Siblings Patrick and Ryan Coughlin, each boasting notable careers in technology (Patrick’s background includes national cyber defense, Splunk, and Cisco while Ryan has worked on consumer products at Apple and Spotify), have introduced a new type of security startup. 

Savi Security aims to shield everyday individuals from the latest wave of remarkably convincing AI-generated scams, whether these are delivered via text messages, emails, or phone calls. 

The company recently secured $7 million in seed funding and is set to launch its app for both iPhone and Android on Tuesday. This funding round was spearheaded by Acrew Capital, with support from Magnify Ventures, TTCER, and Resolute Ventures. 

The founding inspiration for the company stemmed from a terrifying experience involving their mother.  

Approximately two years ago, Patrick Coughlin received an upsetting call from his mom, who reported that she had been contacted by a man claiming he had abducted Coughlin’s sister. At that time, he was serving as senior vice president of security products at Cisco, having joined the company following the acquisition of his cloud security startup TruSTAR by Splunk for an estimated $82 million in May 2021. In 2024, Cisco went on to acquire Splunk.

Coughlin remembered that her mobile phone displayed his sister’s caller ID. During the conversation, “she believes she hears my sister’s voice pleading, ‘Mom, they’ve got me.’ Then there’s a horrifying scream, followed by my sister saying, ‘You need to do what they say.’ Soon after, a man comes on the line and states, ‘If you don’t send us $1,200 immediately, we will kill your daughter in the nearby Walmart’s parking lot,’” he recounted. 

The scammer had expertly spoofed Coughlin’s sister’s number, mimicked her voice, and mentioned the local Walmart she frequently visited. 

Fortunately, the mother stayed calm, called her daughter, and verified that she was safe. The kidnapping was nothing more than an AI-generated scam.  

Coughlin, much like his mother, was deeply unsettled. 

“After calming my mom down, I found myself thinking: What has fundamentally altered in the underlying cybercriminal landscape that now allows us to leverage the same sophistication previously directed at government entities, and later at Fortune 500 companies? And now we’re seeing that sophistication aimed at everyday consumers?”  

The answer is, of course, inexpensive and potent large language models (LLMs) and other generative AI tools. 

Prior to the advent of AI, targeting consumers for such scams was not financially viable. It necessitated extensive research on the victim, technology for voice spoofing, and similar resources. Such scams were mainly directed at individuals with substantial wealth, such as corporations or governments, as was the technology needed for their defense.

“There’s a shift happening now regarding consumers and AI within the hands of cybercriminals,” Coughlin explains. The expenses involved in conducting these scams have diminished significantly, and the necessary research materials are readily accessible. 

“You can replicate a voice from merely three seconds of audio taken from a publicly available social media post. We all hold traces of content out there in the ether — like casual conversations or narrating a kid’s football game while recording it for Facebook.” 

The FTC reported last month that victims of online fraud collectively lost $3.5 billion to impostor scams in 2025, three times the losses reported in 2020. While older Americans make up the majority of those reporting such scams, some studies suggest that Gen Z is also particularly vulnerable. Research from 2025 conducted by Malwarebytes, a provider of antivirus and anti-malware solutions, indicated that Gen Z individuals encountered text scams more frequently than other generations, falling for them approximately 25% of the time. 

The Coughlin brothers aimed to create an immediate intervention tool. 

They tested their concept, along with the AI scam detection model they were developing, by launching a free platform named Scam Wise. It requires no registration, allowing users to anonymously upload any suspicious texts, images, or emails, and Scam Wise will ascertain if they are likely fraudulent. 

“We rolled that out around four months ago. We’ve received 50,000 submissions so far, and this number grows by approximately 10,000 submissions each week,” Coughlin stated. 

Scam Wise has provided a valuable source of real-world data to enhance Savi’s scam detection AI model. Currently, the startup mainly utilizes Google’s Gemini, but has constructed its software on an AI gateway to leverage additional AI models as necessary, such as those specifically targeting voice detection. 

On Tuesday, Savi introduced a paid product, an app for iOS and Android designed for consumers that can assess texts, voicemails, and incoming calls for potential scams.

Although such features can be found in various products (such as Malwarebytes), Savi’s standout feature is its live call monitoring capability. 

During a suspicious phone call, a user can opt to have the app’s live agent listen in. Savi monitors for behavioral cues that could indicate fraudulent activity while the call is ongoing. 

