Category: Career

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Artificial Intelligence Business Career Work

The key to thriving in the AI age is beating the bottlenecks

One of Nate B. Jones’ recent videos has the title Why the Smartest AI Bet Right Now Has Nothing to Do With AI (It’s Not What You Think). While the title is technically correct, I think it should be changed to In the Age of AI, You Have to Beat the Bottlenecks.

Bottleneck: a definition

Many Global Nerdy readers aren’t native English speakers, so here’s a definition of “bottleneck”:

A bottleneck is a specific point where a process slows down or stops because there is too much work and not enough capacity to handle it. It is the one thing that limits the speed of everything else.

Imagine a literal bottle of water.

  • The body of the bottle is wide and holds a lot of water.

  • The neck (the top part) is very narrow.

  • When you try to pour the water out quickly, it cannot all come out at once. It has to wait to pass through the narrow neck.

In business or technology, the “bottleneck” is that narrow neck. No matter how fast you work elsewhere, everything must wait for this one slow part.

Elon is often wrong, but you can learn from his wrongness

My personal rule is that when Elon Musk says something, and especially when it’s about AI, turn it at least 90 degrees. At the most recent World Economic Forum gathering in Davos, he talked a great “abundance” game, with sci-fi claims that AI would create unlimited economic expansion and plenitude for all:

Nate Jones watched the talk with Musk, but came to the conclusion that Musk’s take is the wrong frame for the immediate future. The current AI era will be one of bottlenecks, not abundance. I agree, as I’ve come to that conclusion about any grandiose statement that Musk makes; after all, he is Mr. “we’ll have colonies on Mars real soon now.”

Here are my notes from Jones’ video…

Notes

Instead of abundance, Nate suggests that what we are entering is a “bottleneck economy.” While AI capability is growing, the actual value it produces won’t automatically flow everywhere and benefit everyone. Instead, it will concentrate around specific areas based on AI’s constraints and limitations [00:00].

Research from Cognizant claims AI could unlock $4.5 trillion in U.S. labor productivity (and yes, you need to take that figure with a huge grain of salt), and it comes with a massive caveat: businesses must implement AI effectively. Currently, there’s a wide gap between AI models and the hard work of integrating them into business workflows. This “value gap” means that the trillion-dollar impact won’t materialize until organizations figure out how to bridge the distance between models can do in general and what they can specifically do for a company’s operations [01:01].

Physical infrastructure is the first bottleneck. AI capability is increasingly constrained by things it needs from the physical world, specifically land, power, and skilled trade workers. Building the data centers required to train and run models takes years, and not just for the building process, but also permitting and connections to the power grid. This creates a wedge between the speed of software development and building infrastructure  [03:56].

Beyond just buildings and power, the hardware supply chain is the second bottleneck. Access to compute, high-bandwidth memory, and advanced chip manufacturing (controlled largely by TSMC) determines who gets a seat at the table. Companies that understand this are securing resources years in advance and treating regions with stable power and friendly permitting as strategic assets. This creates a market where value is captured by those navigating physical constraints in addition to building better algorithms [06:02].

The third bottleneck is one you might not have thought of: the cost of trust. As the cost of generating content collapses to near zero, the cost of trust is skyrocketing. Jone highlights what he calls a “trust deficit,” calling it a major coordination bottleneck. When any content can be fabricated, the ability to verify and authenticate information becomes expensive and crucial. Value will shift to institutions, platforms, or individuals who can mediate trust and provide a reliable signal in world rapidly filling with synthetic media slop [07:36].

For organizations, there’s the bottleneck of applying general AI to specific contexts. A general AI model won’t know a company’s private code base, board politics, or competitive dynamics. The bridge between “AI can do this” and “AI does this usefully here” requires tacit knowledge; that is, the practices and relationships that aren’t written down but live in the heads of the company’s employees. Companies that solve this integration problem will unlock productivity, while those that don’t will spend lots of money on tools they never use [09:55].

The fifth bottleneck is another one you might not have though of: the increasing value of taste. For individuals,  and especially for those in tech, the bottlenecks are shifting from acquiring skill to getting good at making judgment calls. AI is commoditizing hard skills like programming (it’s cutting down the time to proficiency from years to months), the really valuable skills are going to be taste and curation. The ability to distinguish between AI output that’s “good enough” versus AI output that’s extraordinary will become the differentiator. Developing taste takes experience, time, and observation. This is going to create a dangerous race for early-career professionals, whose entry-level work is being devalued [14:52].

