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My career advice for 2027: Audit your career. A native worker often thinks, “This is how things are done here. That’s just the way it is. That's my niche.” An outsider asks, “Why are we doing it this way, or why are we even doing this? What's the point when the market has moved on"? You respond to the labour market that exists, not the one you prefer. If your existing skills are no longer serving you, let them go. It's not a sunk cost. They served their purpose, and your market has moved on. When a skill no longer clears the market at the rate you need (whether because of automation, changed buyer preferences, or simply oversupply), the rational move is usually to stop treating it as the centre of your offering. Continuing to refine it, market it, or organise your career around it simply because you already invested years/decades is how people end up becoming obsolete. Develop new skills for the new market. Growth and innovation require stepping outside the comfort of majority thinking. The outsider mindset allows you to see problems and opportunities more clearly and earlier than your competition. Your existing career may have become your comfort zone, and it could soon become your death zone if you don't adapt fast. Audit your current skill stack against actual demand signals (job postings, contractor rates, internal project success, customer willingness to pay) rather than against what felt valuable 2 or 3 years ago. Don’t play by the same rules as everyone else. Question them. Redefine them. Create your own path. That mindset can open doors, sometimes even to working with world-class clients like MIT, from an office halfway across the world.

People in tech role who are currently being replaced by AI today are going to be the same people who replace others with AI once they build there career in AI. This answers your question "why you are being a part of the system which results in mass layoff for others". There is no right or wrong here. Its just survival at this point. You respond to the labor market that exists, not the one you prefer. If you don't do it, someone else will. Survival of the fittest.

Pick your role now, as it will get far worse in 2027. Most companies of the future will revolve around only two core roles: Agent Developers and Agent Supervisors. Over time, every department will likely operate within this structure. Agent Supervisors - senior subject-matter experts responsible for overseeing the performance, reliability, and strategic alignment of AI agents. Agent Developers - build, maintain, and optimise these agents, translating business logic and domain knowledge into operational code, APIs, and workflows.

Learn to manually perform a task first and do it dozen of times before thinking of automating it. So when anything breaks during automation (and it will), you know exactly what broke and how to fix it. Without manual reps, your automation workflow is unlikely to survive production load.

AI is not a technology. It’s infrastructure. You can’t opt out of infrastructure. It's deeply embedded in everything you use. Using failed adoption stories and memes as a crutch to highlight AI's current shortcomings won't halt or reverse AI adoption or change decision-makers' minds. “Billions” of dollars have already been invested. Your memes, complaint posts, and boycotts stand no chance. Adapt or see yourself becoming obsolete.

Keep all your workflow automation in n8n and forget about Retell Workflows. Retell Workflows lets your agent run a few actions at the start or end of a call without an external tool like n8n. For example, before the call, it can fetch the caller’s lead, booking or account details. After the call, it can update your CRM, create a ticket, post to Slack or send an SMS. It has no other triggers. n8n goes much further. It can be triggered by almost anything: a schedule, a webhook, a form submission, a new CRM record, an incoming email or SMS, or a Retell call. It has hundreds of ready-made nodes with authentication handled for you. It can branch, map and reshape one payload for five systems, with retries and error paths on each branch. It gives you error workflows and replay. And the same workflow can serve voice, chat, forms and email. Retell gives you eight native integrations and a JavaScript sandbox. In short, Retell Workflows is nowhere near a substitute for n8n. So if you still need n8n, why split your logic across Retell and n8n? Splitting it means no single place shows how the system works. One side gets updated, and the other doesn’t. Both may write to your CRM. And when something fails, you have to debug two systems to find out which one did it. Keep all your workflow automation in n8n and forget about Retell Workflows.

