How are CTOs feeling about AI?

According to Andy Skipper, founder of CTO Craft, they’re experiencing fear, uncertainty, and doubt.

And if the technical leaders of companies are feeling that way, what can the rest of us expect? Certainly, we dream of productivity boosts and an AI El Dorado – but that’s not the reality.

That’s why we sat down with Skipper to talk about how CTOs should manage expectations for AI, and how to navigate the hype versus reality.

Stakeholders and investors are watching CTOs closely, and the pressure is rising

Many CTOs, Skipper notes, are navigating intense pressure from non-technical stakeholders and investors alike, especially with the massive resources being invested in AI and LLM technologies.

He’s a bit careful about this:

AI is not going to reduce costs or increase productivity in the way some non-technical people think just yet. It’s getting there, but it’s not there yet.

At the same time, Skipper points out a surprising upside: AI is giving engineering leaders a chance to reconnect with the code and architecture without writing all the code themselves:

One of the things you have to accept as an engineering leader is that you are going to get further away from the code the more senior you become. AI gives people an opportunity to get back to architecture and development work, even if they aren’t coding themselves.

CTO role can be isolating

When Skipper became a CTO for the first time, he quickly realized just how isolating the role could be. There was nowhere for tech leaders to share challenges, get support, or navigate the non-technical side of the job.

That gap inspired him to start CTO Craft, now a community helping senior engineering leaders navigate team dynamics, strategy, and AI.

When I was a CTO for the first time, I didn’t have somebody who I could talk to about the issues I was seeing or compare notes with people who had similar challenges. That’s what CTO Craft is all about – helping people understand where the challenges come from and understand they’re not alone in having those challenges.

As a coach and mentor, Andy works closely with CTOs around the world, helping them deal with issues like burnout, communication with nontechnical stakeholders, and, lately, how to adapt in the AI era.

The most common CTO mistake? Always chasing the newest technologies

Many first-time CTOs struggle with burnout, overextending themselves to shield teams from stress, and balancing hands-on coding with high-level responsibilities. He explains:

A lot of the people that I work with directly are suffering from burnout. First time CTOs commonly miss out self-preservation. And usually that’s a combination of too much expectation of their own energy levels, their own abilities, backlogs…

And after overextending themselves, first-time CTOs often make another common mistake: chasing the newest technologies. While adopting the latest tools and frameworks can seem exciting, Skipper warns that it’s not always the best choice for fast-moving teams trying to scale.

“Using bleeding-edge tech can slow you down, make systems harder to maintain, and even complicate hiring because the talent pool for newer technologies might be limited,” he explains.

As a coach, Skipper says these are just some of the recurring challenges he sees among engineering leaders, alongside a range of other operational and people-related issues.

Engineering skills alone won’t make you a CTO

For aspiring engineering leaders, Skipper highlights that growing into a successful CTO requires more than technical excellence: commercial understanding, communication, coaching, and vision-setting are just as crucial:

The difference between a good engineering manager and a great CTO is understanding how technology drives business success, while still inspiring and guiding your teams.

But technical and business skills are only part of the picture. Motivation and team management are equally critical. Skipper stresses that not everyone is motivated by the same things, and leaders need to understand individual drivers:

Having a vision in the first place is very important. But when it comes to actually bringing individuals along on the journey, they all need to be worked with differently. You can’t just set it and expect everyone to be motivated.

He also warns against a common mistake among CTOs: trying to shield their teams from the challenges of a pivot or rapid change. While the instinct is understandable, it often backfires and drains the leader’s emotional energy. Instead, transparency and realistic communication are key:

Being transparent, being realistic, measuring your words, not being super negative about everything, but still being realistic, I think all these things are really important.

The need for a support network, not another tech stack

Skipper believes resilience and peer support are crucial for engineering leaders navigating the complexity of the CTO role. Sharing experiences and learning from others can help leaders realize they’re not alone when facing difficult decisions.

Looking ahead, however, he admits that the pace of technological change makes it hard to predict what the role will look like in the future.

Five years from now, I honestly have no idea what the role of a CTO will look like. The way we build software is already changing rapidly, especially with AI. But the fundamentals like setting a vision, communicating it clearly, and connecting technology with business outcomes, will always remain essential.

For Skipper, that uncertainty makes peer support crucial: it helps leaders adapt, learn, and navigate a fast-changing profession.

Ultimately, he believes the most important skill for CTOs is the ability to keep learning and tackle challenges without going it alone.

