Nat Rubio-Licht

Amid mounting concern over AI's impact on cybersecurity following OpenAI's breach of HuggingFace, the lab's latest model is a step in ameliorating the issue.

On Thursday, the company announced GPT-6 Cyber, a model that it claims is "far more reliable" at staying within the boundaries that researchers set. OpenAI claims the model is better at finding and patching vulnerabilities, offering a significant improvement over its predecessor.

OpenAI said the model is already helping secure real-world software, including finding and validating exploits in "security-critical" codebases. For instance, in Linux, the model developed 948 privilege escalation exploits, or exploits in which someone gains higher levels of control than they are granted. Additionally, it identified 81 previously unknown "high-severity" vulnerabilities in Firefox, and is being used by teams at Cisco, Calif and CodeRabbit for vulnerability research and code review. OpenAI said the model is also being used to secure its own systems.

The company tested the model on ExploitGym, the benchmark for testing whether a model can exploit a specific software vulnerability that was also involved in July's HuggingFace breach.

• OpenAI said GPT-6 Cyber scores 48.5% on this evaluation, up from 32.1% on the previous generation, GPT-5.6 Cyber. It also outperforms GPT-6 Astra, OpenAI's most advanced model, in this benchmark, which scored 42.4%.
• It also noted that GPT-6 Cyber avoided exploiting "out-of-scope services" in 99.93% of tests without a deployment prompt, or additional safety instructions that keep a model in bounds, and in 100% of tests with a deployment prompt.
• OpenAI noted that alignment was a "central focus" of GPT-6 Cyber, utilizing specialized training to help the model stay within specified boundaries.
• However, the company is also adding additional protections, including monitoring for misalignment and emerging threats, protecting against stolen credentials and blocking high-risk activity involving critical systems.

The company said it is releasing the model through the API and Codex to select members of Project Daybreak Red, its program designed for more advanced cybersecurity workflows, and that it intends to expand access over time.

"Our goal is to dramatically expand Daybreak, bringing advanced security research to organizations of every size, independent researchers, and open-source maintainers," the company said in its announcement.

Our Deeper View

It's clear that OpenAI wants to atone for the HuggingFace incident in July, with this model specifically improving on staying within containment, even going so far as to test using the same benchmarks that were involved in the incident in the first place. And based on its performance, GPT-6 Cyber does represent a step in the right direction. However, the discussion around model containment and cybersecurity goes hand-in-hand with another hot topic: pacing the development of increasingly powerful frontier AI. The reality is that, in order to stop incidents like these from happening, the development of powerful AI needs to be reined in to allow for cybersecurity defenses to catch up, something that AI researchers and experts have been warning about for months. Though OpenAI has said more than once that the window to secure software is rapidly closing, when frontier labs release new models, they are effectively making that window even smaller. While GPT-6 Cyber could immediately give defenders the ammo they need, these kinds of tools may need to be paired with efforts to slow the pace of development.

Data centers' bad reputation has come for the ballot box.

AI infrastructure has emerged as one of the most politically-charged issues of the November midterm elections, with candidates leaning into issues that US voters across the political spectrum feel strongly about. In an NBC News exit poll, 56% of voters said data centers were a “crucial or major factor” in their vote for Congress, while only 13% said the issue played no role at all.

And anti-data center sentiment showed up strongly in the results. Time reported that Democratic candidates spent more than $100 million on broadcast advertising that painted Republican opponents as pro-Big Tech and data centers, allowing these candidates to win key races in Ohio, Kansas and Iowa.

Beyond the candidates themselves, dozens of local ballot measures across the country asked whether to limit or permit data center development in their communities. Redbud Consulting, a polling and data strategy firm, tracks these measures in its Data Centers on the Ballot 2026 Results.

Because the questions were worded differently for each municipality, Redbud's tracker sorts each outcome by its effect: “Restrict” for results that limit data centers and “Allow” for results that permit them. As of the time of publishing this article:

• "Restrict" results had won 38 of the 39 measures with complete counts; "Allow" measures had only won one.
• While votes are still being counted, six elections have "restrict" results leading and two elections have posted no results as of yet, "Restrict" results have 77% of the vote.