Review of Retell Memory. Should you be using it? Retell AI recently introduced a new feature called ‘Memory’. It lets your voice agent remember a caller between conversations, so returning callers can be identified and previous context can be retrieved. It all sounds good in theory, but it is not something you should be using or relying upon. I will put it in the same category as pointless features like Retell Workflow. Here is what happens when you use and rely on Retell Memory: #1 When memory lives inside Retell, it is siloed. There is no built-in sync to your CRM, so your SMS tools, email and staff never see it unless you build the export yourself. Scenario: A caller tells the voice agent she has a dog, and that the technician should use the side gate. The technician arrives, checks the job in your field-service app, sees no note, and walks straight into the back garden through the front. Gets brutally attacked by the dog and ends up in hospital. Your client ends up paying the bill. It's an extreme example, but you get the point: what happens when you don't share context across your tech stack. #2 Retell Memory only knows what happened on Retell calls. If a technician closes the job or the invoice gets paid in your CRM, memory will not know. It can confidently carry forward a stale open item. Scenario: Sarah’s furnace was repaired on Monday, and the job was closed in the CRM. On Wednesday, she calls about a new thermostat. The agent opens with “Are you still waiting on that furnace repair?” #3 Identity rests on the phone number alone. On a shared household line or a reassigned number, the agent gets the wrong person and the wrong history. Scenario: Harry booked an AC service from the home landline. Diana calls from the same number to cancel a different job. The agent greets her as Harry and asks about his AC appointment. #4 One prompt covers the whole workspace. This is the most deadly downside. You cannot tailor what a sales agent remembers versus a support agent without splitting into separate workspaces. Scenario: Your sales agent needs to remember budget, timeline, and the competitor quote the lead mentioned. Your support agent needs model numbers, warranty dates and the last fault reported. Both write to the same 2,000-character memory under one prompt. Tune the prompt for sales, and the support agent forgets the fault history. Tune it for support, and the sales agent forgets the budget. Write a prompt that covers both, and each update squeezes both sets of details into 2,000 characters, so you lose half of each. You are better off keeping memory inside your CRM and treating the CRM as the single source of truth. What happens if you run both Retell Memory and your CRM? You get two sources of truth that can drift apart over time, and the agent cannot tell which one to trust. It is worse than relying on your Retell Memory alone or CRM alone. When we have to use a CRM anyway and can't really skip it, what's the point of using Retell Memory? The CRM can do everything Retell Memory does, and it does it better. You have full control over the context and can share it across your tech stack. The biggest architectural downside is maintaining a second customer record. If staff update the CRM while Retell maintains a separate brief, you need rules for which record takes precedence and how corrections reach both. What if you don't use a CRM? If you don't use a system of records (CRM or CRM like tool) you likely don't have a use case that requires retaining caller ID and previous context. I have yet to encounter a business that uses no system of record at all. Even if they aren't using a conventional CRM, they're likely using tools similar to a CRM, like field service tools (Service Titan, Jobber) or good old Google Sheets or Excel. So the real fix is still a CRM or CRM like feature. Retell could save itself a lot of work or rework by testing new features with developers who run voice agents in production before shipping them to everyone.

All these people with no coding background learning Claude code, but who is hiring them? If the skill doesn't translate to $ its just a hobby. Monetising AI is a totally different game. Companies pay for outcomes they can "trust" in production, not speed of generation or 'glue work'.

A bad workman always blames his tools. It’s not the AI, it’s the person using it.

Pro Tip: You can purchase unused API credits from businesses at a 50–60% discount and slash your API call costs in half. Many businesses purchase API credits in bulk or receive promotional credits from AI providers such as OpenAI and Anthropic. Some never use their full allocation. Instead of letting those credits go to waste, they can monetise unused API capacity through third-party marketplaces. Platforms such as Surplus Intelligence, Attium and LLM Mart advertise discounted access to AI models through these arrangements. In some cases, switching may be as simple as changing your API endpoint and API key. The catch is that discounted API access doesn't necessarily mean identical reliability, latency or data protection. So verify the provider's terms, test the service and compare your actual costs before switching. If you're running AI agents at scale, this is an opportunity worth exploring. Even a modest reduction in API costs can add up quickly.

I don’t know who would want to hear this but you can’t stop. Setting arbitrary goals like 5 sets of 8 reps each and taking designated breaks between sets won’t take you anywhere. Your body will soon adapt and after that no more growth for you. At that point you are just wasting your time in the gym. You don’t count reps/sets. You don’t take breaks because you have to. You keep doing reps after reps unless you physically can’t do them any more. Your hands start becoming numb. You start losing grip. You just can’t take it anymore. That’s the sign you need to stop and take a break. Nobody told me this. I figured it out on my own. But I know many bodybuilders take this approach once they plateau. You can’t stop. Also, don’t feel ashamed of working out with light weights, the weights you can comfortable handle. I see too many guys lifting heavy weights they can barely move with correct form. Without muscle contraction, correct form and tons of sets they are not going to build any muscles. Just wasting there time in my opinion. Remember, you are in competition with yourself. So don’t pay attention to what other people are doing or how much they are lifting. And yes, people are still going to judge you. It’s just human nature. But the same people won’t be around with ‘get well soon’ cards when you get hurt lifting heavy, trying to impress them. You workout as hard as you can. Stop counting reps and taking random breaks just because you have to. Only stop when it becomes physically impossible to continue. Thats how you push yourself in every session.