The combination of problem-finding and execution are the sixth bottleneck. When problem-solving becomes automated, finding the problem and executing on the solution become the new moats. The market will reward those who can frame the right questions and navigate the ambiguity of implementing appropriate solutions. Jones emphasizes that while AI can generate a strategy or a plan, it can’t execute the “grinding work” of follow-through, holding people accountable, and navigating organizational politics. Success depends on identifying these new personal bottlenecks rather than optimizing for old skills that AI is turning into commodities [16:50].

Tips for techies and developers to beat the bottlenecks

  • Cultivate a sense for taste in addition to a skill for syntax. As coding moves from purely “grind” to at least partially “vibe” (see my vibe code vs. grind code post), your value shifts from writing code to reviewing AI-generated code. You need to refine your sense of what makes code good to differentiate yourself from the flood of AI output, which tends towards the average. [15:06]
  • Specialize! To beat the “good enough” standard of AI, pick a niche, and specialize in it. The window for being a generalist is closing, and extraordinary depth allows you to spot quality that AI (which once again, tends towards the average) misses. [16:16]
  • Pivot to problem finding. AI makes a lot of problem solving cheap, which makes problem finding the rare and precious thing. Stop defining yourself solely as a problem solver. Focus on defining the right problems to solve, framing the architecture, and determining direction. This management-level skill is harder for AI to replicate than execution. [16:50]
  • Value tacit knowledge and context. Tacit knowledge is the “soft” knowledge of how an organization works, and it’s almost never documented (at least directly), but lives in the heads of the people working there. Knowing why a legacy codebase exists or understanding specific stakeholder needs is a “context moat” that general AI models can’t easily infer. [17:36]
  • Focus on execution and follow-through. AI can generate the plan/code, but it can’t navigate the friction of deployment. The “grinding work” of implementation, such as convincing teams, fixing integration bugs, and finalizing products, is where the real value now lies. [18:47]
  • Build your tolerance for ambiguity. This has always been good for real life, but now it’s also good for tech work, which used to live in rigid, well-defined, unambiguous spaces… but not anymore! The tech landscape is shifting rapidly, and the ability to remain functional and productive while “metabolizing change” and dealing with uncertainty is a critical soft skill that separates leaders from people who freeze when things become ambiguous. [20:01]
  • Audit your personal bottlenecks: Be honest about what is actually constraining your career right now. It might not be learning a new framework (the old bottleneck). Instead, it might be your ability to integrate AI tools into your workflow or your ability to communicate complex ideas. Find those bottlenecks and come up with strategies to overcome them! [21:25]
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Artificial Intelligence Career

Notes from Nate B. Jones’ video, “The People Getting Promoted All Have This One Thing in Common (AI Is Supercharging this Mindset)”

I’ve often been asked “How do you keep up with what’s going on in the AI world?”

One of my answers is that I watch Nate B. Jones’ YouTube channel almost daily. He cranks them out at a rate that I envy, and they’re full of valuable information, interesting ideas, and perspectives I might not otherwise consider.

If you haven’t seen this channel before, he recently published a great “starter video” titled The People Getting Promoted All Have This One Thing in Common (AI Is Supercharging this Mindset). It covers a topic that should be interesting to a lot of you: What to do when the traditional career ladder is getting dismantled, and yes, the answer involves AI.

Here’s the video, and below it are my notes. Enjoy!

Notes

Kiss the traditional career ladder goodbye

The conventional path for white-collar career advancement that’s been around since the end of World War II is being dismantled. It used to be that you’d land an entry-level role, learn through work that starts as simple tasks but gets more complex as you go, and gradually climb the corporate ladder. That’s not the case anymore. If you’ve been working for five or more years, you’ve seen it; if you’re newer to the working world, you might have lived it.

Jones opens the video with these worrying stats:

  • Entry-level hiring at major tech companies has dropped by over 50% since 2019
  • Job postings across the US economy have declined by 29%
  • The unemployment rate for recent college grads is now greater than the general unemployment rate

This isn’t a temporary freeze but a structural shift where the “training rung” of the ladder is being removed. Those repetitive, easier tasks that you assign to juniors (summarizing meetings, cleaning data, drafting low-stakes documents) are exactly what generative AI now handles, and it’s getting better at it all the time.