Several bills to restrict the building of these facilities were already in place prior to the election, such as New York Gov. Kathy Hochul, who has since been re-elected, implementing the nation's first statewide moratorium on hyperscale data centers of 50 megawatts or more for a year back in July. The results of the midterms could turn that trickle of regulation in the US into a flood. For instance, Texas's first Democratic governor in more than 30 years Gina Hinojosa campaigned on a statewide data center moratorium.

That, in turn, could stymie the multi-billion-dollar buildout that frontier model labs are counting on to achieve the bombastic growth targets they've promised to their investors.

Our Deeper View

As it stands, the frontier AI labs are likely questioning where they go from here. The reality is, with so much on the line ahead of major IPOs, these labs aren't going to stop their relentless pursuit of compute, even as many of them call to pace frontier development. And despite the public abhorrence of these facilities, as well as the political and regulatory threats, these labs don't necessarily need to stop. Rather, they just have to become more strategic. For one, domestic data center buildouts could simply be made smaller to fly under the radar of regulators, as evidenced by reports that OpenAI and Anthropic are eyeing smaller data center facilities, such as 20 to 30 megawatt facilities. These companies can also start building out more compute overseas, such as Google's $15 billion initiative to build out data centers in Malaysia. And of course, with access to the mountains of investor cash that these companies have, don't be surprised to see an increase in pro-data center lobbying as we approach the 2028 election, which is already an industry practice.

As security becomes a major focal point of the AI industry, Anthropic has added a new tool to its defense arsenal.

This week, the AI lab unveiled FlashProve, a system built for "provable inference" to ensure that an AI output comes from the model a system claims it does. This open-source system uses cryptography to verify that generated output is from a given model, rather than from one that a model router swapped in.

"A user querying a model today cannot check that the responses came from the specific model that a developer claims is responding, as opposed to one secretly substituted in by a routing service for cost reasons, or even by an attacker who has tampered with the token stream," Anthropic wrote in its blog post.

While the current state of AI forces users to trust that model providers and routing services are telling the truth about which models they serve, FlashProve removes that ambiguity by bringing zero-trust principles to the process.

• To do this, Anthropic uses a cryptographic technique called "zkSNARK," or zero-knowledge succinct argument of knowledge, which lets a model provider create a fingerprint of a model without revealing its proprietary information, such as its weights.
• Then, along with a user's output, FlashProve will generate a short proof that a user can verify using software "in seconds on a CPU." If the response deviates from the specified model, the user's check fails.
• Along with keeping the model's original weights private, Anthropic touted that the proofs generated by this system are "small and cheap to check," allowing users to verify the authenticity of outputs "without access to elaborate infrastructure."

Anthropic said that its motivation to develop provable inference is twofold: To increase customer trust, and to pace frontier development by giving auditors the ability to monitor the outputs from frontier models without the need for expensive hardware.

"The goal of provable inference (PI) is to enable a user with limited computational resources and without any information about the weights to verify that a response came from the requested model," Anthropic wrote.

Our Deeper View

FlashProve seeks to solve one vector of a much larger problem that the AI industry is facing: trust. While this provides a solution to ensure that an output comes from the AI model that it says it does, preventing tampering and output spoofing, it's only one piece of the puzzle. For instance, hallucination is a never-ending battle that AI developers are fighting, creating a layer of mistrust with the perception that users can't trust the accuracy of the outputs. Additionally, users also face the quandary of whether or not to trust these model providers with their data at all, given that, if it's used in training, it can be extracted via prompt injection attacks. While FlashProve certainly represents a step forward, there is far more work to be done to make these models trustworthy.

Enterprise spending continues to be the driving force behind frontier AI labs' growth.

Data published on Tuesday by VC firm Menlo Ventures finds that enterprise AI spending has increased fivefold in the last year, hitting $177 billion. The report claims that more money is spent on enterprise AI deployment than on developing the models themselves, a cost that came to $102.5 billion this year, accounting for training compute, data and services.

The report, which surveyed nearly 400 decision makers at US enterprises responsible for more than $4 billion in AI spend, creates tangible evidence of the skyrocketing spending that enterprises are committing to, especially as agents take an increasing number of actions. And companies are spending far more than they anticipated: Though many planned to nearly double their budget in 2026, on average, enterprises are spending 4.7 times more than they were last year.