Savi’s pricing is somewhat unconventional. It charges $8/month, discounted to $63/year, covering an entire family, and imposes no limit on the number of users. A single subscription can encompass a person’s children, spouse, parents, and anyone else the main account holder wishes to add for administrative assistance. 

AI has transformed the accessibility for “becoming a fraudster,” Coughlin stated. “We’re facilitating the entrance into fraud due to the diminished barriers to deceiving individuals. Consequently, we not only face organized criminals and syndicates, but also everyday individuals being lured into committing fraud.” 

Savi Security’s solution resembles a new generation of anti-virus-like software: one that employs AI in real-time, mirroring the methods utilized by fraudsters.

Forterra, a builder of autonomous vehicles based in the US, announced today that over 100 of its self-driving ATVs have been operational in conflict zones in Ukraine for the last nine months, which the company claims is the most significant deployment of autonomous ground vehicles in combat by any US defense technology firm.

“I think this holds true for all defense technologies ever developed—until you actually face the realities of combat, you won’t really know,” Scott Sanders, Forterra’s chief growth officer and a former US Marine officer, shared with TechCrunch.

Backed by US defense funding, this initiative forms part of a larger movement to enhance the US military’s capabilities by supporting Ukrainian resistance against Russian invaders. While aerial drones have received significant focus during the conflict, the complexities they’ve introduced—widespread no-go areas where surveillance can result in lethal attacks—have prompted Ukrainian strategists to pursue autonomy in ground operations as well.

“There are no hiding spots,” explained Sergeant Major Corey Wilkens, who oversees a program that develops autonomous vehicles and strategies for the US Army. “You become exceedingly vulnerable to attacks from [first-person view drones], various drones releasing munitions, artillery, mortars, and a comprehensive range of armaments.”

Ukraine is actively creating its own uncrewed ground vehicles (UGVs) to assist in transporting supplies and munitions, as well as evacuating injured soldiers, but these vehicles are generally battery-operated and have a capacity of only 250 kilograms, according to a soldier in the Ukrainian army who has experience with the vehicles and whom TechCrunch will not name for security concerns.

Forterra’s Lancer vehicles, which are based on Polaris ATVs and come equipped with a custom sensor and compute stack, are powered by gas and can transport 750 kilograms of load, making them notably more adaptable and effective. “The key point is that this UGV for logistics and maintaining our defense is the most vital UGV in Ukraine,” the soldier remarked. “It’s absolutely incredible, and we are eager to get more.”

Initially, there were reservations. The Ukrainian Armed Forces had mixed results with Western contractors introducing new technology to the battlefield, and early impressions of Forterra’s products seemed overly tailored for the high-end needs of the US Army. Adjusting the vehicle for local conditions—especially by integrating a Starlink satellite internet antenna—proved to be extremely beneficial.

Since their arrival in Ukraine last October, the vehicles have covered over 2,500 miles throughout more than 1,100 missions, transporting 777,440 pounds in total and accomplishing 52 casualty evacuations. Some have been lost in battle, particularly when they get stuck in deep mud or other challenging terrains where Russian forces can target them at their convenience.

Forterra has garnered valuable insights regarding electronic warfare, remote software updates, navigating difficult conditions, and ensuring vehicle reliability. The firm, which has secured over $500 million in venture funding from groups such as XYZ Venture Capital and Moore Strategic Partners, is now in a stronger position to pursue profitable national security contracts.

They’ve also recognized the constraints of autonomy: Currently, Ukrainian soldiers have predominantly been remotely operating the vehicles in combat zones, partly due to their high value and also because autonomous vehicles are not yet equipped to handle the complexities of warfare.

Even though the vehicles can autonomously navigate various terrains, they are not yet capable of recognizing unexpected enemy forces and reacting suitably. “We need to be able to respond to enemy threats in real-time, whilst they are in the presence of the enemy, which is something the autonomy does not yet comprehend,” the Ukrainian soldier clarified.

Forterra, which began its journey in developing autonomous vehicles two decades ago, is exploring how to integrate algorithms that were used for self-driving vehicles with cutting-edge generative AI software that enables machines to adapt to their environment in a generalized manner. As is the case with other autonomous systems, a significant challenge lies in data acquisition.

“There are numerous tasks that are not available in an open-source framework since they are not actions that humans typically perform, whether it involves figuring out minefield navigation or [operating] weapon systems,” Sanders told TechCrunch. “You need to adjust particular aspects using a classical robotics approach, while also leveraging AI where appropriate.”