As a result, the “ladder” is being disassembled while people are still trying to stand on it. Entry-level roles now require experience that entry-level jobs no longer provide because AI has cannibalized the work that used to serve as the learning ground [00:55]. Jones argues that in a world where the passive route of “doing your time”to get promoted is vanishing, the only viable strategy left for career survival and growth is cultivating extreme high agency.

High agency and locus of control

High agency sounds like a feeling of confidence, self-assuredness, or empowerment. It’s best understood through the theory of Locus of Control, which psychologist Julian Rotter developed in the 1950s.

Jones proposes a mental exercise [1:55]: draw a circle and list all major life elements (promotions, skills, family, economy). For low-agency individuals, significant factors like promotions or learning requirements fall outside the circle, perceived as things determined by managers or the market. For high-agency individuals, absolutely everything falls inside the circle.

The high agency mindset dictates that while you cannot control external events, you can control the way you respond, and by extension, your trajectory (sounds like the modern stoicism that’s popular in Silicon Valley circles, as well as at my former company Auth0).

When a high-agency person encounters a barrier that seems outside their control, they reframe it with a four-word Gen Z expression: “That’s a skill issue” [03:23]. Whether it’s lacking a technical skill or not knowing how to navigate office politics, they view the obstacle not as an immovable wall, but as a gap in their own abilities that can be bridged through learning and adaptation.

High agency vs. systemic barriers

Jones took the time to address the valid criticism that this mindset ignores systemic unfairness or is that “bootstrap mentality” that ignores structural problems. He argued that high agency is actually most critical for those with the least privilege. He observes that people from disadvantaged backgrounds often display higher agency because they lack the safety nets that more advantaged people have, which often leads them to be more passive  [4:48]. When failure isn’t an option, you put in the effort not to fail.

While no one literally controls whether they get laid off, the high-agency mindset focuses on controlling the response: where to direct energy, what to learn next, and how to pivot.

However, Jones warns that an internal locus of control can be taken too far, leading to the tendency to blame yourself for everything that goes wrong. The goal isn’t to beat yourself up for every setback. Instead, it’s to channel that internal orientation into a “challenge” mindset. Instead of thinking “I failed because I’m inadequate,” the high-agency approach is “I haven’t found the right angle of attack yet, but I can figure it out” [5:41]. This distinction, which looks a lot like “growth mindset,” turns potential anxiety into a strategic focus on solving problems.

AI as the “jet engine” for agency

Jones’ thesis is that AI is the “greatest equalizer for agency that has ever existed” because it acts as a force multiplier for anyone willing to act [5:59]. Barriers that previously required years of expensive education or access to elite networks, such as coding a website, analyzing complex data, or launching a marketing campaign, can now be overcome by a single individual with a laptop and determination. AI doesn’t care about your pedigree; it simply responds to questions and executes commands.

This technological shift allows high-agency individuals to bypass traditional gatekeepers. Jones shares examples of people (including the creator of Base44) moving from dead-end situations to running scaling businesses not because of luck, but because they used AI to relentlessly patch their skill gaps [6:12]. In this new era, if you don’t know a programming language or a business concept, AI allows you to learn and implement it simultaneously, effectively turning “skill issues” into temporary speed bumps rather than dead ends.

Speed becomes what sets you apart

A critical consequence of the AI era is the acceleration of the gap between high and low-agency individuals. Jones notes that while this difference used to play out over decades, AI now makes the separation visible in months [7:33]. High-agency people leveraging AI can accomplish 10 to 100 times more than their passive counterparts, compressing career trajectories that used to take twenty years into a fraction of the time (supposedly; consider the myth of the 10x developer). Conversely, career stagnation that once took a decade to notice (you sometimes see this in “company lifers”) now becomes apparent almost immediately.

This acceleration means that waiting for permission or the next rung of the ladder to appear is a strategy for failure. The people currently being tapped for leadership are those who combine high agency with “AI-native” thinking, leading them to redefine roles instead of just filling them [8:11]. In an organizational structure that is inherently malleable and constantly disrupted by scaling intelligence, titles don’t matter. Instead, what really matters is generating value and outcomes.

The “Say/Do Ratio” and execution

Jones talks about what he calls the “Say/Do Ratio” as a measure of high agency. It’s the gap between saying you will do something and actually doing it.