If you find yourself skimming or summarising every long piece of text, you are also losing your ability to read each LLM output with close attention to detail, correctness, completeness and consistency with earlier context, prior assumptions, decisions reached and corrections applied. You need to make sure that the LLM retains context and all prior constraints, assumptions, decisions reached and corrections applied throughout the conversation until a project/task is complete. That's a lot of cognitive load. But if you lose your ability to read a long piece of text over and over again, you become sloppy, overlook LLM failure modes, burn more tokens on corrections and produce less reliable AI-generated work. So don’t lose your reading comprehension skills. Read even more and read carefully.

In this article, I catalogue several LLM and user failure modes I have encountered over the years across tasks such as vibe coding, building websites, automating workflows, developing RAG systems, and creating AI agents. My aim is to keep these limitations in view, recognise recurring failure patterns and reduce the chances of being caught off guard by an LLM’s output. The end goal is to produce reliable automation workflows and other AI-generated outputs. Knowing about various failure modes can help you understand why agents behave the way they do, why they sometimes seem to lie, ignore instructions, or take actions they aren't authorised to take, and what you can do about it. In my opinion, this is one of the best pieces of knowledge I have ever acquired in the field of AI automation. If you can stomach the article, technical detail and dry subject matter long enough to read it from start to finish, you will be better equipped to use LLMs for automation work than 99% of AI users and developers. optimizesmart.com/blog/four-…

Devon Larratt (multiple times arm wrestling world champion) vs poor me 😃 120 kg (265 lbs) vs 70 kg (154 lbs). Professional vs Amateur. But we both love grip strength and are forearm crazy 🤪 You are only as strong as your grip. So train it. 💪

As an AI user/developer, be aware of: >> False completion: “I booked your appointment,” when the booking failed. >> Partial completion presented as full completion: “I updated all the files,” when only some were updated. >> Unperformed verification: “I checked the source document,” when it did not read it. >> Attempt presented as success: “I sent the email,” when it submitted a request but received no confirmation. >> Omitted actions: Failing to report an additional action it took outside the authorised scope. These are all examples of the LLM failure mode: action-reporting failure. This failure model occurs when an LLM’s description of what it did does not match what actually happened. For example: An HVAC answering agent collects the caller’s details and calls a booking tool. The tool got the requested date and time right but failed to book the meeting and returned an error. The agent tells the caller: “Your technician visit is booked for tomorrow morning.” The caller now believes an appointment exists even when no appointment is actually booked. This issue occurs when you rely only on LLM training data to define planned, attempted, confirmed, failed and uncertain actions instead of explicitly defining them in operational terms. Such issues are often intermittent. They may filter out in initial testing but can cause issues later on, unannounced esp. on a large scale.

They: "How do I make my career AI-proof?" Me: Wrong question. That's the saddest career strategy imaginable. You should be asking, "How do I build a career in AI?" Dodge AI long enough and see yourself becoming obsolete. Chasing an "AI-proof" career is a pipe dream. You treat AI as "not real work" → The market disagrees. → You are replaced by a person who uses AI, or worse, you are replaced by an agent. Dodge AI long enough, and obsolescence isn't a risk; it's the default outcome. The AI wave isn't optional esp. in the tech industry. It's infrastructure, like electricity or the internet. You don't "opt out" of infrastructure and thrive. You learn to build on top of it. Embrace AI aggressively now, and in a few years you'll look back at "AI-proof" as the saddest career strategy imaginable.

I don't have any need to learn more about digital analytics, GA4, or BigQuery, which helped me move on to Voice AI. If you are "stuck" learning the same tech/tool stack year after year, you risk lagging behind in your career or, worse, becoming obsolete. After a certain point, you only get better at finding and fixing edge cases, while the market moves on to something completely different. At that point, you can be highly proficient and still less employable or less compensated than someone with shallower but more relevant skills. People who pivot after extracting high-leverage insights from the previous domain usually land better than those who cling to their existing skill sets until they clearly become obsolete.