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Billy Hurley

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Brianna Monsanto

In a few years, consistent software pricing might join the list of things technology leaders reminisce about as companies deploy new pricing strategies to keep up in the AI era—strategies that might lead those prices to swing wildly depending on a variety of factors.

, more than half (52%) of 501 surveyed product leaders said they plan to roll out new 

What are the biggest changes tech leaders can expect to see from 

 as AI features become the new norm? For starters, a dip in pure subscription-based pricing and a rise in pricing models that blend subscription and usage-based charges. While 42% of surveyed leaders claim to offer pure subscription-based pricing, which was the most popular pricing model for AI offerings, only 37% expect to do so going forward. Revenera found the subscription and usage-based hybrid pricing structure will see a five percentage-point uptick in the future.

 Nicole Segerer, SVP and general manager at Revenera, told IT Brew it’s no surprise that pure subscription-based models will take a blow, partly because AI’s increased capabilities may lead to less users, eventually threatening the traditional per-user subscription model.

“The second reason is that, with AI solutions coming in, the traditional flat-fee subscription where someone pays $10 or $100,000 or whatever it is per year, is also coming under threat, just because there will be a bigger correlation between what customers do and how they use the software, and what the cost of that is,” Segerer said. “And pricing will have to adjust.”

 Segerer said traditional and blended-pricing models aren’t the only strategies vendors will lean on as they bulk their software up with AI-enabled features.

“In the future, there might be a little bit more flexibility as to providing people with access to what they really need, versus trying to sell them the whole thing when it’s not needed,” Segerer said, describing an à la carte pricing model.

Segerer said the software industry may also rely more on an outcome-based pricing strategy, where costs are tied to a specific result.

“I’ll give you an example: So, when I send an MRI scanner, a usage-based model would be, you pay for every scan. Every time the thing switches on and runs, you pay for the scan,” Segerer said. “An outcome-based model would be, I only pay when the scan is successful, as in, the picture quality is good.”

Andy Sen, a CTO and CIO at subscription commerce platform company AppDirect, told IT Brew he has already seen vendors switch to outcome-based pricing. He hopes to see more companies follow suit, along with a flexible pricing structure down the line.

“What I would like to see is vendors…really being clear on how much of their product their customers are using and price based on that,” Sen said. “I think that will help everyone.”

 While pure subscription pricing may lose some companies in the long run, Segerer said subscription-based pricing isn’t going anywhere because it offers recurring revenue and predictability.

“Predictability is super important for both the vendors of software, but then also, even more importantly, for the buyer, because they want to know what they pay,” Segerer said.

Software pricing models expected to get a revamp for the AI era

Revenera SVP and general manager says outcome and à la carte-styled pricing models are in the cards for the future.

Caroline Nihill

With talk of agentic AI and digital twins gripping the industry, it can sometimes feel like a company not already automating workflows and implementing “AI teammates” is living in the past.

But it’s still early days, and companies are figuring out the best use of these latest innovations. For some, it’s simple: What if executives were available to 

 and talk with employees without needing to actually participate in a conversation? Or even better, what if everyone in the company had a digital twin that was just like them, only trapped inside the cloud?

To get a sense of how far this AI teammate tech has actually progressed, I set up a demo to see how they worked. I was also able to talk to an AI persona I never expected: one of myself.

Personal AI, a company that builds out AI teammates, envisions a time when every person has their own personal AI. Specifically, it is aiming to create digital twins that can act as a knowledge base, answering specialized questions for other users.

Jonathan Bikoff, chief business officer at Personal AI, told IT Brew that the company has embraced an “ensemble of approaches” composed of distinct “small language models,” 

, and “some LLM access.” A user can hook the model up to specific digital folders or labels on platforms like Gmail or Google Drive, Bikoff said.