Most people have a poor ratio, letting weeks or months pass between intention (“I’m going to learn this skill!” or “I’m going to hit the gym daily!”) and action. They’re either hit by “analysis paralysis” or waiting for perfection [12:37]. High-agency individuals shrink the distance between “say” and “do.” They start immediately, even when they feel unprepared or uncomfortable.

AI serves as a powerful accelerator for improving this ratio by helping users “ship halfway-done” work (think “Minimum Viable Product”) or get past the “blank page” problem instantly.

Jones cites Kobe Bryant as a prime example of this mindset. Bryant viewed nervousness not as an emotion to be managed, but as an information signal that he hadn’t prepared enough, which is a variable that he could control [11:38]. Similarly, in the AI age, preparation and execution are more accessible than ever, allowing those with high agency to move from idea to prototype without getting stuck in the “planning” phase.

Solo founders and lean unicorns

The combination of high agency and AI is reshaping the business landscape, and the surge in solo founders and “lean” billion-dollar companies. Jones points out that the share of startups with solo founders has nearly doubled since 2015, and we’re approaching the era of the one-person billion-dollar company [15:13]. He cites the example of solo founder Maor Shlomo, who built Base44 from a side project to an $80 million exit in six months without a full-time team or venture capital, simply by pushing code to production 13 times a day [16:20].

This trend proves that AI allows individuals to operate with the output capacity of entire teams. Founders and operators can now “speedrun” through obstacles that used to require hiring specialists, whether it’s understanding server-side architecture or generating marketing materials. The constraint on building a massive business is no longer headcount or capital, but the agency of the founder to utilize AI to extend their own capabilities and solve problems [16:47].

Don’t wait; generate!

In the end, the high-agency mindset is grounded in an obsession with pushing value into the world. Jones describes this as a belief that the world is “bendable”: if you generate enough value and contribute enough, the world will eventually respond in your favor [18:15].

This orientation prioritizes contribution over extraction; instead of asking “What can I get?”, high-agency people ask “What can I create?”. Simply put, you get what you give.

This perspective shifts the focus from waiting for opportunities to making them. If you approach AI as a tool to expand your locus of control, you can systematically knock down barriers between you and your goals. Jones concludes that the future belongs to those who don’t wait for the old structures to return but instead use their agency to build, ship, and learn now, viewing the current disruption not as a threat, but as an unprecedented opportunity for growth [21:44].

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Career Systems Work

Lessons you can take away from my job search experience

Red rows mean I’m out of the running, orange means that I’ve applied but nobody’s gotten back to me other than with an automated email, yellow means I had an initial screener interview but then things stopped because of the holidays, and green means active and in progress.

Pictured above is a version of my job search spreadsheet with a couple of columns hidden and some details redacted. But despite the missing info, it still has useful data points for you, namely:

  1. You have better odds with a referral. You probably know this, but it’s worth repeating. Every referral so far has at least resulted in an initial “HR screener” interview, and two of them have resulted in final interviews (which I’ve denoted in the spreadsheet using the videogame term BOSS FIGHT!).
  2. To stand out to recruiters, you need to be a certified yapper on LinkedIn! (This is a sticker on my Windows laptop.)

    Being noisy on LinkedIn pays off. Did you know there are Recruiter versions of LinkedIn? There’s Recruiter Lite, which can cost up to $2,000 annually, and then there’s Corporate version, which is said to sell for about $10,000 to $12,000 per year per seat.

    Recruiters get paid when they match people looking for jobs with employers looking to fill positions, so they’re willing to shell out lots of money for a specialized version of LinkedIn, provided that they get king-sized multiples of that money by finding the right match for their clients. Think of LinkedIn as a search engine for job candidates.

    In the spreadsheet pictured above, note than 6 out of 30 opportunities — that’s one in five — is marked in the How it started column as Recruiter found me. They found me on LinkedIn because I post and comment regularly on AI, Python, and technology in general, which in turn generates “signal” on LinkedIn for those topics that clearly points to me. Long story short: You want to get found by recruiters on LinkedIn? You have to post on topics relevant to the job you’re looking for on LinkedIn.