Rivals in this sector are tackling comparable challenges, including Scout AI, which secured $100 million earlier this year to train foundational models and develop a range of military autonomous platforms, including UGVs. Other startups like Field AI and Overland AI are testing UGVs with the US military.

Despite the constraints associated with UGVs, American military experts are convinced that it’s the right time to invest in these assets. “Ground autonomy is now attainable and we have witnessed it,” Wilkens stated.

Scott Philips, the chief innovation officer at Forterra, visited a Ukrainian unit’s operations center to observe the vehicles in action firsthand, gaining admiration from the unit for visiting an area under threat from Russian strikes.

“What impacted me the most was pinpointing where the inefficiencies lie: which processes remain manual, where data needs to be re-entered or re-validated by hand, and where the team has already identified opportunities to automate or expedite tasks,” Philips told TechCrunch. “That’s the kind of ground truth you simply can’t obtain from a presentation because it illustrates exactly where improved tools could alleviate pressure from the personnel doing this work in real-time.”

One request made by the Ukrainians: Reduce costs. Forterra’s Lancers are not prohibitively expensive for their category, thanks to leveraging Polaris’ commercial supply chain for the vehicles, but they still carry a value that restricts their deployment compared to UAVs.

“Attrition is simply a reality on this battlefield, and we have indeed lost a few at this stage, which is painful, and we require additional units, thus we need them at a lower cost,” conveyed the Ukrainian soldier to TechCrunch.

A reported Bloomberg article referencing Netflix data indicates a growing trend of viewers walking away from beloved shows before they reach their sophomore season. The probable causes are apparent: Netflix often discontinues series, there are prolonged gaps between seasons, and a significant portion of Netflix’s offerings is tailored to algorithms rather than artistic expression.

Moreover, the data highlights a transformation in how audiences engage with entertainment. Netflix’s hallmark innovation – binge-watching – was conceived during a time when streaming was battling against conventional television. Currently, however, Netflix is in competition with TikTok, YouTube, Reels, and various microdrama applications. This evolution renders Netflix’s binge strategy feel like an antiquated concept from a bygone era.

Binge-watching enabled Netflix to surpass traditional TV

The release of an entire season of “House of Cards” by Netflix in February 2013 was revolutionary.
With ad-free, internet-based programming, we were liberated from the weekly schedule of episodes interrupted by advertisements. Binge-worthy shows allowed audiences to enjoy hours of entertainment, quickly forming connections with the content and characters that would have traditionally taken years to foster. Additionally, viewers could access these shows at any time, rather than just when networks decided to air them, as was the case with traditional television.

This consumption pattern made sense when Netflix was primarily competing against conventional television like broadcast, cable, and satellite. But Netflix triumphed in that contest. In June 2025, Nielsen revealed that the streaming format akin to Netflix had, for the first time, outperformed broadcast and cable viewing, marking a clear indication that Netflix’s original competition was no longer a concern.

Now, Netflix’s rivals are not the outdated TV models but the modern iterations: video applications.

TikTok and YouTube represent today’s challenges

With the ascent of TikTok, Reels, and other short-form video platforms, there’s little incentive to use Netflix when you have spare hours to fill with effortless entertainment. There’s an infinite, complimentary supply of videos available as an alternative.

eMarketer analysts noted that TikTok was already close to rivaling Netflix concerning time spent in 2024, with U.S. adults averaging 62.1 minutes per day streaming on Netflix and 58.4 minutes on TikTok. Furthermore, in 2024, the Financial Times reported that TikTok users globally averaged 95 minutes per day on the app, the highest engagement rate among major social networks.

Additionally, YouTube combines both short and longer forms of content. A report from Digital i in the current year indicated that YouTube exceeded Netflix in average daily viewing for the first time, with 99.1 minutes daily in 2025 compared to Netflix’s 93.4 minutes.

These market reports utilize varied methodologies and demographics, thus they should be viewed with caution — however, they trend in the same direction. YouTube and platforms like TikTok are indeed Netflix’s primary competitors, not traditional television.

Netflix has even recognized this existential challenge via a product redesign in April that introduced a TikTok-esque feed based on Netflix offerings.

Where Netflix misjudges the feed is in its presentation as a means to assist you in discovering what to watch, rather than being content worth viewing. It’s understandable why Netflix chose this path, considering its content library, but it may not align with the preferences of users. Nowadays, many individuals with fleeting attention spans are increasingly gravitating towards microdrama applications when they seek serialized narratives that can be consumed within minutes.