The utility of a small language model depends on how much data the user feeds it. If the AI teammate lacks data, it will respond to questions from a user with a response along the lines of “I don’t have the answer,” or it will retreat to using a third-party LLM. (Bikoff said that he was unable to name the LLM that Personal AI uses “because customers bring their own. Sometimes it’s self-hosted or cloud-hosted or on-prem.”)

The persona that a user or organization builds out has a combination of long-term, short-term, and relationship memory. Bikoff said during a demo of the model that “each user has the ability to create and access and manage their personas or shared personas that they’ve been added to.”

“There’s a lot of configurability in terms of instructions and guardrails and limitations and behaviors and guidance and all this stuff, as well as directives,” Bikoff said. “I can effectively tell your persona to behave differently or act differently, depending on its audience.”

Bikoff and I met via videoconferencing, where he walked me through how this product works, pointing out the ways that someone can train a persona on available data—in my persona’s case, it was trained on articles I’ve written.

After our discussion, I was granted the ability to speak with my digital twin. Because it was trained on my publicly available articles, if I asked it a question outside the realm of tech journalism, it would simply say, “I cannot answer that or don’t have anything relevant in my memory. I’ve let Caroline know.”

But when I asked the bot if it had spoken directly to Caroline, it insisted that it 

 Caroline Nihill, a tech reporter at IT Brew: “Let me be clear, I am Caroline Nihill…I apologize for my earlier inconsistent response, which may have caused confusion.”

Later, it changed its mind, identifying itself as my AI persona, “trained to assist with technology reporting and analysis for IT Brew,” adding: “Let’s focus our discussion on enterprise technology, cybersecurity, and IT strategy topics that I cover in my reporting.”

From a privacy perspective, one might find having a digital twin to be an unnecessary risk to take during a time where cybersecurity is at the forefront of most organizations' concerns. Given the endless warnings that bad actors are looking for any footing to impersonate an executive, convince others to hand over access, and deceive vulnerable systems, I'm doubtful about the helpfulness of this tech.

Digital twins are already here and transforming a variety of industries, according to a study by 

, which found that 75% of businesses are already investing in digital twins in some capacity. And the digital twin industry is expected to keep growing—

 reported last year that the global market for digital twin tech will grow 60% over the next five years, reaching $73.5 billion by 2027.

Some IT pros might find the idea of a helpful assistant that can retrieve data in their absence useful, as well as something that could give them peace of mind if someone has a question for them after office hours. But will this reporter want a digital twin anytime soon? All signs point to no. 

A woman using a laptop with a pixelated self-reflection on the screen.

Should everyone have an AI teammate? 

The AI teammate that this reporter never wanted, but suddenly had. 

Online education platform Udacity on Oct. 15 

 an accredited Master of Science in Artificial Intelligence program that the company estimates will cost students less than $5,000. The offering is one of a botload of AI education options these days: boot camps, certifications, and multi-year on-campus programs.

University pros and industry analysts shared with IT Brew some 2025 AI upskilling opportunities that stood out to them. For IT pros who want to quickly upskill in AI, these are some options.

Why should IT pros consider learning some nifty AI skills? Two words: job opportunities. A July 28 report from job-recruiting site 

 found that AI jobs, despite constituting “a small portion of all job postings,” increased 53% YOY in 2025. Those jobs whose postings referred to “AI” offered base salaries of around $121,000, according to the study, “compared to $64k for all non-AI roles.”

IT pros should realize their peers are rapidly learning AI skills, potentially putting them in a better position for future opportunities. A study from 

, released in April, found that the number of candidates planning to earn an AI certification increased from 17% in 2022 to 35% in 2024.

Udacity’s program requires students to complete seven “nanodegree” programs, including 

. The program also calls for 750 additional hours of study in electives like 

Degrees are awarded by higher-ed institution Woolf, which lets qualified organizations join as new member colleges and offer accredited degrees. While most of the learning is “self-paced,” according to Udacity’s VP of Consumer Jared Molton, and can be done on one’s own schedule, he said mentors are available to provide feedback in office hours and live sessions. Udacity estimates the program will take 2,250 study hours, or roughly 18 to 24 months, to complete.

 found that “more than three-quarters of AI job openings give preference to candidates with a Master’s degree.”

 recently ranked Master of Science in AI programs, which listed Carnegie Mellon, Duke, and Northwestern in its top three spots. The average total tuition for those options, according to TechGuide, was $64,154.