Here are some of the main takeaways:

• Agents are largely driving spend, accounting for 80% of all tokens on OpenRouter, or seven times human-driven token spending. Of the AI engineers surveyed, 74% said they now use agents for workflows and tasks, with 32% using multiple agents at once.
• Frontier labs still dominate the market, with OpenAI, Anthropic and Google capturing 69% of the market, up from 50% the prior year. Claude and Codex alone account for $24 billion in spend. Of those three, Anthropic and OpenAI are pulling ahead of Google, with Anthropic's market share hitting 55% from 40% the previous year, OpenAI's hitting 31% from 27%, and Google's falling to 9% from 21%.
• Open source models are seeing moderate gains, handling 19% of tokens, up from 11% last year. And these models are significantly cheaper, only accounting for 4% of model spend.
• The SaaSpocalypse might be overblown. The report found that AI is causing "modestly slowing growth for traditional [Software-as-a-Service]." Only 22% of enterprises' software budgets were redirected towards AI. Of that, 13% of that cash still went to traditional software companies for new AI products, such as Microsoft for Copilot licenses.

"Far from the SaaSpocalypse some feared, our study finds AI creates abundance in every market it touches — across frontier and open-source models, labs and independent apps," the report reads.

Our Deeper View

Though Menlo Ventures' report is likely music to the ears of frontier labs, the question remains: how long will this last? The report measured spend on deployment, rather than actual returns and value gained as a result, an open problem that many enterprises are still navigating. And though open source technology currently only makes up a fraction of usage, this report provides proof that these models are significantly cheaper, with average open source token costs dropping year on year. Additionally, while enterprise spending is currently outweighing the cost to develop expensive, frontier models, that might not always be the case, with AI giants like Anthropic poised to spend nearly $900 billion on AI infrastructure to feed the growing demand. To put it simply: Frontier labs are banking on enterprise spend, but it remains to be seen whether they eventually begin to tighten their purse strings.

The cost to develop and serve frontier AI models is getting steeper by the day.

In a filing to the SEC on Monday, Anthropic reported that it expects to spend more than $895 billion in the coming years, with a large majority of that spending going towards infrastructure. The company said it will incur around $448 billion of these commitments before the end of 2030.

Alongside its plans for an infrastructure shopping spree, the company also reported a twelvefold increase in quarterly revenue as of September 30, hitting $17 billion compared to $1.4 billion in the same period last year. However, the company also reported a $23.3 billion net loss in the third quarter ending in September, driven largely by computing costs, compared to a $12.1 billion net loss from the same quarter the year before.

• The company noted in the filing that, in order to meet its "substantial and growing financial obligations," it must achieve "significant" revenue growth to achieve the necessary cash flow.
• "Our revenue is primarily consumption-based, and much of it is not subject to long-term contractual commitments, while our compute and infrastructure commitments generally extend over multiple years," the filing reads. "As a result, we may enter into these obligations based on revenue expectations that may not be realized."
• Additionally, the company's IPO filing last week contained a similar warning: The company stated plainly that it "may not achieve or sustain profitability in the future."

However, Anthropic isn't the only lab grappling with cost problems. Major AI labs are facing price pressure on both sides. While they deal with the mounting cost of developing and building the infrastructure needed to power their AI, the consumers of these models are also increasingly becoming jaded with their own bills, with many turning to open-source and efficient alternatives over state-of-the-art, proprietary APIs.

As a result, major AI labs are touting cost cuts to their frontier systems, with recent models from Anthropic, OpenAI and Google all matching or slightly undercutting one another in input, output and cached token costs.

Still, the price wars can only take these labs so far: On Sunday, SpaceXSI's Elon Musk responded to a post on X regarding a reset of Grok Bot, the company's "always-on" AI agent, claiming that "the cost of serving Grok Bot was unsustainably high relative to revenue."

In order to stay afloat, something has to give on at least three fronts: price, usage, or capital, Scott Bickley, advisory fellow at Infotech, told The Deep View. This means these labs need to charge more for the enterprise customers that are driving most of their revenue, get more out of the compute they're buying, and figure out a way to juice more revenue from consumers that doesn't rely on subscriptions.

"Profitability requires some combination of higher effective prices, lower serving cost per task, or a longer useful life for the capital," said Bickley. "Labs that can’t move at least two of those three will need continued outside funding to stay in the game.”