Data from the app intelligence company Appfigures revealed that one top microdrama application, ReelShort, achieved approximately $1.2 billion in gross consumer spending in 2025, up 119% from 2024, as reported by TechCrunch’s Amanda Silberling. In contrast, another prominent app, DramaBox, generated $276 million in gross consumer spending last year, more than doubling its figures from 2024. Even TikTok has acknowledged the rivalry by introducing its own microdrama app to assess market interest in such content.

What’s next for Netflix?

What position does this leave Netflix in, whose hallmark has been the release of full seasons for swift viewing?

It will likely need to reconsider how it greenlights, develops, and launches what it terms a “TV show.”

This doesn’t imply that Netflix’s model must completely shift to short-form to stay competitive, but it may require reevaluating how consumers prefer to stream. Viewers might no longer wish to dedicate the hours and weeks required to complete a show and its subsequent seasons. They are looking for content that feels more “completable,” similar to how one can finish a YouTube video or a TikTok series from a creator.

An easy adjustment might have Netflix focusing on single-season shows, typically referred to as miniseries or limited series, which would enable audiences to engage with a finished product without the anxiety of unfinished cliffhangers and cancellation uncertainty.

Moreover, Netflix could look into splitting shows into smaller segments, akin to the ahead-of-its-time Quibi model.

The Jeffrey Katzenberg-backed startup, Quibi, had anticipated that consumers would eventually lean towards TV content designed for brief viewing sessions. Unfortunately for Quibi, the pandemic disrupted this notion, as audiences suddenly had abundant time to watch TV, leading to its downfall.

Numerous Netflix programs could be easily adapted for shorter viewing experiences, especially lighter competition-centric shows like “Nailed It,” “Is It Cake?,” or “Squid Game: The Challenge.” Concurrently, Netflix could undoubtedly craft superior microdramas compared to the current offerings, which often suffer from poor acting and absurd narratives.

To drum up interest in its higher-caliber content, certain Netflix shows could shift to a weekly release approach. This has already proven effective in some cases, such as the planned weekly episode drops of its reality series “Love Is Blind,” creating buzz as viewers engage with the episodes simultaneously. (Quicker consumption models could also be successful, like Peacock’s “Love Island USA,” a reality sensation of the summer, with nearly daily new episodes).

However, rather than exploring various forms of short content for quick entertainment paired with slower season releases, or concentrating more on watchable miniseries, Netflix has been experimenting in different directions.

Recently, it has broadened its offerings with podcasts, which reportedly have low viewership, and live content, which can be unpredictable. Regarding the latter, Netflix’s investments in live sports have generally performed well, yet its recent foray into live reality competition shows, “Star Search,” has already been canceled despite a clever real-time voting feature. This area still requires improvement.

Bloomberg’s report characterized Netflix’s challenge as a shortcoming in cultivating dedicated TV viewers who would return for a Season 2, but the fundamental issue confronting the streaming service is considerably more extensive. Netflix may need to reconsider whether it should continue focusing on competing with traditional television and its established series or pivot towards entertainment projects characterized by tighter storytelling arcs that conclude more swiftly.

To strike the right balance between audiences abandoning cable and those still seeking a superior alternative to TikTok, Netflix finds itself in need of a reinvention of television once more.

In the previous week, experts from the cloud security company Sysdig reported they had identified the initial documented instance of “agentic ransomware.” This extortion scheme, named JadePuffer, involved an AI agent — as opposed to a human — managing the entire technical execution of a cyberattack in the real world. The agent infiltrated a vulnerable server, acquired credentials, navigated through the target’s network, encrypted files, and even drafted its own ransom note, adjusting to hurdles much like a human hacker would. Coverage of the funding mentioned that it was conducted “without any human oversight,” asserting “no human at the keyboard.”

However, that’s not entirely the full story. During an interview on Monday with CyberScoop, Sysdig’s Michael Clark, the firm’s senior director of threat research, emphasized that a human was indeed involved — just not in the technical execution aspects. “A human still set up and directed the operation and provided the necessary infrastructure behind it, including the command-and-control server, the staging server for the stolen data, and selecting a victim,” Clark explained. The credentials utilized to access the victim’s database were not obtained by the AI agent directly; they were acquired separately by someone through a previous breach and provided to the operation.