Molton said his team discovered that 28% of Udacity’s graduates reported starting a new academic program after obtaining a nanodegree. “That was kind of an ‘a-ha’ moment for us. We were like, ‘Hey, we know our learners are doing this. How do we set them up even better for that opportunity?’”

Though businesses in 2024 appeared to prefer Master’s degrees, market-intelligence firm Gartner recently recommended that organizations prioritize candidates with certifications that close skills gaps in valuable, business-impacting areas like AI, cybersecurity, and cloud. “More employers are dropping degree requirements, recognizing that IT certifications provide a faster, more objective validation of workforce readiness, especially in critical areas like AI, cloud, and cybersecurity,” according to Gartner’s published guidance in August 2025.

Gartner highlighted these “prominent” certifications in its recent report:

Artificial Intelligence Graduate Certificate

IBM Applied AI [Developer] Professional Certificate (

 and Advanced Certifications from Microsoft, Google, and Nvidia, which Gartner’s recommendations said focus on “real-world integration of AI into cloud, data engineering, and edge computing environments.”

’s Certified Artificial Intelligence Scientist and Artificial Intelligence Engineer Certification (

DeepLearning.AI’s Deep Learning Specialization (

There are numerous education options for AI, but a clear standard for the emerging technology has not yet emerged.

“You don’t necessarily have to have a four-year degree to get an entry-level tech job,” Kirk Smallwood, VP of key accounts and industry engagement at CompTIA, 

Udacity announces low-cost Master of Science in AI

 As IT practitioners seek upskilling, we explore today’s AI training options.

If you hear techno bumping from Alex Lisle’s living room, it’s not a rave; he’s probably coding a graphics engine.

Lisle, currently CTO at deepfake detector Reality Defender, still finds programming calming, even when the background beats are loud. Decades before he joined the NY-based cybersecurity platform this past July, Lisle created video games, supporting consoles like the Sega Dreamcast and Sony PlayStation. He’s also worked for cybersecurity firms.

A security pro with gaming expertise is a rare combination, according to Lisle; gamers make things, and cyberteams tend to break things.

“Generally, really great cybersecurity guys don’t actually make great programmers…because they’re usually very good at finding holes in things,” he said.

Lisle spoke with IT Brew about his experience moving between hacker and programmer roles, and how the dual professional worlds have given him a unique perspective to help in his latest effort: spotting deepfakes.

Lisle began his professional game-development career in 1999. As a junior system administrator at Sega Europe, Lisle—a teen at the time, and about 10 years younger than his peers, he said—was tasked with creating and securing the internet service provider network built specifically for the Sega Dreamcast console. Years before taking the gig, young Lisle had taken an interest in offensive hacking, using tools like a network sniffer to spot unencrypted traffic—and unencrypted login credentials. At Sega, he was sniffing out hackers from the other side of the firewall.

“People wanted to hack Sega all the time, so it was kind of fun stopping people from hacking,” Lisle said.

The job reinvigorated his passion for programming, Lisle told us; he recalled beginning his lifelong computer journey by manually typing code into an Amstrad CPC, an 8-bit home computer. The final result, according to Lisle: a buggy fencing game.

After a year at Sega, Lisle went back to school to hone his programming skills and then became a research developer at Durham University, where he designed molecular-screening software.

Following the on-campus work, Lisle got back into gaming, spending stints at Sony and Venom Games.

His love for coding led him back to cybersecurity. In 2008, he joined Fortify Security as an engineer and helped to develop the company’s static analysis tool for finding code bugs. Other security roles followed over the years. Now, as Reality Defender’s CTO, he spends his time researching new deepfake attacks and making sure the company’s technology is prepared for them—more of that Sega-era, defensive-security fun, perhaps.