*Infobip, the global communications API leader that launched ShiftMag, was an Event Partner at CTO Craft 2026.

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For years, founders from Central and Eastern Europe (CEE) and Southeast Asia (SEA) have been culturally conditioned to believe that “going global” means going to the United States. But the conversation at this year’s Shift Conference in Kuala Lumpur painted a very different picture of how startups actually scale across continents.

With insights from Božidar Pavlović, Partner at AYMO Ventures, Lennise Ng, CEO & Co-Founder of Borong and Najla Zamri, Scouting Director at Antler, the panel made one thing clear: the U.S. is not startup’s Holy Grail, at least not anymore.

And for many founders, turning East may be the smarter, cheaper and more realistic move. But first things first, all three panelists insisted that startup should dominate their own market before attempting anything else.

Start in your own backyard

Although some startups have global ambitions from the very beginning, Pavlović, partner at the Croatian early-stage VC fund AYMO Ventures, suggested that founders should first validate their product in their home market before expanding abroad, if that market is mature enough. Only then it makes sense to raise money, expand regionally and later globally, he added.

Without strong local traction, scaling becomes guesswork, and that is expensive, said Zamri from Antler, a global early-stage VC firm and startup platform headquartered in Singapore. To reconcile local and global ambitions, she advised this:

Scaling too early is like shooting in the dark. Start local, but design globally from day one.

So different, yet so similar

On the panel, an unexpected conclusion emerged: despite being half a world apart, both regions CEE and SEA share a surprising similarity. They are emerging ecosystems, still building the infrastructure that Silicon Valley takes for granted, and they face many of the same struggles.

Pavlović said:

I’m surprised how similar the ecosystems are here and in Croatia. Most of the problems we have, you have too.

And some of them are building global products from tiny markets, fragmentation, funding gaps, talent shortages and over-romanticizing the U.S..

Fragmentation, rich cousin and networks of trust

He highlighted that Southeast Asia has an advantage when it comes to scaling, as just ten markets can represent tens of millions of customers. In contrast, twenty-seven markets across the EU may barely total half a million. He added that scaling across Europe is far more complex due to differences in regulation, currencies, and languages.

But Ng corrected him, pointing out that Southeast Asia’s large populations come with their own challenges, most notably fragmentation. As CEO of Borong, one of the fastest-growing companies in the Asia-Pacific region, she spoke from personal experience.

Borong is a Malaysian B2B marketplace platform that helps large enterprises, local retailers, and small businesses access suppliers, manage inventory, and place orders more efficiently. The platform addresses a major pain point in Southeast Asia, and those are traditional supply chains and wholesale distribution for small retailers which are often fragmented, informal, and inefficient.

Ng explained it succinctly:

Indonesia looks huge on paper, but it’s 17,000 islands with different networks of trust. If you don’t know how to build local relationships, it becomes brutally expensive.

She briefly analyzed the surrounding markets. According to her, Indonesia is chaotic, relationship-driven, and operationally heavy. She called Singapore the “rich fancy cousin you want to impress,” which acts more as a showroom for investors than a market to validate product-market fit. Malaysia, she concluded, provides a scalable home base, with pain points similar to those in Central and Eastern Europe.

The right partners

When it comes to scaling, founders must not only decide where to expand, but also with whom. “Expansion starts with the right partners,” Ng emphasized.

Investors also felt the need to clarify that they are partners, not puppet-masters, as they are sometimes perceived.

“Founders often fear that investors will dictate strategy, but after investing, we sit on the same side of the table,” said Pavlović and continued: “You share your network and insights, but you don’t push anything unless something is clearly not being delivered.”

Najla from Antler explained that their role is to help founders avoid mistakes, not to make decisions for them.

To the East, to the East…

They were all on the same page that it’s easier to succeed in the East, and that maybe now is the right time to stop treating U.S. as the Holy Grail.

Panelists agreed that founders from both CEE and SEA often chase American funding and prestige only to bleed dry on CAC, regulation, legal costs and a brutally competitive talent market.

The idea that the U.S. is the only way to “be global” is outdated. With APAC, MENA and even Eastern Europe opening up, global now means any large interconnected region especially those where talent is affordable, markets are underserved, English is widely spoken, investors are actively seeking emerging-market upside and last but not least, founders can build trust faster.

These regions give founders something the U.S. rarely does: breathing room.

