• Five people who earn more than $100,000 annually shared how they're spending their money.
• They're trying to balance spending on big purchases with saving for future goals.
• Some have spent money on a new car or travel, while others have invested in a home or startup.

For some, earning a six-figure income can facilitate a big splurge. For others, it's an opportunity to establish additional income streams or financial security.

Abid Salahi earns about $140,000 a year from his software engineering job. The 26-year-old, based in Vancouver, said his biggest purchase over the past year was a new car that cost roughly $37,000. Additionally, Salahi said he upgraded his home workspace.

Despite his earnings, one thing has been out of his reach: owning a home. The houses in his area that check his boxes cost more than $500,000. To afford a down payment, Salahi said he's saving and being more judicious about how much he spends dining out and at the grocery store.

Reaching a six-figure salary can be a challenge for some employees. The average annual salary for US-based full-time workers was about $82,000 as of November, the latest data available, per a New York Fed survey. Some workers who earn more than six figures have used the opportunity to set themselves up for potential future success.

Business Insider asked five people who've made more than $100,000 annually what they've spent their money on in recent years. BI has verified their six-figure earnings.

Balancing spending now and saving for the future

Earning a six-figure income has also created new opportunities for John, who's on track to earn roughly $250,000 this year by balancing a full-time and part-time remote IT role.

The millennial, who's based in California, said one of his biggest expenses over the past year was his sister's medical bills, which were about $30,000, he said.

When he spends money on himself, he focuses on fun and health. He hired a personal trainer, who charges about $130 weekly for a one-hour session. Last year, he spent about $9,000 on a three-week honeymoon in Asia.

While he's trying to take advantage of his money in the present, John said he's also prioritized saving for the future.

"I follow a concept of 'pay yourself first' — where I put money into retirement and savings first, and then the rest is disposable," said John. His identity is known to BI, but he asked to use a pseudonym due to fears of professional repercussions.

Looking forward, John said he's saving money for the children he hopes to have one day, a bigger car, and a home.

Corritta Lewis is also balancing spending now while saving for the future. Last year, Lewis earned roughly $280,000 from her consulting job and a travel blog she runs as a side hustle. The 35-year-old, who's based in Orlando, said she and her wife spend most of their disposable income on travel.

"We've been digital nomads for four years, so most of our money was used to travel the world and have amazing experiences," she said.

Despite her travel expenses, Lewis said she doesn't live a luxurious lifestyle and is focused on long-term saving. She aims to work part-time hours by her 40th birthday.

"Right now, we are prioritizing savings and investments," she said.

Investing in themselves and real estate

Margaret Pattillo took home around $128,000 last year from her digital marketing and PR business. The 27-year-old, who's based in Florida, said she's on track to earn more than $160,000 this year.

Pattillo used her earnings to buy a home earlier this year and has plans to buy a second home as an investment property. She tries to use her money to create additional income streams that will set her up for future financial success.

"I don't place much value in material items and I'm lucky that I get to travel for work frequently," she said. "I'd say my biggest goal is to build up as many cash-flowing assets as I can in the next 10 years."

Christopher Stroup has put his earnings toward a different type of investment: a new business.

Stroup earned roughly $130,000 last year working as a financial advisor. The 33-year-old, who's based in California, said his income has helped him improve his relationships with friends and family by giving him the budget to go out to eat and on trips. He said his goal is to travel to Europe at least once a year.

Over the past year, Stroup said the biggest thing he's spent his money on is the financial planning business he launched in September. He said his startup costs have included marketing expenses and hiring a team. However, he hopes the investment in his business will put him in an even better financial position.

"If it works out well, achieving my financial goals on my desired timeline has a much higher probability of happening," he said, adding that two of his main goals are owning a home and starting a family.

Are you making over $100,000 a year? Are you willing to share your story and the impact this income has had on your life? If so, contact this reporter at [email protected].

• Computer science graduates are struggling to secure jobs and internships amid increased competition from tech layoffs.
• Recent graduates told BI they have sent hundreds of job applications with little response.
• Some are choosing to pursue a "panic master's" degree to delay their job search.

A computer science degree has become an increasingly popular choice for students seeking a six-figure job in Big Tech out of college.

However, as the tech industry took a sharp turn from the hiring sprees during the pandemic to mass layoffs, conversations with over a dozen CS majors revealed many are struggling to find full-time roles and internships despite sending out hundreds of applications — sometimes as many as 700.