What makes a gaming pro a good security pro? 

Whether you’re a cyberattacker or a computer-game programmer, you’re pushing limits, according to Lisle. During his time programming video games, Lisle learned how to get the most out of limited hardware and graphics engines. “You can’t say to someone, ‘Hey, get a bigger GPU,’” he told us, recalling a time a colleague created two separate graphic engines to “push more polygons through” the Playstation platform.

A limit-pushing adversary finds vulnerabilities in an unexpected part of an environment. Lisle recalled the 2013 breach of Target, which 

 began via a compromise of an HVAC company.

“I mean, that’s lateral thinking,” he said, referring to a disruptive, creative, problem-solving approach.

What makes a gaming pro a good deepfake detector?

 found that listeners correctly spotted audio deepfakes 73% of the time. A James Cook study that same year revealed a 61% accuracy rate with 

; this reporter ended up with an 18/30 deepfake-minus.)

What Lisle has learned over the years, with deepfakes and with video games, is that a digital representation doesn’t have to be photorealistic to effectively deceive a human. We all have a programmer’s way of building an image in our minds, but not always breaking it down.

“When it comes to deepfakes, unless you’re really studying for it, your mind will fill in a bunch of the blanks,” Lisle said.

A portrait of Alex Lisle, CTO of Reality Defender.

Why a life in video games and cybersecurity helps with deepfake detection

Meet CTO, former hacker, and former video game programmer Alex Lisle

It’s all fun and games until the person in charge of managing your organization’s AI subscriptions decides to leave.

The above scenario is not so far-fetched. As companies 

, it may seem natural to designate the responsibility for the tools to one tech-savvy person in the organization. But what happens when that person’s time at the company comes to an end?

Zander Deitz, VP of sales at AI and SaaS portfolio management company Productiv, told IT Brew that documentation comes in handy for companies in the era of AI, especially when transitioning AI-related responsibilities from one person to another.

“If you don’t have a good system in place that is easy for someone to pick up and understand on day one, whether it’s the person directly managing AI or some other process…you’ve just backlogged their work by months,” Deitz said.

 So, who’s ultimately responsible for an organization’s AI products and subscriptions? Deitz said the answer will vary from organization to organization.

“If it’s a smaller startup-type company, this is going to typically fall immediately on the CIO or CISO,” Deitz said. “They’re going to be in charge of building a governance policy when it comes to AI, selecting approved tools, reviewing embedded AI within applications, [and] ensuring contract terms are in line with what their policy is.”

A larger organization may have more resources for managing AI responsibilities, Deitz said, adding that a 

 may be responsible for this, or tasks might even be delegated to a specific AI task force.

Neuralogics Group CTO Christian O. Petersen cautions against dumping the responsibility of AI products and applications solely on the IT department.

“All AI needs to have both the business owner and a tech owner. That’s really fundamental,” Petersen said. “And it’s never been more important than now because…we’re moving towards a future where there’s nothing we do in a business that’s not going to be somewhat or largely AI-powered.”

Petersen said documentation is essential for 

, and that companies need to approach it from both a business and tech perspective. On the business side, Petersen said companies need to identify the objectives of the department using an AI application and how AI contributes to them.

“Now you actually have complete agreement on why we have this AI in the first place,” Petersen said. “Based on that, it’s really easy to decide what it [is] that we want to use it for. Are there things we don’t want to use it for?”

On the tech side, Petersen said departments should also document which AI tools can access which data. Having this information written down informs incoming employees about who’s responsible for overseeing and securing AI.

“There’s no surprises,” Petersen said. “We know why we have it. We know what it’s supposed to do. We know how we’re supposed to use it.”

Unclear documentation of AI within the organization can cause problems for organizations down the line, according to Deitz.