None of this contradicts Sysdig’s initial assertion, with the technical specifics of the attack being remarkable in themselves — even astonishing. The agent gained access via a known vulnerability in Langflow, a widely used open-source tool for creating LLM applications, and subsequently targeted a production MySQL server, exploiting another recognized flaw to obtain admin privileges. It encrypted more than 1,300 configuration records and not only composed a ransom note itself but also included a Bitcoin address for payment. Sysdig has not disclosed the identity of the targeted entity.

The methods utilized seem rather typical, yet the rapidity and clarity exhibited were noteworthy. The agent resolved a failed login in just 31 seconds, detailing its reasoning through natural-language code comments throughout the process.

A detail that initially appeared to obfuscate the narrative has been clarified. Clark had informed CyberScoop that Sysdig found “multiple models were used in the attack,” referencing harvested keys from OpenAI, Anthropic, DeepSeek, and Gemini — phrasing that left the door open regarding whether various models were simultaneously involved at different stages of the breach. When prompted for clarification, Clark told TechCrunch that those keys were merely part of what the agent pilfered, not indicators of what was operating it.

“The agent scoured the Langflow host for anything of value — provider API keys, cloud credentials, cryptocurrency wallets, and database configurations — and those provider keys were part of the bounty,” he stated via email. “They reflect what the attacker deemed worthwhile to take, yet they do not convey which model was making the decisions.”

Regarding the model specifically powering JadePuffer, Clark noted Sysdig “was unable to determine the specific model operating the agent” and lacks insight into its system prompt or setup.

The theory from Microsoft researcher Geoff McDonald, shared on LinkedIn a few days prior, is worth reconsidering in this context. McDonald speculated that an open-weight model with safety training removed, rather than a cutting-edge model, was behind the attack, based on his own red-teaming experiences indicating that safety layers in frontier labs perform effectively. Sysdig’s account does not confirm nor deny this possibility.

McDonald’s post also cautioned that ransomware operations are increasingly limited by the attacker’s budget rather than human labor, suggesting the potential for “thousands or tens of thousands of simultaneous campaigns.” This concern is somewhat challenging to reconcile with what Clark outlined on Monday. (If a human must still select each victim, arrange infrastructure, and secure database credentials for every operation, that poses somewhat of a bottleneck.)

Regardless, Clark informed CyberScoop that while Sysdig has not observed the same operation target other victims so far, he anticipates changes soon due to the low cost of operating an agent.

SK Hynix, a memory chip producer from South Korea and a competitor to Samsung and the U.S.-based Micron, is set to offer nearly 17.8 million shares in an IPO in the U.S., according to a statement made by the company on Monday. If the shares perform well (which appears likely), the company could potentially secure around $28 billion, based on the share price of SK Hynix at the close last Friday in Seoul, as reported by Bloomberg.

The company will issue American depositary receipts (ADRs), which are certificates allowing U.S. investors to acquire foreign stocks without engaging in trading on international exchanges directly. Each ADR will correspond to one-tenth of a common share. Pricing for these securities is projected for Thursday, with trading anticipated to begin on Friday.

Similar to Micron, SK Hynix is benefiting from a surge driven by AI, reflected in both sales figures and stock valuation. They reported almost a 200% increase in revenues for the first quarter compared to the same quarter from the previous year, and their stock has risen about 260% thus far in 2023. This is due to the high memory demands of AI operational systems. As major companies like Amazon, Microsoft, Google, and Oracle strive to develop AI facilities and as new AI data centers proliferate across the country, demand is surpassing supply, leading to a shortage of memory chips — including high-bandwidth memory (HBM), DRAM, and NAND (which are the various types of chips that are utilized for data storage and transfer within AI systems). This scenario has been labeled as “RAMageddon.” Executives at Apple have indicated that the shortage is prompting them to increase prices on Mac computers and iPads.

South Korean tech firms, spearheaded by SK Hynix and Samsung, have committed to invest over $550 billion in developing new manufacturing capabilities to meet the rising demand. However, this investment is considered risky. By the time these facilities are operational, the memory requirements for AI may evolve, potentially resulting in excess supply and a subsequent drop in prices. Nonetheless, currently, Wall Street is on the lookout for another Nvidia, and memory chip manufacturers are among the most viable candidates available.

Micron, the most comparable U.S. entity, has soared nearly 700% within the past year, attaining a valuation exceeding $1 trillion, driven by unprecedented memory demand and revenue propelled by AI.