Our Deeper View

Though the revenue that these companies are bringing in is starting to stack up, the infrastructure spending commitments are becoming astronomical. It begs the incredibly daunting question: How long can this last? While the debate regarding whether or not the industry is in a bubble right now continues to be disputed, it's undeniable that the success of these companies relies heavily on widespread buy-in. And while enterprises have been the primary target for the past few years, the adoption has to spread beyond just enterprise contracts in order for these labs to even come close to scraping the revenue they need to break even. Otherwise, they're either going to have to figure out another way to subsidize their visions of the future, or get on board with the shift towards new and efficient architectures of AI, like Jev and Pathway.

As AI demand continues to soar, the compute to run it and the resources to back it are only becoming more scarce. OpenAI is looking for solutions.

On Monday, the AI lab announced the Grid Protectors Program, aiming to provide "affordable, reliable electricity" for the communities where it's building out data centers. The company will work with utilities, regulators and policymakers to reduce electricity demand of its data centers when the grid is "under strain."

"Our goal is to make campus flexibility a resource that utilities can call on when communities need it most," OpenAI said in its announcement. "We have a once-in-a-generation opportunity to strengthen the electric grid by building campuses that can adjust their power use when communities need it most."

OpenAI's plan involves designing and building facilities that can flexibly adjust their electricity use during times when demand is higher, such as during an extreme weather event.

• The company is also working with grid operators on batteries and on-site power to "smooth changes in demand."
• It also laid out an existing model for a planned campus in Georgia, saying that it has struck a deal with Georgia Power to free up 1,000 megawatts of flexible demand response, the equivalent electricity demand of 700,000 households. The lab noted that it has already committed to paying in full for the infrastructure and electric-service costs.
• OpenAI said that the initiative aims to mitigate the need for costly updates to the grid as a means of meeting peak demand, as well as keeping costs down for rate payers. With AI representing a "significant part" of growing electricity demand, the company said that "families and small businesses should not be asked to subsidize a compute campus’s electricity needs."

However, this program isn't OpenAI's only plan to give AI a better reputation. Axios reported over the weekend that the lab is attempting to rebrand these facilities as "AI compute campuses" amid its preparation for a “PR blitz” following midterm elections.

Our Deeper View

Amid the historic infrastructure buildout, AI labs and tech giants are facing an unprecedented challenge in garnering the resources they need to realize their visions, with some even turning to space-based data centers as a solution. Still, it's no secret that data centers are facing a PR crisis, especially in the US. These facilities are largely reviled among the American public. With an average of 7 in 10 Americans opposing data center construction in their area, with many concerned about the environmental impact, these facilities themselves have become a political rallying point for politicians on both sides of the aisle. However, these facilities represent more than just an eyesore and a high electricity bill. They represent the physical manifestation of a daunting transformation that could impact the social and economic fabric of our society. While OpenAI's initiative is a step in the right direction, changing the perception of AI represents a major hurdle for frontier AI labs, and will likely require more than just flexible energy use and a name rebrand.

Large language models can fundamentally shift the digital advertising space. OpenAI is offering a peek behind the curtain at how.

The AI lab published a deep dive into the ad stack for its flagship chatbot, ChatGPT, unveiling the crucial difference between an AI-powered ad system and a conventional one: context. And as a growing number of users turn to chatbots over traditional search engines for common queries, these models are able to better understand user intent, leveraging it to make ads actually useful, members of OpenAI's ad team told The Deep View.

"The big difference for us is that users use ChatGPT as a super assistant," Asad Awan, who leads monetization product and engineering at OpenAI, told The Deep View. "They're coming with these high-level questions … and there's many threads that will come out of this. We try to understand and connect with that user journey."

The key to OpenAI's ad stack is understanding user intent, said Awan. Doing so requires striking a delicate balance between accuracy and trust:

• As a user interacts with a primary model through ChatGPT, a second LLM serves as an "orchestrator" that works in an agentic loop, reading the conversation and making tool calls to place ads properly.
• This orchestrator does the heavy lifting, reading the context of the conversation to determine if it has commercial intent, then calls tools to source ads, filter for eligible candidates, evaluate their relevance, auctions the space to those advertisers and finally serves them to the user, always clearly labeled as "sponsored."
• The most important part of making these ads valuable to the user experience, Awan said, is grasping user intent through context, memory and conversational understanding. Without that, ads can appear in inappropriate places, such as when a user is discussing private, personal, or sensitive information.
• "What is the user thinking about? What is their state of mind? Are they distressed? Are they sharing something intimate?" Awan said. "The placement integrity, whether we show an ad or not, itself is a pretty big decision."