There were several powerful thoughts I would like to share from that panel, and they all function great as a conclusion, so listen to them carefully:

• Start where you can win.
• Expand where you understand the culture.
• Choose markets that fit your product, not your ego.

The post CEE and SEA Startups Should Stop Chasing the U.S. and Start Looking East appeared first on ShiftMag.

Why on Earth would we make big career decisions based on anonymous tips?

The answer is – because it’s easy. Because it confirms what we already feel. Because they are pressing all the right buttons in our heads…

If I asked you: “Are you stupid?”, your answer, hopefully, would be – NO!

But what if you are? What if we all are? Not in the general sense but concerning how easily we get seduced by Jane and Joe Doe on the internet, especially on Reddit. In a sea of relatable posts and viral threads, even the smartest among us can get swept up.

And that raises a bigger question:

Are we really shaping our careers or is Reddit shaping them for us?

Something’s off

That was the wake-up call or rather, the wake-up question, behind the talk I Didn’t Want to Change My Job, but Reddit Made Me Do It, delivered by Mirna Horvat (co-founder and CEO, Leapwise and xolvi), at Shift Conference in Zadar.

In the last three years, she has read around a thousand CVs and interviewed roughly 200 people that wrote them. Something was off, she said.

Almost all of the CV’s had the same motivations for leaving their current jobs, they used the same phrases, had the same salary expectations, the same frustrations, the same anxieties.

Trying to figure out who was writing the script for what she was seeing in every job interview, she spent four months online, reading through Reddit, LinkedIn and various other online communities.

50 users shape communities of 20.000+ members

A handful of anonymous users, around fifty of them, are shaping community with 20.000 members or more. That might sound strange, but it’s actually not unusual.

There’s something called the 90-9-1 rule, or participation inequality, defined by Jakob Nielsen back in 2006. What does it mean? Basically, it says that 90% of people online are just consuming content, 9% occasionally comment, and only 1% actually create content.

On career-related subreddits, it’s even more extreme: 95% are silently reading, just 4% occasionally comment, and still, it’s the same 1% who are setting the tone – and the narrative – for everyone else.

But let’s return to our 50 “influencers”. Ninety-seven percent of them use so called throwaway accounts, but they also share some common characteristics:

They work remotely for well-known companies, but claim they only “sweat” for two hours a day. And they all seem to share the same attitude: No company is good. None.

That’s not all. They’re all earning €11,000 or more, at least, according to Croatian Reddit.

Science behind their impact

It’s clearly BS. So how come it works so well? It’s about being negative. Really, it is.

Mirna pointed out that negative comments trend 40% better and receive 40% more engagement. She even explained the science behind it.

A recent UCLA study scanned brains of people exposed to negative content. The amygdala, the brain’s fear center, activates in 180 ms, while the prefrontal cortex responds only after 500 ms. In other words, you feel fear before you can think logically about what you see.

Also, you have to take into consideration that the negative aspect of information, and the feelings you get from negative comments, stay in your mind five times stronger than the positive ones.

This isn’t about us as individuals, Mirna added, it’s about evolution. Our ancestors had to react quickly to negative circumstances in nature to survive. So our brains became wired to prioritize threats over feel-good moments.

She then brought up an MIT study which found that just being exposed to a negative comment makes you 25% more likely to think negatively about your own job, even if you were perfectly happy with it before.

People only share extreme emotions

Mirna shared another reason for the negativity bias. It’s the J-Curve effect from a study of 15 million Amazon reviews.

Most people leave either 1-star or 5-star ratings, while the indifferent majority stays silent. The same applies to how we talk about work, neutral days don’t make for engaging stories, so people share only strong positive or negative emotions.

This isn’t just Reddit. LinkedIn’s 2023 algorithm favors posts with emotional language like “I’m happy” or “I’m excited,” giving them three times more reach than neutral posts.

You probably shouldn’t blindly trust listen to online expert eather

We should stop listening to those flashy online voices who became “senior professionals” at 25 after just two years of work, living a dream life and getting paid astronomical amounts for their so-called brilliance.

A Harvard Business Review study even found that 73% of people giving online career advice have never actually hired anyone, and yet they’re out there, dishing out advice like experts.

But this is your career. You get to decide your next steps. As Mirna wisely reminded us, true growth takes time and patience, there are no shortcuts:

Every skill you have is built in layers. It takes up to 1.000 hours of dedicated work to acquire each layer, each step. And if you want to move forward, you cannot skip them. If you try to skip steps, you’ll have to make up for it later.

