Blue Tides and Silicon Currents: Navigating Political Shifts and the AI-pocalypse

Blue Wave Slams Utah

We had a pleasant surprise with the election results on Tuesday night with the blue wave that gave control of Congress to the Democrats and broke the Utah House supermajority. The Utah Stonewall Dems saw potentially 16 of our endorsed candidates win their races, even though on Thursday, November 5, there were still late ballots that need to be counted. We had our first annual GayMe night Saturday, a potluck bring-your-own-game at the local Teamsters Hall in West Valley to help unwind after the election.

On Tuesday night we waited with baited breath. In the days leading up to the election, there were already reports of incredibly high turnout and early voting lines were reaching around the corner. There was already talk of dissatisfaction with the economy and its policies. The signs were there that the people were turning out against the current administration.

I posted the question to a chat agent and it said that it was one reason for high turnout, but analysts also talked about high polarization being a turnout factor. I didn't buy the latter. High polarization turns out the current base and may actually lead to decreased turnout in the middle. High turnouts are typically associated with a feeling of solidarity in a referendum.

I tuned into ABC Livestream occasionally during the day. Starting at 6 pm, when the results were showing strong Democratic leads along the Eastern seaboard and in Texas, I was getting excited about what was happening in Texas, Kansas, and Georgia. The Democrats were in the process of pulling upsets off. Meanwhile, the Salt Lake County Clerk had to extend the polls til 9 pm because the lines were so long. This felt like the primary in 2016 all over again.

And then the polls finally closed and Utah's results were posted. And as I scrolled through the results, Democrats were dominating in the districts they were trying to hold or take back. I immediately saw three Republicans that were targeted falling way behind. This was more that I had expected. Way more. The blue wave had crashed through Utah, finally.

The next morning, I immediately wanted to congratulate the Utah Stonewall Endorsed Candidates and looked at each individual race. A handful dominated their races, and a total of 16 of 24 of our endorsed candidates were winning! The lowest vote of any of our endorsed candidates statewide was 38%. Every one of these candidates pledged to be champions of the LGBTQ+ community, including Ben McAdams, who was elected back to Congress. In addition eight transgender women won their respective races!

The LGBTQ+ community has a much stronger voice at the table, Donald Trump is officially a lame duck president, and the Utah House supermajority has been broken for the first time in 40 years. We have a shot in 2028 of gaining a Democratic senator and governor, and the Salt Lake County Democratic Party is looking very closely to flip five legislative seats that had very tight results.

AI-pocalypse Averted or Not

Is AI-pocalypse here or are we narrowly averting it? What I am referring to as the impeding AI-pocalypse is the economic upheaval resulting from large-scale irresponsible use of Artificial Intelligence.

Private equity poured lots of money into researching and propping up a model that has a diminishing return on investment, both environmentally and socially.

Corporate firms felt pushed to push their programming staff to develop software as rapidly as possible using LLM based foundations without taking the time to understand how to develop quality code at speed. Technical debt and bugs started appearing in the code that the both AI generators found harder to fix. Downstream effects ensued.

Meanwhile, software engineers who were laid off are rightly asking for 50% more in salary because of what they went through and the mess they had to clean up. A large number of voters said no to large-scale data centers eating our resources, and companies who became reliant on edge AI for code generation find its cost too high, directly and indirectly.

Profits and potential profits have been eaten away, and cities, states, and counties are shutting the doors to more large-scale data centers. The AI tech sector took a nose-dive after the election, likely leaving it less likely investors will invest and shore up the market.

What is bad is the adoption was forced on the tech sector before guidelines for high-quality usage patterns could be explored and documented. On the other hand, some engineers like myself are learning how to leverage the strengths the models were trained on, while retaining control of the delivered product. Pushing tech adoption bubbles has the negative side effect just discussed.

I should push my intent-driven pairing model more. If there is a way I can tie this to potential AI research, advisers might consider this an acceptable statement of purpose. This would align with HCI (Human Computer Interaction), particularly HAI (Human AI interaction).

Obviously, there would be surveys needed. I would also need to do experiments with the different models on different scenarios. I’m not sure where I could get statistics on the different approaches, but an academic research survey may shed some light.

Once I get volunteers, I could add a system prompt that establishes the pairing methodology for one variant and not the other. After capturing the sessions, I could also check and classify whether the user specified an interactive prompt style. My perfect sample set would be Computer Science students.

The issue is that my first fall term as a PhD student won't start until 2027 if I am admitted. Meanwhile, I can continue to refine my model in my daily interactions whenever I am coding. Of course, I am shifting to support the data platform team, where the coding interaction would be simlar, but in a different domain. This has the potential to break into multiple research papers that I can use for my dissertation.

Navigating the Path Ahead

HR just released the updated career tracks for legacy Wwex employees. It looks like they added a few senior roles. The highest bar for Individual Contributor (IC) is Executive Principal (EP). This might help narrow down what my “significant promotion” I’ve been waiting on will be. My thought is that it will be skipping Principal II and landing in Grade 12.

EP looks like it would report directly to the C-Suite. A promotion to Principal II, I would consider as “standard” and not necessarily significant.

So I’m assuming we can rule out the manager roles, which leaves Principal Data Architect (which I doubt), Principal II Solution Architect (possible), and Senior Principal Software Engineering (also possible), while assisting the data platform team, consulting and occasionally maintaining core backend architecture, maintaining data integration changes, and edge development.

They’ve had me lead Tiger Teams, and that’s okay. The progress projections are a bit annoying, but I can do it. The nice part is I don’t have to manage performance reviews and time off requests. If they only promote me to Principle II, per the legacy guidelines, I wouldn’t make Senior Principal until I was 64.

There’s no Grade 14 IC position for the software engineering track on this path.

According to the ladder, when I reached that level I would have three choices moving from level 13 Software Engineering Principal to Level 14:

• Solution architecture,

• Data architecture, or

• Management.

What gives me a little extra possibility is that the CTO has announced that he intends to be able to make spot promotions and transfers for people that desire to be on other teams or initiatives.

Looking at job postings, after I get my PhD, I could be eligible for local jobs starting in the $200k range. I’m still in the process of applying for admission, though. My current potential statement of work is around my AI-assisted Intent-based model as part of an emphasis on the Human AI interaction model with the realm of accountable artificial intelligence.

I believe this model has potential downstream effects, like helping entry-level programmers become more seasoned professionals, restoring the entry-level bar into software engineering. This idea hits three rungs:

• Human Computer/AI Interaction,

• Accountable AI, and

• Computer Science Education

I’m reminded of the Star Trek and science fiction cyborgs, where humans learned to integrate with AI systems to have greater skills and accomplish more. Of course, cyborgs have been depicted as potentially scary when the human loses control, and that is a theme we need to remember. While I am not recommending we all become cyborgs, I believe that we can accomplish more using these powerful tools while deliberately retaining control.