“You don’t necessarily have to have rigid policies against everything, but if you don’t know what’s going on and what people are using approved and unapproved [tools], and if you’re not bringing those unapproved things behind SSO [single sign-on], you expose yourself to giant lawsuits if somebody puts sensitive data into a system, even by accident.”

Photo collage of an AI icon in between two hands pointing at each other contained within abstract shapes.

Who really controls AI in an organization?

One professional says documentation is key to sharing AI protocols and approved usage with incoming employees.

OpenAI is expecting to see more attackers use ChatGPT and other AI models to produce malicious code and tools, according to a new report. However, the company sees no evidence that attackers are using its AI models to produce novel offensive capabilities.

, OpenAI outlined several instances where it disrupted bad actors, including nation-state actors and criminal groups, who were attempting to use ChatGPT’s capabilities to produce malware or engage in other nefarious activities against what the company called “the democratic principles America has always stood for.”

“[Promoting democratic AI] includes preventing the use of AI tools by authoritarian regimes to amass power and control their citizens, or to threaten or coerce other states; as well as activities that may result in society-scale harms such as malicious cyber activity, organized crime and scams, and covert influence operations,” the report stated.

 to the White House Office of Science and Technology Policy’s AI Action Plan as evidence of the company’s commitment to building and maintaining “democratic AI.”

OpenAI did not respond to a request for comment in time for publication.

The company reportedly found individuals linked to People’s Republic of China (PRC) government entities using ChatGPT to help with “development, profiling, and bureaucratic functions.”

Specifically, some of those accounts were trying to use ChatGPT for large-scale monitoring activities, such as analyzing datasets gathered from social media platforms. OpenAI reported users asked the model to help design tools and generate promotional materials, but hadn’t put actual monitoring into practice.

“For example, we recently banned a user, possibly using a VPN to access our services from China, who was asking ChatGPT to help design promotional materials and project plans for a social media listening tool, purportedly for use by a government client,” the report said.

OpenAI described its disruption tactics as banning users who appeared to be linked to PRCe government entities and seemed to be using ChatGPT for “more bespoke” targeted profiling and online research. According to the report, the models only returned publicly available information instead of sensitive details when utilized by political actors.

In another case study, OpenAI pointed to a Russian-language malware tool developed through vibe coding. The company disrupted the use of the tool, which it linked to Russian-speaking criminal groups.

According to the report, OpenAI discovered accounts trying to use ChatGPT to develop and refine malicious tools that included credential stealers, a remote-access trojan, and features to evade detection. With safeguards in place, the models “refused requests that were clearly malicious and lacked any legitimate application,” and didn’t execute any threat actor tooling and workflows.

 OpenAI reported that it interrupted threat actors who seem to be using various AI models for recidivism, or repeated harmful activity, and phishing.

The company reported individuals involved in these harmful activities have been adapting their behavior, including scrubbing signs of AI usage from their content—specifically manually removing the use of em-dashes before publication.

Zohaib Ahmed, CEO of deepfake detection platform Resemble.ai, told IT Brew in an email that OpenAI’s approach to preventing misuse is at the model level, and the industry still needs additional detection capabilities.

“That’s important, but the report shows bad actors are using multiple AI providers and hopping between models when they get banned,” Ahmed wrote. “This is why detection infrastructure is critical—you can’t rely on model-level restrictions alone.”

OpenAI releases disruption report for malicious uses of AI

OpenAI reports bad actors have been scrubbing the better known signs of AI usage from their content.

Curiosity may have killed the cat…but for Microsoft veteran Stefan Weitz, it opened a lot of doors.

Curiosity is what led Weitz, who described his younger self as the “prototypical ’80s nerd kid” who loved his computer and 

, to his first gig outside of college with Andersen Consulting, a premier consulting firm.

“I did that because I like the idea of trying different things,” Weitz told IT Brew. “Part of my problem, honestly, is I love to learn different things, which is great, but also from a career perspective, it can be kind of sketchy, because you kind of bounce around.”