Now, some are opting for a "panic master's" instead, delaying their search by getting a graduate degree in the hopes the job market will improve in a year or two.

Samhita Parvatini, who graduated from Penn State University in May, told Business Insider that she entered college during the hiring frenzies of 2021 when computer science degrees were "highly sought out."

"Every industry needed engineers," she said. "Everybody said, 'Oh, it's one of the most valuable degrees you can get. You can earn so much money, you get a lot of success and career growth.'"

After roughly 250 to 300 applications since her graduation and little success, Parvatini said that the Big Tech landscape felt like it was "becoming the opposite" of what it was five years ago.

Software developer employment largely declined between late 2019 and early 2024, according to data from ADP Research Institute, with some spikes in the second half of 2021 and winter 2022 amid the pandemic hiring spree. Data from Indeed indicates job postings in the software development sector have largely dropped back to pre-pandemic levels.

Meanwhile, videos showing swarms of candidates at job fairs have become an increasingly common sight on social media.

Yahya Bashir, a recent CS graduate from Gustavus Adolphus College, said that his job-hunting experience in the last year has become more arduous.

During his last application cycle in the summer of 2023, Bashir said he often heard back quickly from companies and was invited to several interviews. However, the majority of the roles he applied to this year, which he estimates to be around a hundred, didn't reply.

"Most of them, you don't even hear back from them," Bashir said. "You submit your application, and there's just nothing."

Competing against laid-off coders with more experience

Facing low response rates and, in some cases, "ghost job" postings, software engineers fresh out of school are also having to compete with their more experienced peers.

With companies continuing to trim their staff, the tech sector has also faced two years of brutal layoffs. In 2022, over 165,000 employees were cut from a thousand tech companies, according to Layoffs.fyi, a website tracking tech layoffs. In 2023, the number of layoffs increased to over 264,000. So far in 2024, nearly 150,000 employees have been cut from over 520 tech companies.

With hundreds of thousands of already established tech workers cut loose into the job market, new graduates are facing increased competition for fewer openings.

Emos Ker, a recent graduate from New York University, said that although sub-industries within computer science, like AI and LLMs, are booming as Big Tech invests heavily, these fields often require a higher level of training.

Although more universities like Carnegie Mellon and Columbia are starting to offer AI degrees and programs, Ker said that many institutions are not yet able to provide the specific education needed for more specialized fields like AI.

Looking through a stack of résumés, companies may choose to hire a seasoned Big Tech veteran over a CS graduate who would likely require more guidance.

"With all the tech firings, they're looking for people who are like midlevel, senior engineers," Ker said. "And unfortunately, for people like us who want to come out and work in AI, it's not really easy to get into because you kind of need to train us from the ground up."

Punting the hunt with a 'panic' master's

Instead of risking being hung out to dry in the job market, several recent computer science graduates told BI that they or their peers have opted to return to the classroom to delay the search.

"The funny thing is, when I started my undergrad, I was very stubborn and was like, 'Oh, I don't need a master's,'" Parvatini said. "'It's a CS degree, you know, it's so valuable."

A month out from graduation and without a job lined up, Parvatini said she applied for her master's as a "last-minute decision."

"I knew that I wasn't going to go anywhere after graduation," she said. "So I thought, might as well apply, and we'll take a couple of classes, you know, do something better with my time during this period."

Professor David Garlan, the associate dean for Carnegie Mellon's computer science master's program, said that while the university hasn't seen a notable increase in CS grad enrollment, other schools with less selective and extensive programs may experience otherwise.

"It's definitely true that when the economy has a downturn, people go back to education because they're not able to find jobs so quickly," he said. "So there is definitely that trend, overall."

Enrollment in MIT's EECS Master of Engineering program increased from 241 students for the 2023-2024 academic year to 303 this academic year — a spike compared to previous years when enrollment stayed relatively consistent in the mid-200s.

A report by the Council of Graduate Schools said that computer science was the "only field to increase in first-time enrollment (5.4%) between Fall 2021 and Fall 2022."

Ian Hurrel, who is finishing his last semester at Georgia Institute of Technology, said enrolling in the university's one-year master's program was largely due to the worsening job market.

"A lot of people, including me, wanted to stay in college one more year to get an internship," Hurrel said. "It was very much a 'panic masters' sort of thing."

