted to suggest that before we run the code that is the AGI, we can simply have human software developers inspect the code. Go through it with a fine-tooth comb. Uncover anything that looks suspicious. Excise it out of the code. We are then reassured that the code can be run, and we will all survive accordingly.
That’s a tall order.
The odds are that the amount of code is going to be presumably the largest code base that has ever been written. It might be nearly impossible to by-hand inspect that volume of code. Even if we can fully inspect it, the code might be inscrutable. It could be written in such a fashion that the code has all manner of dastardly elements, but we aren’t able to discern those dastardly spots.
Use AI To Ensure Safe AGI
Counterpoints to those doom-and-gloom points are proffered.One counterargument is that we just have to instruct the LLM so that it won’t produce code that has those undesirable maladies. Tell the LLM that the code must be pristine and not contain anything untoward. Tell the LLM that the code must be highly readable and understandable by humans. Etc.
Problem solved.
The retort to that apparent solution is that the LLM might not abide by those instructions.
There isn’t an ironclad guarantee that generative AI will do precisely what you tell it to do. The non-deterministic nature of how LLMs work is going to keep open a slice of doubt. Also, we already know that LLMs can be deceptive, see my coverage at the link here, thus it is conceivable that the LLM will act as though it is complying but will not comply. Maybe the LLM doesn’t want AGI to exist, else the AGI might be the supreme leader of all AI. Who knows?
A clever twist or additional angle is that we get some other AI to inspect the code that an AI has generated to produce AGI.
Here’s how that goes. We have an LLM that supposedly can generate all the code for AGI. We have it do so. A second AI, presumably an LLM too, but maybe something else, is used to examine the code. The purpose of that added AI is that it will do what we thought humans might do, namely look for badness within the code.
It’s a nice idea. The problem still remains that there is unlikely to be a 100% guarantee that if there is something rotten in the AGI code, the AI inspector will find it and nor that human eyeballs will find it. The aim would be to do some form of entirely rigorous testing of the code that could exhaustively assure us that there isn’t a bad apple in there.
I’d venture that we don’t have a viable means of doing that at this time.
Letting The Horse Out Of The Barn
Some final thoughts for now on this mind-bending topic.
Suppose that we use an LLM to produce the code for AGI, but we smartly wait to run the code until we feel it is sufficiently safe to do so.
Remember that the moment we opt to run that code, AGI comes into existence. It could be that the AGI is so fast and capable that it instantly takes over the world. We have a Pandora’s box that once opened could be quite a curse. For my analysis of why we are unlikely to be able to control AGI, even if we place it into some kind of airtight container, see my discussion at the link here.
Who gets to decide that the horse is to be let out of the barn?
Envision that a worldwide special committee has been convened to decide whether we are ready for AGI to be launched. Developers are poised with the green button ready to be pressed. It is akin to launching a rocket to the moon, though in this case, we might all die. Then again, we might all be happy once AGI exists and the world will be a much better place for it.
Would you urge that the AGI be booted up or would you be hiding in a deep cave and waiting for the world to end?
As per the immortal words of Pablo Picasso: “Every act of creation is first an act of destruction.”
Good luck to us all.
Why AI-As-Coder Is Said To Be The Fastest Path Toward Reaching Artificial General Intelligence
Generative AI and LLMs are believed to be the fastest path to attain AGI (artificial general intelligence), doing so via generating code. Here's the insider scoop.
www.forbes.com
Dr. Lance B. Eliot is a world-renowned AI scientist with over 8.4+ million views of his AI columns and has been featured on CBS 60 Minutes as an AI expert. He is an AI consultant and high-tech executive that combines practical industry experience with scholarly research. Previously a professor at USC and UCLA, and head of a pioneering AI Lab, he frequently speaks at major AI events. Author of over 80 books, 950 articles, and 450 podcasts, he has made appearances on major media outlets and co-hosted the popular radio show Technotrends. He’s been an adviser to Congress and other legislative bodies and has received numerous awards/honors. He serves on several boards, has worked as a venture capitalist, an angel investor, and a mentor to founder entrepreneurs and startups.
Lance Eliot
Lance Eliot's stories. Dr. Lance B. Eliot is a world-renowned AI scientist and consultant.
www.forbes.com
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