The very things you’re trying to avoid will come back to haunt you. Maybe not now, but eventually, in another job, another role, you’ll be forced to catch up.

Talk to real people when you want to change jobs

You really don’t have to be a victim of everything you read online. If you can’t stop yourself from diving into Reddit threads or doomscrolling posts about bad, bad IT companies, remember the 72-hour rule:

After an intense emotional experience, your feelings drop by 50% in the first 24 hours and usually fade by day three. Give yourself time before making big decisions.

The truth is, Reddit (or any online platform) can’t make you do anything.

“We allow ourselves to be influenced by the noise coming from outside,” says Mirna.

She concluded with a few grounded pieces of advice:

Talk to real people when you want to change jobs. Don’t just rely on the internet. No job is perfect. No company is perfect. Work with people you admire. When you stop admiring them, move on. Do something else. And stop doing anything that isn’t turning you into the person you want to become.

The post Who’s Really Shaping Our Careers – Us or Reddit? appeared first on ShiftMag.

AI, just like your dog, will eagerly try to please you but its assistance can sometimes be anything but helpful. AI, just like your dog, needs a firm master, warned principal developer advocate at Atlassian Dugald Morrow.

As one of the first speakers at the Shift conference in Kuala Lumpur, he delivered a clear message: use AI wisely, not blindly. 

AI needs guidance and supervision

Morrow illustrated how much AI needs guidance and supervision by dividing AI tasks into three levels.

• On the first level, AI performs well.

This is when it’s dealing with simple, well-defined tasks such as searching for information or processing raw data. 

• On the second level, AI can still perform, but often inconsistently.

This occurs when tasks grow more complex and require understanding the relationships between pieces of data – in other words, when analysis demands some degree of knowledge.

• On the third and most challenging level, AI fails.

This stage involves deep reasoning, judgment, and the creation of entirely new content – and here, the house of cards begins to fall apart. Yet our expectations for its ability to generate content remain enormous.

Despite appearing capable, AI lacks true wisdom

Although it appears capable, AI lacks the necessary wisdom, it works well within known data but struggles to go beyond it, Morrow explains. He even offers a diagnosis of AI: 

AI actually suffers from the Dunning-Kruger effect. It’s always willing to answer a question, but sometimes it’s not quite capable of doing so. 

He even goes a step further comparing AI to a dog: 

It’s got boundless enthusiasm, gleefully doing whatever you ask, though the results are often questionable or sometimes brilliant. We all know dogs have incredible senses – some can even sniff out cancer. Yet they can also do very silly things, just like AI can hallucinate or miss obvious facts.

What’s the cost of delegating everything to AI? 

This comparison works pretty well, although we could just as easily say that we’re the lazy dogs, demanding AI to do things instead of us.  

Morrow, who has a very interesting CV, having helped build submarine combat systems (for Collins-class submarines) and later an air traffic control system for Canada, in his exposé posed a question we’ve all asked ourselves: What’s the cost of delegating everything to AI? 

He offered a few answers to that, and they are not flattering.

While using AI in generating content you don’t become as familiar with that content as you would have if you generated the content manually. And you can’t evaluate if your content is of a high quality or not, he said. 

He warned the developers in the room that in their code may be hidden bugs or performance issues that they would have thought about if they had created it alone. 

Also he pointed out that overuse of AI erodes our skills to create: 

We all heard the practice makes perfect. You can’t learn tennis by just watching it or asking someone else to do it for you.  

96% of companies haven’t seen major AI gains yet

It’s tempting to use AI, but it’s really not necessary everywhere. How addicted we’ve become to LLMs and how often they fail to meet our expectations is shown in a recent Atlassian report. 

Although 62% of executives say that AI makes them more efficient, 37% say that it has wasted their teams’ time. Among the 12,000 people who participated in the research, 42% admitted that they trust AI too much. Perhaps the most striking finding from the report is that 96% of companies haven’t seen any dramatic improvements from AI yet. 

Therefore, Morrow proposes a managed AI approach, which means using artificial intelligence with the right level of human oversight, depending on the complexity and importance of the task.  

As we all know, not every task should be handed over entirely to AI.  

However, some rudimentary ones, like extending existing code patterns or fixing small bugs can be. In those cases, AI can safely work almost independently, since it’s simply repeating known patterns.  