Although computer and information sciences often have lower numbers of graduate enrollment compared to other fields, a report by Burning Glass Institute indicated that 7% of those who earned graduate degrees in CS remained unemployed.

The extra schooling, while costly, can not only buy students time, it can potentially lead to a more employable résumé and higher salary. According to data from PayScale published last year, employees with a master of science degree make an average base salary of $112,000, compared to $72,000 for those with a bachelor's degree in computer science.

'Perseverance and a little bit of luck'

Despite lower morale among some CS majors, others believe that the tech sector is not as dire as social media portrays it.

Sydney Bishop, a senior at UC Irvine, said despite being unable to land an internship this past summer after over 180 applications, she remains optimistic about the job market.

"I haven't lost faith that I'll get a job somewhere," Bishop said. "It just might not be a cushy tech job that all of us have been raised to think about."

While tech giants like Google and Microsoft may not be handing out as many opportunities as they did during their hiring peaks, Bishop said that the technical skills of programming are still — and will continue to be — needed within companies.

Hurrel, who was able to land an internship with Amazon this past summer, said that he disagrees with the "fear-mongering" from people online saying computer science is a dying degree.

"I don't think it's oversaturated to the point where it will become extremely devalued and not be a worthwhile career anymore," he said. "I think it's just going to be harder than it was at the peak to break into it."

Hurrel added that there are "clearly still jobs" and that several of his peers have also been able to land internships and full-time roles. The US Bureau of Labor Statistics projects that employment of software developers will increase by 18% by 2033.

Samuel Onabolu is one of these newly minted engineers. After what he estimated to be over a thousand applications, he was finally able to land a full time software engineering role four months after graduating from Brock University in May.

"I'm kind of surprised I even got a job so early because there are 2023 grads, 2022 grads that are still looking," he said. "So I would say it's just a lot about perseverance and a little bit of luck."

Onabolu said that while he had been "feeling really depressed" during his unsuccessful job search, he advised other new and incoming grads to prioritize internships and networking events to hopefully get their foot in the door.

"I feel like every CS major is going through the exact process I went through," he said. "I feel like it just takes that one acceptance, that one offer, to kind of break into that career."

• AWS said developers spend most of their time on non-coding tasks, impacting productivity.
• It introduced Amazon Q Developer — an AI agent to aid developers — at the re:Invent keynote on Tuesday.
• But junior engineers are concerned AI tools like Amazon Q could reduce coder demand.

Artificial intelligence could give coders more time to code. Programmers aren't sure whether that's a good thing.

In a post Tuesday, Amazon Web Services said developers report spending an average of "just one hour per day" on actual coding.

The rest is eaten up by "tedious, undifferentiated tasks," AWS said. That includes learning codebases, drafting documents, testing, overseeing releases, fixing problems, or hunting down vulnerabilities, AWS said. The company didn't say where it got the data.

AWS CEO Matt Garman spoke to the developers in the audience at the company's re:Invent keynote on Tuesday, introducing a tool he said would give them more time to focus on creativity. Amazon Q Developer is an AI agent that AWS is rolling out in two tiers with free and paid options.

The announcement is another indication that technology like AI could upend the way many coders do their jobs. Some have argued that AI will remove some of the tedium from tasks like creating documentation and generating basic code. That could be great for coders' productivity — and perhaps for their enjoyment of the jobs — yet it could also mean employers need fewer of them.

GitLab has reported that developers spend more than 75% of their time on tasks other than coding. Several veteran software engineers previously told BI that the time they spend coding is perhaps closer to half.

Software engineers on job forums like Blind are discussing how much they should rely on an AI assistant for their work. Some have asked for recommendations for the best agent, and receive mixed replies of "your own brain" and genuine reviews. Others worry that AI has already become a crutch in their coding process.

AWS isn't the only tech giant offering AI to coders. Google CEO Sundar Pichai recently said that AI generates more than a quarter of the new code created at the search company. He said the technology was "boosting productivity and efficiency." Workers review the code that AI produces, Pichai said.

"This helps our engineers do more and move faster," he said. "I'm energized by our progress and the opportunities ahead, and we continue to be laser-focused on building great products."

The rise of AI could be worrisome for newbie programmers who need to develop their skills, according to Jesal Gadhia, head of engineering at Thoughtful AI, which creates AI tools for healthcare providers.

"Junior engineers," Gadhia previously told BI, "have a little bit of a target behind their back."