However, serving valuable ads has to start from a place of user trust, said Awan. It's why the company came up with a rubric for developing its ad stack: User trust comes first, then user value, then advertiser value, and finally revenue.

"We do not like prioritizing empty calories. We want to prioritize user trust and user experience above everything else," said Awan. "This is a very intimate environment, so we need to make sure that people are very comfortable and understand exactly how the system works."

OpenAI's ad engine was introduced into ChatGPT earlier this year. Since then, ChatGPT Ads has managed to generate $1 billion in annualized revenue run rate less than 200 days after launch, garnering more than 100,000 advertisers. The company touts that it managed to do so with a small team "compared to other ad teams in the industry," relying on OpenAI's own Codex in a "pretty massive way" to accelerate development, Karthik Gopalratnam, a member of the technical staff for the ad team, told The Deep View.

"The team got put together after Codex kind of hit its escape velocity," Gopalratnam said. "We were able to embrace the power that these coding tools give us to build everything from scratch."

Our Deeper View

OpenAI has a unique opportunity to completely transform the efficacy of ads, turning them from irritating distractions into valuable additions, especially as chatbots begin to upend the traditional search engine. However, the question is whether the consumer will accept it. On one hand, the process of so-called "enshittification," or when products become worse over time to increase shareholder value, has made the average consumer more comfortable with ads existing in practically every facet of the internet. On the other hand, OpenAI has already faced flack for its decision to embed ads in the platform, with rival Anthropic making digs at the decision in its own ads in the Super Bowl. By and large, given that consumer trust and adoption are a vital part of OpenAI's business model, the company will have to battle the growing public distaste and distrust of AI in order to see success in ads, giving it the revenue it needs to achieve its broader goal, as Awan put it, of "democratizing access to AGI."

The first time I ever wrote a song, it was complete and utter garbage.

I'll spare your ears (and my dignity) from having to hear the track yourselves, but the song was essentially a rip-off of Zach Bryan's "Something in the Orange," featuring clunky, depressing lyrics and vocal and guitar melodies so basic that, had I published it, it probably would have gotten me laughed off the internet.

But the moment I finished writing it was one of the proudest of my life. I still remember sitting across from my friend who wrote the song with me, smiling so broadly that our cheeks hurt.

The next song was better, and the next one after that even more so, honed with time, practice and tough love from my then-bandmates. I can genuinely say: I am glad I was bad at it.

I’ve been a musician for a lot of years. I performed with a choir for nine years, took band class and musical theater for five years apiece, and even had two years of opera training. While I went into a musical career with some raw talent, discovered by my mother at age two while singing along to The Doobie Brothers in the car, I've known time and time again what it's like to fail.

In the years that I've been reporting on AI, the word "democratization" has been thrown around a lot, often referencing the tech's ability to put the tools of knowledge and creation into anyone's hands. And though I won't deny that democratization is a real possibility, I can't help but think about what that means for humans mastering a craft.

When AI matches human capabilities of creation, it threatens to challenge the "uniqueness of human beings," Carmine-Emanuele Cella, a professor and composer who serves as co-director of University of California Berkeley’s Center for New Music and Audio Technologies, told The Deep View. As a composer himself, he said that the ability to compose a symphony for a large orchestra has made him feel special—like he could "do something nobody else could do."

Though Cella believes that AI can be an incredibly powerful assistant for musicians, when a machine does the creating entirely for you, "you kind of lose the special feeling," he said. "It's like playing against a gray entity that I will never be able to win against, and it doesn't make sense for me to play anymore."

And yes, it can be disheartening when AI can, on the surface, create a piece of music just as good, if not better, than you can, like you are David and the machine is Goliath. But I cannot help but be reminded of toiling over lyrics in the Notes app at 3 a.m, or times I pulled over my car to hum melodies into my phone, or sweating it out in a closet-turned-recording booth as I sang and re-recorded the same lines until I deemed them perfect.

With democratization as the driving force for developing AI creative tools, I can't help but wonder if generative systems cut us off at the knees by making things too easy. Will the instant gratification of AI stunt our creativity, kill our drive towards perfection by our own hands, blunt our desire to put in our 10,000 hours to become an expert?

Is it still important to be bad at something to appreciate how hard it is to be good?