  3. You can’t see it in the spreadsheet, but I totally broke the “No more than 2 pages” resume rule. The resumes I submitted to all of the prospects in the spreadsheet — including those in which I’m at the BOSS FIGHT! stage — were 5 pages long.The trick is that my resumes, while long, answer the question “What does this candidate bring to the table?” and that’s really the question recruiters and hiring managers want answered. I customize each resume for each prospect with the assistance of Claude, and it’s worked out quite well for me.I’m betting that you’re reeeeally curious right now, so here’s one of the resumes for one of the BOSS FIGHT! prospects. I hope you find it useful!

Bonus 4th observation

See the row above? That’s an opportunity where I’m going to do a final interview that I applied to, cold, with just a resume (and yes, it was a five-pager) and a cover letter.

I didn’t have a referral, and with this particular one, I applied via LinkedIn and not via the company site because that was the only place to do it. And yet I got that initial interview, which led to all the follow-up interviews. According to the recruiter, it was a combination of the resume, cover letter, and LinkedIn presence.

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Artificial Intelligence Career Conferences Current Events What I’m Up To

The “Careers in Tech” panel at TechX Florida / Reasons to be optimistic 2025

The Careers in Tech panel

On Saturday, I had the honor of speaking on the Careers in Tech panel at TechX Florida, which was organized by USF’s student branch of the IEEE Computer Society.

On the panel with me were:

We enjoyed speaking to a packed room…

…and I enjoyed performing the “official unofficial song of artificial intelligence” at the end of the panel:

Reasons to be optimistic 2025

During the panel, a professor in the audience asked an important question on behalf of the students there: In the current tech industry environment, what are the prospects for young technologists about to enter the market?

I was prepared for this kind of question and answered that technological golden ages often come at the same time as global crises. I cited the examples from this book…

…Thank You for Being Late, by Thomas Friedman, who proposed that 2007 was “one of the single greatest technological inflection points since Gutenberg…and we all completely missed it.”

The reason many people didn’t notice the technological inflection point is because it was eclipsed by the 2008 financial crises.

During the dark early days of the COVID-19 pandemic and shutdown, the people from Techstars asked me if I could write something uplifting for the startupdigest newsletter. I wrote an article called Reasons for startups to be optimistic, where I cited Friedman’s theory and put together a table of big tech breakthroughs that happened between 2006 and 2008.

In answering the professor’s question, I went through the list, reciting each breakthrough. The professor smiled and replied “that’s a long list.”

If you need a ray of hope, I’ve reproduced the list of interesting and impactful tech things that came about between 2006 and 2008 below. Check it out, and keep in mind that we’re currently in a similar time of tech breakthroughs that are being eclipsed by crises around the world.

The leap Notes
Airbnb

In October 2007, as a way to offset the high cost of rent in San Francisco, roommates Brian Chesky and Joe Gebbia came up with the idea of putting an air mattress in their living room and turning it into a bed and breakfast. They called their venture AirBedandBreakfast.com, which later got shortened to its current name.

This marks the start of the modern web- and app-driven gig economy.
Android

The first version of Android as we know it was announced on September 23, 2008 on the HTC Dream (also sold as the T-Mobile G1).

Originally started in 2003 and bought by Google in 2005, Android was at first a mobile operating system in the same spirit as Symbian or more importantly, Windows Mobile — Google was worried about competition from Microsoft. The original spec was for a more BlackBerry-like device with a keyboard, and did not account for a touchscreen. This all changed after the iPhone keynote.
App Store

Apple’s App Store launched on July 10, 2008 with an initial 500 apps. At the time of writing (March 2020), there should be close to 2 million.

In case you don’t remember, Steve Jobs’ original plan was to not allow third-party developers to create native apps for the iPhone. Developers were directed to create web apps. The backlash prompted Apple to allow developers to create apps, and in March 2008, the first iPhone SDK was released.
Azure Azure, Microsoft’s foray into cloud computing, and the thing that would eventually bring about its turnaround after Steve Ballmer’s departure, was introduced at their PDC conference in 2008 — which I attended on the second week of my job there.
Bitcoin

The person (or persons) going by the name “Satoshi Nakamoto” started working on the Bitcoin project in 2007.

It would eventually lead to cryptocurrency mania, crypto bros, HODL and other additions to the lexicon, one of the best Last Week Tonight news pieces, and give the Winklevoss twins their second shot at technology stardom after their failed first attempt with a guy named Mark Zuckerberg.
Chrome

By 2008, the browser wars were long done, and Internet Explorer owned the market. Then, on September 2, Google released Chrome, announcing it with a comic illustrated by Scott “Understanding Comics” McCloud, and starting the Second Browser War.