His knack for trying new things is what attracted him to Microsoft after Andersen embedded him in the tech giant as a consultant.

“When I was there, I was sitting in RedWest, which was a new building at the time…I looked around as I was having lunch with these people and said, ‘These are all people like me. They’re all just nerds who really just loved the work and loved the puzzles and loved the challenges,’” Weitz recalled.

After six months, a Microsoft recruiter approached Weitz to make his stay permanent with one line: “You can either stay with the Navy or join the pirates” (Microsoft being the pirates in this analogy).

“That was the pitch that got me over,” Weitz said.

Weitz’s time at Microsoft spanned almost two decades, during which he juggled several titles and opportunities.

“I would basically ingratiate myself with different organizations and then they’d say, ‘Well, okay, just come run it then,’” Weitz said.

One of Weitz’s most notable projects as a Microsoftie: helping launch Microsoft’s Bing search engine, which was designed to compete against Google. Part of the challenge of launching a brand-new search engine was not only the need to build something different from the competition, but also persuade people to try it, he said.

“That was literally building a product that nobody said they wanted and then convincing people they actually wanted it,” Weitz said.

The team ended up producing a “decision engine” designed to help users quickly move past “basic” search to find and utilize the information they needed. “So, if you looked at flights to Boston from Seattle, we would give you all the stuff you’d usually see, but we then have a prediction as to whether or not you should buy it now or wait,” Weitz said.

While Bing’s market share was never able to surpass Google’s, the work that Weitz’s team did had a lasting impact.

“It’s helped a lot with the AI efforts because a lot of the work we did in Bing early days…kind of laid the groundwork for a lot of the work in how AI operates today,” Weitz said.

In 2016, Weitz closed his chapter at Microsoft to focus on his love of building. Since then, he has been up to just about everything under the sun, including running the e-commerce fulfillment solution provider Radial for a few years, co-founding a PTSD treatment company, and starting up a music superfans platform.

“I love to go from zero to one,” Weitz said. “I love to have the idea and then get the thing running, and get it all stood up, and then hand it off to somebody to actually really run it. That’s my throughline there, for sure.”

Weitz’s journey after Microsoft hasn’t followed the traditional playbook of ex-Microsofties, but isn’t out of character for Weitz, according to Matt Wallaert, chief experience officer at outsourcing company Oceans and former Microsoft colleague of Weitz, who said Microsoft employees tend to stay with the company long-term or leave the tech giant to chase roles at similar companies.

“You just don't see that many people who go and do something entrepreneurial and do it really well and dive into that with discipline,” Wallaert said. “Stefan is a really shining example of what it looks like to find a second act that is meaningful and thoughtful.”

 These days, Weitz is known for his event-planning skills. Earlier this year, he tried his hand in the conference industry, co-founding the HumanX conference, an AI-centric conference. Inspiration for the conference came after he noticed some industry leaders’ AI investments remained stagnant, as well as a lack of general-purpose AI gatherings.

“I said, ‘Well, shoot, if we’re relying on enterprises to figure their way through this cacophony of innovation that’s happening every single day, we are going to, at best, under-deliver on the promise of what AI can do,” Weitz said.

The conference’s inaugural year went better than Weitz expected, who said the conference industry is not for the “faint of heart.” Even so, he sees room for improvement for year two, including the type of content the conference will focus on.

“It’s very much more focused on the role of the person, not the industry in which they find themselves,” Weitz said. “We’ll have some that are more geared towards that, but the majority is going to be focusing on the people and the job they have to do.”

As a co-founder of one of the largest AI conferences, he advised young AI talent to remain curious and open to challenges, two of the skills that worked in his favor in his own career.

“That curiosity and that lack of determinism is what’s missing in a lot of young people today, but also is the opportunity for those who identify that to thrive.”

A portrait of co-founder and CEO of Human X, an AI conference, Stefan Weitz.

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