He said that when an AI tool touted as the "first AI software engineer" came out this year, he received texts from nervous friends.

"There was a lot of panic. I had a lot of friends of mine who messaged me and said, 'Hey, am I going to lose my job?'" Gadhia said.

• A Stanford researcher says his algorithm pinpoints employees who are doing the bare minimum.
• Roughly 9.5% of coders are "ghost engineers" according to his research, which has not been peer-reviewed.
• The research underscores tech's newfound mania with rooting out low performers.

Quiet quitting. Lazy-girl jobs. Bare-minimium Mondays.

Over the past two years, employees have expressed repeatedly that they are fed up with being asked to do too much.

Tough luck. The latest catchphrase to describe working less is "ghost engineer" — and it comes not from burnt-out employees but from a Stanford researcher whose team has developed an algorithm to help tech companies identify freeloading coders.

Stanford researcher and former Olympic-level weightlifter Yegor Denisov-Blanch ran the algorithm, which grades the quality and quantity of employees' code repositories on GitHub, on the work of more than 50,000 employees across hundreds of companies.

Roughly 9.5%, he found, "do virtually nothing."

Measuring output is difficult

Denisov-Blanch calls these workers "ghost engineers," defined as software engineers who are only 10% as productive or less than their median colleague.

His research began as an attempt to find a better way to grade the performance of software engineers, he said in an interview with Business Insider.

"Software engineering is a black box," Denisov-Blanch said. "Nobody knows how to measure software engineers' performance. Existing measures are unreliable because they rate equal work differently."

"It's not fair when someone's doing a very complicated change that's only one line of code. And the person doing the very simple change that's 1,000 lines gets rewarded," he continued.

His algorithm attempts to resolve that tension, giving high ratings to engineers who write many lines of code only so long as that code is maintainable, solves complex problems, and is easy to implement.

Denisov-Blanch's research has not been peer-reviewed.

There are other caveats. Industry-wide, the 9.5% figure could be an overstatement because Denisov-Blanch's research team ran the algorithm only on companies that volunteered to participate in the study, introducing selection bias.

Conversely, while Denisov-Blanch's team didn't classify employees whose output is only 11% or 12% of the median engineer's as "ghost engineers," there's a strong argument that those employees aren't contributing much either, which could mean the 9.5% figure is an understatement.

The hunt for underperformers

Rooting out underperformers has lately become something of a mania among some in Silicon Valley.

In September, Y Combinator co-founder Paul Graham published an essay lauding a management style he called "founder mode," which he distinguished from the conventional wisdom of, in his words, "hire good people and give them room to do their jobs."

"In practice, judging from the report of founder after founder, what this often turns out to mean is: hire professional fakers and let them drive the company into the ground," Graham wrote.

Heading the charge has been Elon Musk, who has spoken proudly about firing 80% of Twitter's employees after buying the company in 2022. Twitter, now X, didn't appear to experience significant outages or service interruptions following the staff reduction.

"Were there many mistakes along the way? Of course. But all's well that ends well," he told CNN. "This is not a caring-uncaring situation. It's like, if the whole ship sinks, nobody's got a job."

More remote workers were superstar coders

Musk now aims to apply that same ruthless efficiency to the federal government. As co-chair of a new Department of Government Efficiency, he pledged in a Wall Street Journal op-ed to slash federal staffing, including by ending remote work to spur resignations.

"If federal employees don't want to show up, American taxpayers shouldn't pay them for the Covid-era privilege of staying home," Musk wrote.

Denisov-Blanche's research showed mixed results for remote work. On one hand, he found that the prevalence of "ghost engineers" among remote workers was more than double that among in-person workers.

But he also found that many more of the most effective engineers — employees whose performance was at least five times better than their median colleague — were working remotely than were in-person.

• A 28-year-old used an AI tool called AIHawk to apply for hundreds of jobs on LinkedIn.
• He said AIHawk helped him land a software engineering job.
• Using AI tools during the application process comes with risks.

Applying for jobs can be a time-consuming and frustrating process, and some job seekers are using AI to try to make it more tolerable.

Guilherme, a 28-year-old based in Brazil, began looking for a software engineering role after he was laid off in April. In October, after little luck, he learned about AIHawk — a tool that allows users to easily apply for up to hundreds of jobs per day. One month later, AIHawk had submitted more than 1,300 applications on Guilherme's behalf and he landed a job.