When Chrome was launched, Internet Explorer had about 70% of the browser market. In less than 5 years, Chrome would overtake IE.
Data: bandwidth costs and speed In 2007, bandwidth costs dropped dramatically, while transmission speeds grew in the opposite direction.
Dell returns After stepping down from the position of CEO in 2004 (but staying on as Chairman of the Board), Michael Dell returned to the role on January 31, 2007 at the board’s request.
DNA sequencing costs drop dramatically The end of the year 2007 marks the first time that the cost of genome sequencing dropped dramatically — from the order of tens of millions to single-digit millions. Today, that cost is about $1,000.
DVD formats: Blu-Ray and HD-DVD In 2008, two high-definition optical disc formats were announced. You probably know which one won.
Facebook In September 2006, Facebook expanded beyond universities and became available to anyone over 13 with an email address, making it available to the general public and forever altering its course, along with the course of history.
Energy technologies: Fracking and solar Growth in these two industries helped turn the US into a serious net energy provider, which would help drive the tech boom of the 2010s.
GitHub Originally founded as Logical Awesome in February 2008, GitHub’s website launched that April. It would grow to become an indispensable software development tool, and a key part of many developer resumes (mine included). It would first displace SourceForge, which used to be the place to go for open source code, and eventually become part of Microsoft’s apparent change of heart about open source when they purchased the company in 2018.
Hadoop

In 2006, developer Doug Cutting of Apache’s Nutch project, took used GFS (Google File System, written up by Google in 2003) and the MapReduce algorithm (written up by Google in 2004) and combined it with the dataset tech from Nutch to create the Hadoop project. He gave his project the name that his son gave to his yellow toy elephant, hence the logo.

By enabling applications and data to be run and stored on clusters of commodity hardware, Hadoop played a key role in creating today’s cloud computing world.
Intel introduces non-silicon materials into its chips January 2007: Intel’s PR department called it “the biggest change to computer chips in 40 years,” and they may have had a point. The new materials that they introduced into the chip-making process allowed for smaller, faster circuits, which in turn led to smaller and faster chips, which are needed for mobile and IoT technologies.
Internet crosses a billion users This one’s a little earlier than our timeframe, but I’m including it because it helps set the stage for all the other innovations. At some point in 2005, the internet crossed the billion-user line, a key milestone in its reach and other effects, such as the Long Tail.
iPhone

On January 9, 2007, Steve Jobs said the following at this keynote: “Today, we’re introducing three revolutionary new products…an iPod, a phone, and an internet communicator…Are you getting it? These are not three separate devices. This is one device!”

The iPhone has changed everyone’s lives, including mine. Thanks to this device, I landed my (current until recently) job, and right now, I’m working on revising this book.
iTunes sells its billionth song On February 22, 2006, Alex Ostrovsky from West Bloomfield, Michigan purchased ColdPlay’s Speed of Sound on iTunes, and it turned out to be the billionth song purchased on that platform. This milestone proves to the music industry that it was possible to actually sell music online, forever changing an industry that had been thrashing since the Napster era.
Kindle

Before tablets or large smartphone came Amazon’s Kindle e-reader, which came out on November 19, 2007. It was dubbed “the iPod of reading” at the time.

You might not remember this, but the first version didn’t have a touch-sensitive screen. Instead, it had a full-size keyboard below its screen, in a manner similar to phones of that era.
Macs switch to Intel

The first Intel-based Macs were announced on January 10, 2006: The 15″ MacBook Pro and iMac Core Duo. Both were based on the Intel Core Duo.

Motorola’s consistent failure to produce chips with the kind of performance that Apple needed on schedule caused Apple to enact their secret “Plan B”: switch to Intel-based chips. At the 2005 WWDC, Steve Jobs revealed that every version of Mac OS X had been secretly developed and compiled for both Motorola and Intel processors — just in case.

We may soon see another such transition: from Intel to Apple’s own A-series chips.
Netflix In 2007, Netflix — then a company that mailed rental DVDs to you — started its streaming service. This would eventually give rise to binge-watching as well as one of my favorite technological innovations: Netflix and chill (and yes, there is a Wikipedia entry for it!), as well as Tiger King, which is keeping us entertained as we stay home.
Python 3

The release of Python 3 — a.k.a. Python 3000 — in December 2008 was the beginning of the Second Beginning! While Python had been eclipsed by Ruby in the 2000s thanks to Rails and the rise of MVC web frameworks and the supermodel developer, it made its comeback in the 2010s as the language of choice for data science and machine learning thanks to a plethora of libraries (NumPy, SciPy, Pandas) and support applications (including Jupyter Notebooks).