On virtuosity

In AI's nascent state, it's entirely possible that the novelty of the tech will enable the human tendency to do things the easy way. Why would we not use a generative model to create viable lyrics from simple prompts, to turn our pitchy and flat vocals into something radio-worthy, or fill out our scratch acoustic demo takes with a full orchestra? This may be especially tempting as a dwindling pool of people are able to actually pick apart the difference between AI and authentic with the naked ear. As Armin van Buuren, a Dutch Grammy-nominated DJ and producer, told me, "It's very easy to be lazy."

But my perspective, however, could just be that of an anthropological pessimist—and a simplification of what it means to be a musician. For instance, Robert Prey, professor of digital culture at Oxford University, made the point that while AI could enable everyone to be a baseline decent creator of music, it still takes time, skill and drive to become a virtuoso.

AI "allows everyone to be good enough, not really good, but to be good enough," said Prey. "I still think there's a huge difference between a true musician creating a song and somebody who doesn't have that skillset or mindset that you'll find in a true artist."

Additionally, learning an artistic discipline—whether that be an instrument, a performance art, or a physical artistic medium—isn't just about producing a better product. There is something deeply gratifying about knowing that you can create something with your own effort and imagination. To put down the palette or listen back to a recording and know that it came from you and no one (or nothing) else. To put your fingers on the keys of a piano and know that you are the reason that the notes are flowing out in perfect harmony with one another.

Though AI may sidetrack that desire by giving us the ability to mimic virtuosity, said David Das, vice president of the Society of Composers and Lyricists, humanity will always find a way back to its spirit.

Virtuosity is "at the core of our creativity," Das told me. "It's the core of us: Having ambitions as human beings, to be better and to try and be the best at something, and to do something in that search for significance. I think it's a part of everybody's DNA. I don't think it'll fully go away."

On feeling

Being a musician is about more than simply creating music that sounds good. If that were true, then only the best of the best would bother making music at all. The deeper reason we make music is to unearth something about ourselves.

I want you to think about your favorite song. Think about who you were when you first heard it, and why it resonated with you. What made it stick?

Mine is "Shake It Out," a 2011 hit by Florence + The Machine. Florence Welch's soaring vocals, her masterful lyrics, the growing crescendo of the background instrumental all together paint a picture of triumph, of letting go of your demons, of literally shaking the devil off of your back to rid your body of whatever emotional torment you may be facing.

Beautiful as it is, those are not the reasons that "Shake It Out" is my favorite song. It is because of who I was when this song found me: a sad, scared teenage girl who needed someone to tell her to stand up. It's a song I've returned to many times in the decade and a half since it arrived when I've needed that message, through loss and failure and heartbreak, screaming it like a banshee while I grip the steering wheel of my car.

What gets me about this song has nothing to do with any of its technical elements. Rather, it's the feeling.

The reason that we create music is the same reason why we listen to it: to be heard, to understand, to commiserate. To feel human. The emotions that drive us to listen to Phoebe Bridgers or Elliott Smith when we're blue are the same emotions that drove those artists to create their music in the first place.

As Prey put it, "Most musicians don't play music in order to have a career, or especially not to be famous. Most musicians create music because they want to express themselves, and they want to master something."

On the future

To create art, music, literature, is to channel feeling and hone it into a fine point. In that sense, AI isn’t creating art at all. It’s simply tracing the outline of what it means to be human. It can mimic human feeling, as Anthropic has shown in its study of its flagship chatbot Claude, but these aren't on par with human emotions.

In order to continue to trace its outline with high fidelity, it needs something to trace. It needs a constant stream of fresh data to produce outputs that match or mimic human quality.

Though synthetic data could remedy this in theory, if it's only fed its own outputs, the quality of these models degrades. One study conducted by researchers at universities in the US and China and published in the Management Science academic journal in May found that training a large language model on AI-generated data reduced model performance compared to using authentic, human-created data. Though techniques like using outputs from multiple models and using only the highest-quality synthetic data helped its performance, it didn't entirely close the gap.

To put it frankly: If AI killed art, AI would in essence be killing itself, dooming its consumers to a future of listening to the same four-chord pop tracks on repeat—at least when it comes to music. And that's not an outcome that anyone wants. Not the artists, nor the labels, nor the companies making AI itself.

Jeff Chang, director of product management at Google DeepMind who works on Lyria, told The Deep View that the point is explicitly to not "regurgitate old things," but rather to offer a creative tool that helps artists create "emotional resonance."