"This the type of job I was looking for," said Guilherme, whose identity was verified by Business Insider but asked to use a pseudonym. "It was certainly a byproduct of AIHawk."

Guilherme is among the people who have struggled to find work over the past year and turned to AI tools to help them write résumés and cover letters, prepare for interviews, and apply for jobs.

To be sure, relying on AI during the application process comes with risks — including a résumé littered with mistakes — and it could be a dealbreaker for some HR departments. Additionally, in the quickly evolving AI landscape, there's a lack of clarity over how employers and job platforms view candidates' use of these tools.

Automating the job search process can save time

Federico Elia created AIHawk earlier this year, and in August, he published the code hosting platform GitHub so anyone could use the tool. AIHawk automates the application process for LinkedIn's easy-apply jobs — which pulls info from a user's profile to fill in an application. To date, AIHawk has been "starred" — or bookmarked — on GitHub by more than 22,000 people globally. There are more than 6,300 members of the AIHawk community on the messaging service Telegram, where users critique the tool, share tips on how to use it, and provide updates on their job searches.

AIHawk is one of many AI job application tools on the market. While it can be installed and used without any cost, users previously told BI that doing so requires some familiarity with the programming language Python.

Guilherme's tech background made it easier to use the tool. He said AIHawk typically applied to about 50 jobs a day and that some of these applications turned into interviews.

Guilherme ultimately was hired for a job he didn't apply for using AIHawk. He said he learned about the role after someone from the company reached out to him via LinkedIn. However, Guilherme believes AIHawk played a major role in the outcome of his job search. When he started using the tool, he said he began hearing from several recruiters about jobs he'd never applied for.

"I got several LinkedIn InMails a day, every single day, since mid-October, from recruiters, hiring managers, and C-suites of companies," he said, adding, "This was something that never happened to me before."

Guilherme said that he believes applying for so many jobs "boosted" his LinkedIn profile in the platform's algorithm — making it easier for recruiters to find him.

"With my account's activity being through the roof, my profile was boosted up in searches, which led to my new boss finding me," he said.

A LinkedIn spokesperson told BI that applying to more roles would not make a person's profile more visible to a recruiter. The spokesperson said that job seekers who keep their profiles up to date are more likely to hear from recruiters.

The spokesperson said that the company doesn't permit the use of third-party software — such as bots — that scrapes or automates activity on LinkedIn.

Guilherme recommended that AIHawk users spend time filtering out job titles that aren't a good fit and use interviews as an opportunity to practice their communication skills — which could help them land a job down the road.

Overall, Guilherme said the biggest perk of AIHawk was the time it saved him.

"Imagine if I had to do this manually?" he said, referring to the resumes he submitted with AIHawk. "I'd probably go insane."

Are you looking for a job and comfortable sharing your story with a reporter? Did an AI job tool help you land a job recently? Please fill out this form.

This article is part of "CXO AI Playbook" — straight talk from business leaders on how they're testing and using AI.

The future of software-development jobs is changing rapidly as more companies adopt AI tools that can accelerate the coding process and close experience gaps between junior- and senior-level developers.

Increased AI adoption could be part of the tech industry's "white-collar recession," which has seen slumps in hiring and recruitment over the past year. Yet integrating AI into workflows can offer developers the tools to focus on creative problem-solving and building new features.

On November 14, Business Insider convened a roundtable of software developers as part of our "CXO AI Playbook" series to learn how artificial intelligence was changing their jobs and careers. The conversation was moderated by Julia Hood and Jean Paik from BI's Special Projects team.

These developers discussed the shifts in their day-to-day tasks, which skills people would need to stay competitive in the industry, and how they navigate the expectations of stakeholders who want to stay on the cutting edge of this new technology.

Panelists said AI has boosted their productivity by helping them write and debug code, which has freed up their time for higher-order problems, such as designing software and devising integration strategies.

However, they emphasized that some of the basics of software engineering — learning programming languages, scaling models, and handling large-scale data — would remain important.

The roundtable participants also said developers could provide critical insight into challenges around AI ethics and governance.

The roundtable participants were:

• Pooya Amini, software engineer, Meta.
• Greg Jennings, head of engineering for AI, Anaconda.
• Shruti Kapoor, lead member of technical staff, Slack.
• Aditi Mithal, software-development engineer, Amazon Q.
• Igor Ostrovsky, cofounder, Augment.
• Neeraj Verma, head of applied AI, Nice.
• Kesha Williams, head of enterprise architecture and engineering, Slalom.