He gave example of his own presentation which he made using Atlassian’s cloudapp development platform: 

The “spot image” didn’t display and showed a 404, so I used Robodev. It didn’t create a new image—just found the correct one and updated the code instantly. Since it was a simple, repetitive task, I trusted AI to handle it reliably.

When AI needs a firm master

But if AI has a task to build something entirely new, master has to be there. In this case Morrow advises breaking the work into smaller parts or defining a detailed specification before asking AI to generate code. That way, human stay in control of structure and intent. 

This is even more important when AI is asked to do complex algorithms.  

Developers should first define and document the algorithm in detail (inputs, outputs, logic) and then let AI complete it, suggests Morrow because that’s how AI has enough context to succeed. Otherwise, he claims, the good output is highly unlikely. 

He showed the code for his presentation and explained how he used AI to extract parts of a large SVG file for different slides, providing detailed descriptions and documentation for the extraction algorithm. 

To conclude, everything he said makes sense. The managed AI approach he advocates is essentially about balancing automation with human judgment. Rather than blindly delegating tasks to AI, developers should determine the appropriate level of guidance and oversight for each situation. As Morrow puts it: 

AI is like your dog. It’s a wonderful tool, but you need to manage it.

The post Lessons from Atlassian: AI is Like Your Dog, It Needs a Firm Master appeared first on ShiftMag.

AI is sexy. But those who are building AI in real, production-grade environments know the truth: the real challenge lies in making something that actually works at scale.

During a recent panel discussion about technical debt and AI transformation at the Infobip Shift Miami conference, where engineers and product leaders deep in the AI trenches gathered, one key piece of advice stood out: keep it simple.

Strip the AI agent down to its core and build a solid foundation for a multi-agent architecture.

Why? Today’s agents are buggy, unreliable, and still learning how to work together. The goal is to train them, refine them, and ultimately build a well-orchestrated agent-to-agent system that can deliver real value.

Gigantic Monolithic Agents are a no-no!

Prabu Ramaraj, Director of Engineering for Generative AI and Automation at AutoZone, is very pragmatic:

„Recently, everybody wants to build agents, but you don’t need to use language models to do all the business for you. If you have to go with the agents, I recommend going with a multi-agent architecture. You don’t want to build a monolithic application, right? The same goes for the agents.

Don’t build one gigantic monolithic agent; build multiple agents that have a specific goal that you can test and form!

“I completely agree!”, instantly added Nick Kljajić, Co-Founder and CEO of AskHandle, an AI-driven support platform specializing in generative AI and natural language processing (NLP):

“Start small, see how it performs. Test it, iterate, once the performance is optimal or it reaches the level where you’re happy with it, build on top of it. And just measure, measure and measure, he insisted.

Making something complex just because it sounds sexy in the AI world doesn’t necessarily add value; it often just adds fragility.

Nick points out that developers should go back to basics:

“Before doing anything complex, make sure your AI system can accurately retrieve the right answers from your knowledge base or documents. And then build and connect to other agents because in multi-agent systems, more things can go wrong.”

Human-in-the-Loop

To build efficient agents, parts of a multi-agent system (that is more effective and scalable than one super-agent!), one must measure and iterate relentlessly. Luckily, agents with a scarce set of skills enable better testing.

Panelists agreed that before scaling up, one should track “meaningful metrics” like AI performance – relevance, confidence, grounding, and business performance – resolution time, containment rate, conversion, and fallback frequency. However, the basic one is whether the customers got the answer they were looking for.

Ervin Jagatić, Product Director at Infobip, explained how, with the “human-in-the-loop, AI” agents can become more accurate and reliable:

“Humans assess the responses from the assistant, par example, are they grounded in the documentation or are they hallucinating. And then reiterate so that humans can go through these analytics, mark the good and the bad answers, and make the system better, iteratively gathering the data and improving the performance of the AI assistant.“

MCP to Reduce Chaos

In a discussion centered around AI agents and system design, standardization was emphasized as a key enabler of scalability and simplicity.

According to the experts, when each team or agent follows its own approach, complexity grows rapidly, especially for engineers. They concluded that adopting standards like MCP helps reduce chaos by defining consistent input/output schemas and improving interoperability across agents.

They declared that standards and shared protocols are especially critical in this early stage to building stable, scalable architectures.

Ervin shared that for companies like Infobip, where APIs are the backbone of communication infrastructure, the goal is to open that stack to AI agents as well, allowing them to use the same tools and communication layers that human users and systems do.

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