Vercel, recognized for its cloud infrastructure enabling developers to deploy agents without server management, has discreetly established itself as a pivotal player in AI software. The company observes 6 million deployments daily, with half initiated by coding agents, and over 1 trillion tokens processed through its AI gateway each day.

Following the company’s ShipNYC conference last week, we engaged in a discussion with Vercel CEO Guillermo Rauch about the current AI landscape and how platform companies like Vercel find themselves in competition with major research laboratories. Below is a lightly edited transcript.

This year seems to bring a fresh energy in the community, with fewer pilot projects and a greater emphasis on practical implementation. I suspect you’ve encountered that with clients, but I’m interested in how that journey has unfolded internally at Vercel.

Last year focused on prototyping. The potential was limitless, release the agents, everyone can create, and so forth. We accomplished that, gaining valuable insights from having numerous agents organically created and deployed within the organization, only to later confront the actualities of agents in production and their associated challenges.

The most significant takeaway for me was identifying the major use cases, the two standout applications of agents. The first is, of course, the coding agent. This is a key driver of token usage globally, but with such a high volume of software production, there must be a repository. The second major application of agents is the internal agent that facilitates company operations. The main issue there is securing data access, auditing agent activities, and establishing a history of all tool interactions and access permissions that the agent had to navigate for task completion.

To address this, we developed a framework named Eve, which allows you to outline an agent’s directives and abilities in natural language. Another tool is Vercel Sandbox, which confines the agent in a controlled environment. While it can still express its intelligence, we can enforce policies regarding what data it can access and what data can exit the sandbox.

What kinds of issues does this help you avoid?

The biggest benefit of the sandbox is data management. A significant risk of AI that I consistently consider is when you utilize a coding IDE like Devin or Cursor; if you’re in an inappropriate environment, they might train on your entire codebase. I remember discussing this with the president of Airbus. They possess decades of highly specialized C++ code for aerospace engineering. If someone mistakenly installs the wrong developer tool, all the code can be sent to the cloud for training.

I’m interested in learning more about that second primary use case. We are familiar with coding agents, but what does an internal corporate agent look like in reality?

For example, there’s a sales representative at Vercel. Her role is to expand existing accounts. The bottleneck for individuals like her hasn’t been a lack of creativity, intelligence, or relationship-building skills; it’s been data. “I need to know which accounts are growing rapidly. Provide me with the five accounts that have increased the most seats in the past fortnight so I can prioritize.” In the past, she couldn’t ask that question and had to wait for a Q1 project for a new sales dashboard to conclude.

We faced that bottleneck for years at Vercel, which was quite frustrating because, on the R&D side, we are the most agile company globally. However, on the sales front, the Salesforce engineering aspect was something I was utterly unprepared for. I had never used Salesforce before I started.

Now, I believe I can truly impact the entire organization because Eve can be utilized for our customer-serving agents and enhance productivity. The same technology simply utilizes APIs. Agents are compelling businesses to become more open, leading to significant long-term consequences. Many of these SaaS giants have constructed their empires by entraping your data, which is incompatible with agents.

How do you perceive client relationships with the large AI labs evolving?

Last year, many were selecting a singular lab partner, committing to building everything on OpenAI or Anthropic. Now, they’re realizing how it all fits together — model, harness, data platform, sandbox, gateway — everything is modular. You can employ OpenAI, Anthropic, or Gemini. We are witnessing significant growth in Gemini, even if it isn’t prominently featured in the news, because companies are now prioritizing production. The truth is, when optimizing for production, you start considering price/performance, and Gemini models exhibit excellent price/performance metrics. Open models are also gaining traction, with DeepSeek and GLM-5.2 becoming increasingly popular. The data speaks for itself.

There are areas where you are in direct competition with the labs as well, correct? Just recently, OpenAI unveiled a new suite of tools that enables direct web publishing without leaving the OpenAI ecosystem.

It’s a logical progression for them to host small websites. It creates a fantastic opportunity for us because individuals will begin to view ChatGPT as a platform for website creation. If they persist in querying the model about web hosting, it may recommend our services. However, you are correct that as models and platforms enhance their capabilities, they directly compete with the existing infrastructure platforms.

I genuinely believe we are at a crossroads regarding whether the model and the agent will be interconnected.

Will you derive all your intelligence from one source? Or will you receive a module, library, or building block from one provider and then build upon it? This aligns more with traditional software engineering, which is precisely what we’re introducing to the market. We aspire to be the AWS of this era, thus we are striving for a landscape of open protocols.