The following discussion was edited for length and clarity.

Julia Hood: What has changed in your role since the popularization of gen AI?

Neeraj Verma: I think the expectations that are out there in the market for developers on the use of AI are actually almost a bigger impact than the AI itself. You hear about how generative AI is sort of solving this blank-paper syndrome. Humans have this concept that if you give them a blank paper and tell them to go write something, they'll be confused forever. And generative AI is helping overcome that.

The expectation from executives now is that developers are going to be significantly faster but that some of the creative work the developers are doing is going to be taken away — which we're not necessarily seeing. We're seeing it as more of a boilerplate creation mechanism for efficiency gains.

Aditi Mithal: I joined Amazon two years ago, and I've seen how my productivity has changed. I don't have to focus on doing repetitive tasks. I can just ask Amazon Q chat to do that for me, and I can focus on more-complex problems that can actually impact our stakeholders and our clients. I can focus on higher-order problems instead of more-repetitive tasks for which the code is already out there internally.

Shruti Kapoor: One of the big things I've noticed with writing code is how open companies have become to AI tools like Cursor and Copilot and how integrated they've become into the software-development cycle. It's no longer considered a no-no to use AI tools like ChatGPT. I think two years ago when ChatGPT came out, it was a big concern that you should not be putting your code out there. But now companies have kind of embraced that within the software-development cycle.

Pooya Amini: Looking back at smartphones and Google Maps, it's hard to remember how the world looked like before these technologies. It's a similar situation with gen AI — I can't remember how I was solving the problem without it. I can focus more on actual work.

Now I use AI as a kind of assisted tool. My main focus at work is on requirement gathering, like software design. When it comes to the coding, it's going to be very quick. Previously, it could take weeks. Now it's a matter of maybe one or two days, so then I can actually focus on other stuff as AI is solving the rest for me.

Kesha Williams: In my role, it's been trying to help my team rethink their roles and not see AI as a threat but more as a partner that can help boost productivity, and encouraging my team to make use of some of the new embedded AI and gen-AI tools. Really helping my team upskill and putting learning paths in place so that people can embrace AI and not be afraid of it. More of the junior-level developers are really afraid about AI replacing them.

Hood: Are there new career tracks opening up now that weren't here before?

Verma: At Nice, we have something like 3,000 developers, and over the last, I think, 24 months, 650 of them have shifted into AI-specific roles, which was sort of unheard of before. Even out of those 650, we've got about a hundred who are experts at things like prompt engineering. Over 20% of our developers are not just developers being supported by AI but developers using AI to write features.

Kapoor: I think one of the biggest things I've noticed in the last two to three years is the rise of a job title called "AI engineer," which did not exist before, and it's kind of in between an ML engineer and a traditional software engineer. I'm starting to see more and more companies where AI engineer is one of the top-paying jobs available for software engineers. One of the cool things about this job is that you don't need an ML-engineering background, which means it's accessible to a lot more people.

Greg Jennings: For developers who are relatively new or code-literate knowledge workers, I think they can now use code to solve problems where previously they might not have. We have designers internally that are now creating full-blown interactive UIs using AI to describe what they want and then providing that to engineers. They've never been able to do that before, and it greatly accelerates the cycle.

For more-experienced developers, I think there are a huge number of things that we still have to sort out: the architectures of these solutions, how we're actually going to implement them in practice. The nature of testing is going to have to change a lot as we start to include these applications in places where they're more mission-critical.

Amini: On the other side, looking at threats that can come out of AI, new technologies and new positions can emerge as well. We don't currently have clear regulations in terms of ownership or the issues related to gen AI, so I imagine there will be more positions in terms of ethics.

Mithal: I feel like a Ph.D. is not a requirement anymore to be a software developer. If you have some foundational ML, NLP knowledge, you can target some of these ML-engineer or AI-engineer roles, which gives you a great opportunity to be in the market.

Williams: I'm seeing new career paths in specialized fields around ML and LLM operations. For my developers, they're able to focus more on strategy and system design and creative problem-solving, and it seems to help them move faster into architecture. System design, system architecture, and integration strategies — they have more time to do that because of AI.

Jean Paik: What skills will developers need to stay competitive?

Verma: I think a developer operating an AI system requires product-level understanding of what you're trying to build at a high level. And I think a lot of developers struggle with prompt engineering from that perspective. Having the skills to clearly articulate what you want to an LLM is a very important skill.