I will always have an affection for Python. I cut my web development teeth in 1999 helping build Givex.com’s site in Python and PostgreSQL. I learned Python by reading O’Reilly’s Learning Python while at Burning Man 1999.
Shopify In 2004, frustrated with existing ecommerce platforms, programmer Tobias Lütke built his own platform to sell snowboards online. He and his partners realize that they should be selling ecommerce services instead, and in June 2006, launch Shopify.
Spotify The streaming service was founded in April 2006, launched in October 2008, and along with Apple and Amazon, changed the music industry.
Surface (as in Microsoft’s big-ass table computer)

Announced on May 29, 2007, the original Surface was a large coffee table-sized multitouch-sensitive computer aimed at commercial customers who wanted to provide next generation kiosk computer entertainment, information, or services to the public.

Do you remember SarcasticGamer’s parody video of the Surface?
Switches 2007 was the year that networking switches jumped in speed and capacity dramatically, helping to pave the way for the modern internet.
Twitter

In 2006, Twittr (it had no e then, which was the style at the time, thanks to Flickr) was formed. From then, it had a wild ride, including South by Southwest 2007, when its attendees — influential techies — used it as a means of catching up and finding each other at the conference. @replies appeared in May 2007, followers were added that July, hashtag support in September, and trending topics came a year later.

Twitter also got featured on an episode of CSI in November 2007, when it was used to solve a case.
VMWare After performing poorly financially, the husband and wife cofounders of VMWare — Diane Greene, president and CEO, and Mendel Rosenbaum, Chief Scientist — left. Greene was fired by the board in July, and Rosenbaum resigned two months later. VMWare would go on to experience record growth, and its Hypervisors would become a key part of making cloud computing what it is today.
Watson IBM’s Watson underwent initial testing in 2006, when Watson was given 500 clues from prior Jeopardy! programs. Wikipedia will explain the rest:

While the best real-life competitors buzzed in half the time and responded correctly to as many as 95% of clues, Watson’s first pass could get only about 15% correct. During 2007, the IBM team was given three to five years and a staff of 15 people to solve the problems. By 2008, the developers had advanced Watson such that it could compete with Jeopardy! champions.
Wii The Wii was released in December 2006, marking Nintendo’s comeback in a time when the console market belonged solely to the PlayStation and Xbox.
XO computer You probably know this device better as the “One Laptop Per Child” computer — the laptop that was going to change the world, but didn’t quite do that. Still, its form factor lives on in today’s Chromebooks, which are powered by Chrome (which also debuted during this time), and the concept of open source hardware continues today in the form of Arduino and Raspberry Pi.
YouTube

YouTube was purchased by Google in October 2006. In 2007, it exploded in popularity, consuming as much bandwidth as the entire internet did 7 years before. In the summer and fall of 2007, CNN and YouTube produced televised presidential debates, where Democratic and Republican US presidential hopefuls answered YouTube viewer questions.

You probably winced at this infamous YouTube video, which was posted on August 24, 2007: Miss Teen USA 2007 – South Carolina answers a question, which has amassed almost 70 million views to date.
Categories
Career What I’m Up To

The interview where I ALMOST succeeded

Last week, I interviewed for a developer relations leadership role at a company whose product I genuinely use and admire.

I made it to round 2 of 3, but ultimately wasn’t selected.

While I didn’t land the job and a chance to work with an amazing company and incredible team, I’m honored to have been considered and incredibly proud of the work I put in:

  • 30+ hours of research and preparation
  • 100+ slides across two presentations
  • Some of the most meaningful conversations I’ve had with a team in years

I could simply throw up my hands in resignation and leave all that work and content to languish in a folder on a backup drive or in the cloud…

…but instead, I’m sharing it here. Why?

Because:

  • Good ideas deserve to circulate! Maybe there’s a framework, approach, or creative solution in my presentations that could help someone else.
  • We’re not alone in this. The job market is tough right now, and I want people to know they’re not the only ones putting in extraordinary effort. I know you’re out there, giving it your all!
  • Transparency builds community and helps others. Real examples of strategic work are worth way more than hand-waving abstract advice.