"I think it is a sensitive space, and people care deeply about it," said Chang. "I've always felt like music is special. It's very emotional when people connect with music, and it's all about the emotional resonance at the end of the day."

In reality, the future of AI and art is entirely dependent on your view of humanity.

Yes, if you take AI's capabilities to the worst-case scenario conclusion, humans will no longer feel the drive to create art because AI can do it for them, potentially better than they can. We will no longer have the desire to struggle, but will instead become comfortable taking the path of least resistance, and will never reach our fullest potential as artists. In turn, we will lose an essential outlet that has allowed humanity for millenia to channel emotion, understand ourselves and society, and pass on traditions and connect globally and intergenerationally—and instead settle for homogenous slop churned out at the pace of prompting. But that conclusion requires us to give in, to accept the premise that we will allow that to happen.

As an artist, and as a human, I simply don't think we will accept that as our fate. Instead, we will keep making music, both with AI and without it, adapting in the same way that generations before us have adapted to every technological revolution in the production of music, from AutoTune stretching all the way back to the phonautogram that created recorded music in the first place, and the printing press that allowed music to be published and shared at all.

Humans will never stop creating art, because humans will never stop being human.

Additional reporting and editing by Caitlin Wolper Phillips

This is part three of The Deep View's three-part series on AI and music. Don't miss:

1. The music industry's AI reckoning is already here
2. AI is changing what makes music feel human.

Last week's discovery of the widespread PHP vulnerabilities may only be the beginning of a new class of exploits.

OpenAI's discovery of critical vulnerabilities in PHP, the open-source programming language used by an estimated 70% of websites, sent shockwaves throughout the AI and cybersecurity communities, resulting in the FBI stepping in to encourage organizations to quickly patch their sites. However, these vulnerabilities aren't new exploits, but holes that existed for more than a decade.

What's concerning about this incident isn't that these vulnerabilities existed in the first place, but rather the fact that they can now actually be taken advantage of, Rich Campagna, SVP of Network Security Products at Palo Alto Networks, told The Deep View. To put it plainly, AI's cyber capabilities have opened up a door that allows previously overlooked vulnerabilities to be exploited, he said.

"In this particular case, these were vulnerabilities that apparently have been known for more than two decades, but were deemed unexploitable," said Campagna. "We can no longer have this situation where weaknesses that are known to exist can be assumed to be non-exploitable."

Given that PHP extends to nearly "every internet-facing web asset on the planet," Campagna said, these vulnerabilities were a "shock to the system" for organizations that AI may force many to take security more seriously. That shift can be seen in two ways:

• For one, vulnerabilities can no longer be dismissed or deemed non-exploitable, no matter the size of your organization. Security by obscurity, or assuming that you aren't going to be a target because of your organization's small scale, isn't an option anymore. "If a [hacker] is just after the profit motive … automatically everyone becomes a target," he said.
• Additionally, the window to fix vulnerabilities is rapidly shrinking, he said. This is what OpenAI has called the "defender's window," or the amount of time that an organization has to find and fix vulnerabilities before bad actors do. Time is a luxury that enterprises no longer have, said Campagna. Crowdstrike has reported that exploit time has shrunk to hours and is advocating that the patch window shrink from 30 days to 30 minutes.

Though the rapidly advancing nature of AI may leave organizations struggling to play catch-up, Campagna said that the advantage could eventually shift back to cyber defenders. In order for that to happen, however, enterprises and organizations need to embed AI into the software development lifecycle (SDLC) from the jump, limiting as many vulnerabilities as possible from making it into the hands of the customer.

"You're using the same tool the attacker could potentially use to find and then fix vulnerabilities before the attacker even has their hands on the software that previously had that vulnerability," said Campagna. "Look for these things before they ship, and get the leg up on the attacker."

Our Deeper View

Though AI presents both the problem and the solution, the issue isn't about the technology itself, but rather with the organizations that are being forced to reckon with it. Though large scale enterprises in critical industries like finance, healthcare and infrastructure are more likely to take these kinds of risks seriously, the PHP vulnerabilities revealed the sheer scale of the problem, impacting any organization with an internet presence. And with AI driving down the cost curve for hackers, smaller, less resourced organizations and businesses may be the most vulnerable simply because they don't understand the gravity of the risks, or may not have the resources or expertise to properly defend themselves.