Williams: Developers need to understand machine-learning concepts and how AI models work, not necessarily how to build and train these models from scratch but how to use them effectively. As we're starting to use Amazon Q, I've realized that our developers are now becoming prompt engineers because you have to get that prompt right in order to get the best results from your gen-AI system.

Jennings: Understanding how to communicate with these models is very different. I almost think that it imparts a need for engineers to have a little bit more of a product lens, where a deeper understanding of the actual business problem they're trying to solve is necessary to get the most out of it. Developing evaluations that you can use to optimize those prompts, so going from prompt engineering to actually tuning the prompts in a more-automated way, is going to emerge as a more common approach.

Igor Ostrovsky: Prompt engineering is really important. That's how you interact with AI systems, but this is something that's evolving very quickly. Software development will change in five years much more rapidly than anything we've seen before. How you architect, develop, test, and maintain software — that will all change, and how exactly you interact with AI will also evolve.

I think prompt engineering is more of a sign that some developers have the desire to learn and are eager to figure out how to interact with artificial intelligence, but it won't necessarily be how you interact with AI in three years or five years. Software developers will need this desire to adapt and learn and have the ability to solve hard problems.

Mithal: As a software developer, some of the basics won't change. You need to understand how to scale models, build scalable solutions, and handle large-scale data. When you're training an AI model, you need data to support it.

Kapoor: Knowledge of a programming language would be helpful, specifically Python or even JavaScript. Knowledge of ML or some familiarity with ML will be really helpful. Another thing is that we need to make sure our applications are a lot more fault-tolerant. That is also a skill that front-end or back-end engineers who want to transition to an AI-engineering role need to be aware of.

One of the biggest problems with prompts is that the answers can be very unpredictable and can lead to a lot of different outputs, even for the same prompt. So being able to make your application fault-tolerant is one of the biggest skills we need to apply in AI engineering.

Hood: What are the concerns and obstacles you have as AI gains momentum? How do you manage the expectations of nontech stakeholders in the organization who want to stay on the leading edge?

Ostrovsky: Part of the issue is that interacting with ChatGPT or cloud AI is so easy and natural that it can be surprising how hard it is actually to control AI behavior, where you need AI to understand constraints, have access to the right information at the right time, and understand the task.

When setting expectations with stakeholders, it is important they understand that we're working with this very advanced technology and they are realistic about the risk profile of the project.

Mithal: One is helping them understand the trade-offs. It could be security versus innovation or speed versus accuracy. The second is metrics. Is it actually improving the efficiency? How much is the acceptance rate for our given product? Communicating all those to the stakeholders gives them an idea of whether the product they're using is making an impact or if it's actually helping the team become more productive.

Williams: Some of the challenges I'm seeing are mainly around ethical AI concerns, data privacy, and costly and resource-intensive models that go against budget and infrastructure constraints. On the vendor or stakeholder side, it's really more about educating our nontechnical stakeholders about the capabilities of AI and the limitations and trying to set realistic expectations.

We try to help our teams understand for their specific business area how AI can be applied. So how can we use AI in marketing or HR or legal, and giving them real-world use cases.

Verma: Gen AI is really important, and it's so easy to use ChatGPT, but what we find is that gen AI makes a good developer better and a worse developer worse. Good developers understand how to write good code and how good code integrates into projects. ChatGPT is just another tool to help write some of the code that fits into the project. That's the big challenge that we try to make sure our executives understand, that not everybody can use this in the most effective manner.

Jennings: There are some practical governance concerns that have emerged. One is understanding the tolerance for bad responses in certain contexts. Some problems, you may be more willing to accept a bad response because you structure the interface in such a way that there's a human in the loop. If you're attempting to not have a human in the loop, that could be problematic depending on what you want the model to do. Just getting better muscle for the organization to have a good intuition about where these models can potentially fail and in what ways.

In addition to that, understanding what training data went into that model, especially as models are used more as agents and have privileged access to different applications and data sources that might be pretty sensitive.

Kapoor: I think one of the biggest challenges that can happen is how companies use the data that comes back from LLM models and how they're going to use it within the application. Removing the human component scares me a lot.

Verma: It's automation versus augmentation. There are a lot of cases where augmentation is the big gain. I think automation is a very small, closed case — there are very few things I think LLMs are ready in the world right now to automate.