I’m sharing the slides that outline my complete developer relations strategy presentation plus my tactical execution plan, anonymized and annotated with speaker notes. You’ll see my “Foundation / Focus / Flywheel” framework, community engagement strategies, and how I approached everything from attribution tracking to expanding into Europe.

  • If you’re job searching: Take what’s useful here and build on it! No attribution necessary. We’re all in this together, and your success doesn’t diminish mine.
  • If you found this valuable: Please share it! It helps me and others going through this.
  • If you’re hiring: This shows how I think about DevRel strategy. If your team builds for developers, let’s chat!

My philosophy is that either I win or I learn. I learned a lot from this process, and I’m grateful for the opportunity to have engaged with such a thoughtful team.

Next time, I just might win.

Here’s the link to my presentations.

Categories
Artificial Intelligence Career What I’m Up To

Claude does impression of me, comes up with new acronym: STARBS

Ben Affleck’s interview scene from “Good Will Hunting”.

I’m working on some new videos for the Global Nerdy YouTube channel, and one of them is on answering questions in the dreaded “behavioral interview.” That’s the interview where you’re asked questions like “Tell me about a time when you had to deal with a conflict on your team. How did you handle it?”

To help come up with ideas for the video about behavioral interviews, I posed some questions to LLMs. As I like to say, no matter how creative or clever you are, it is by definition impossible to make a list of ideas that you’d never come up with yourself.

So I posed this question to Claude, which for some reason, seems to know stuff about me:

How would you describe a behavioral interview, if you had to describe it in the style of Joey deVilla?

And wow, was the response snark-tastic:

Here’s the text of the response:

Joey deVilla’s Guide to Behavioral Interviews

Imagine me adjusting my accordion straps and coffee mug before dropping this knowledge bomb…

So you wanna know about behavioral interviews? Those corporate rituals where they ask you to “Tell me about a time when…” instead of just asking if you can do the damn job?

Behavioral interviews are the tech industry’s version of “Show, don’t tell” – except instead of showing your actual skills, you’re telling stories about when you supposedly used them! It’s like your professional life as fan fiction, where the hiring manager is desperately hoping you’re not writing in the genre of fantasy.

These interviews operate on the radical premise that past behavior predicts future performance. Revolutionary, I know! It’s like assuming that because I once played “Lady of Spain” on my accordion at a Tampa dev meetup, I’ll do it again given the chance and sufficient sangria.

The formula they want is STAR: Situation, Task, Action, Result. But what they’re REALLY looking for is STARBS: Situation, Task, Action, Result, Bull$#!t – that special sauce where you frame yourself as the hero who saved the project while maintaining perfect diplomatic relations with that passive-aggressive PM who kept moving the goalposts.

The trick isn’t just recounting what happened – it’s storytelling with a corporate-friendly narrative arc. You need conflict (but not TOO much conflict), challenges (that showcase your skills), and resolution (where you emerge victorious but humble).

Remember: in behavioral interviews, there are no failures – only “growth opportunities” and “valuable lessons” that conveniently transformed you into exactly the employee they’re looking for!

Adjusts glasses, takes dramatic sip of coffee

Now, if you’ll excuse me, I need to go practice my own STAR stories for when someone inevitably asks me about “a time I demonstrated adaptability in a challenging situation” – which is code for “that time I coded for 36 hours straight because someone in management promised an impossible deadline.”

My questions, after reading Claude’s output:

  1. Am I really that much of a smart-ass?
  2. “STARBS” — Situation, Task, Actions, Result, Bullshit — is pretty clever. Whose writing did Claude lift that from?

So I did some web searches using terms like starbs star situation task action result bullshit, and couldn’t find any instance of STARBS. It seems to be one of those effectively new creations that an autocomplete-on-steroids can generate.

I’m not gonna lie: I kind of like STARBS, and wish that I’d come up with it myself.

(If you’re interested, see if you can do some searches to find other instances of the term STARBS.)

Categories
Career Humor Process

Types of Workers

Comic titled “Types of Workers,” with three images. First image: “Remote,” featuring worker on Zoom call. Second image: “Hybrid,” featuring worker on Zoom call. Third image: “Office,” featuring worker on Zoom call.