r/ArtificialInteligence

The only thing growing faster than the artificial-intelligence industry may be Americans’ negative feelings about it, as former Google Chief Executive Eric Schmidt saw on Friday.

Delivering a commencement address at the University of Arizona, Schmidt told students the “technological transformation” wrought by artificial intelligence will be “larger, faster, and more consequential than what came before.” Like some other graduation speakers mentioning AI, Schmidt was met with a chorus of boos.

We have officially reached the end of the unmetered AI honeymoon phase. Tech giants are rapidly moving away from flat rate subscriptions, introducing rolling compute bars and strict hourly caps to protect their heavily strained GPU infrastructures.

This sudden squeeze completely breaks heavy user workflows like novel drafting or deep academic research. Long chat histories now incur a massive context tax, meaning one complex prompt can vaporise an entire afternoon allowance in minutes.

Ultimately, the era of treating cloud supercomputing like an infinite, completely free resource has vanished. We are entering a highly nuanced reality where digital intelligence is a rationed utility, inevitably forcing schools, creative writers, and broader industries straight back to traditional, analog methods of human critical thinking.

How are you adapting your workflows to these new usage caps?

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I added tested Gemini 3.5 Flash and ran it through around 10 saved evals I use for model selection decisions in production.

So far, the result is not what I expected.

On most of my tasks, Gemini 3.5 Flash underperformed older Gemini variants. In the screenshot below, this is a vision emotion-detection eval with 5 runs per model:

In, this eval it ended way down at 13th place, even though 3.1-pro and 3.1 flash lite are top 1 & 2, its even lower than gemini 3 flash actually. Its 10x more expensive than flash lite for a worse result. Its an avg result of 5 runs so its not a one time fluke. On top of that, this is 1/10 benchmarks with similar outcomes, although admittedly this is one of the worst case.

https://preview.redd.it/e87e67lm752h1.png?width=2750&format=png&auto=webp&s=93e7820e8d6f5cc832c0b756ed27ff00f2c21ae9

I ran this via an online benchmarking tool. Not claiming this means Gemini 3.5 Flash is bad universally. These are my saved evals, and Gemini and any models can be prompt-sensitive. But for my workflows, these benchmarks unfortunately indicate that I can't use it as is.

I really hope that this is something that will change, because I had high expectations for this model given their previous release. To me it just goes to show that artificial analysis and other generic benchmarks can really be misleading when it comes to model decisions. From what the results they were showing I was expecting much better...

More Data on the eval:

====================================================================================================
LLM Benchmark Results - Emotion Detection - Increasing Complexity
====================================================================================================

Model                   Provider    Avg Score           Stability   Rec. Temp Pricing     Cost*       Time      Acc/$     Acc/min   Completion
----------------------------------------------------------------------------------------------------------------------------------------------
gemini-3.1-pro          gemini      80% (3.2/4.0)       ±1.000      0.3       High        $0.0292     23.48s    109.58    8.18      100.0%    
gemini-3.1-flash-lite   gemini      75% (3.0/4.0)       ±0.000      0.3       Medium      $0.00114    6.24s     2.63K     28.85     100.0%    
gpt-5.4                 openai      75% (3.0/4.0)       ±0.000      N/A       High        $0.0128     8.45s     234.24    21.31     100.0%    
claude-opus-4.6         anthropic   75% (3.0/4.0)       ±0.000      0.3       High        $0.0246     12.44s    121.73    14.46     100.0%    
gemini-3-flash          gemini      65% (2.6/4.0)       ±1.000      0.3       Medium      $0.00735    16.36s    353.81    9.54      100.0%    
sonar                   perplexity  65% (2.6/4.0)       ±1.000      0.3       Medium      $0.0256     10.61s    101.60    14.71     100.0%    
grok-4-fast-non-reason  xai         55% (2.2/4.0)       ±1.000      0.3       Low         $0.000375   7.31s     5.87K     18.06     100.0%    
gpt-5-nano              openai      55% (2.2/4.0)       ±1.000      N/A       Very Low    $0.000592   12.35s    3.72K     10.69     100.0%    
mistral-medium-latest   mistral     55% (2.2/4.0)       ±1.000      0.3       Medium      $0.00219    8.29s     1.01K     15.93     100.0%    
llama4-maverick         meta        50% (2.0/4.0)       ±0.000      0.3       Low         $0.00202    7.35s     988.82    16.33     100.0%    
gpt-5.4-mini            openai      50% (2.0/4.0)       ±0.000      N/A       Medium      $0.00384    12.95s    520.53    9.26      100.0%    
claude-sonnet-4.6       anthropic   50% (2.0/4.0)       ±0.000      0.3       High        $0.0148     8.96s     135.25    13.39     100.0%    
gemini-3.5-flash        gemini      50% (2.0/4.0)       ±0.000      0.3       High        $0.0168     11.32s    118.99    10.60     100.0%    
gpt-5.4-nano            openai      38% (1.5/4.0)       ±1.000      N/A       Low         $0.00103    11.31s    1.46K     7.96      100.0%    
claude-haiku-4.5        anthropic   25% (1.0/4.0)       ±0.000      0.3       Medium      $0.00493    5.74s     202.88    10.46     100.0%    

Total models tested: 15

I don’t know if I’m the only one feeling this, but life felt way simpler before AI exploded everywhere.

I feel like I’m in a constant cycle of stress about upskilling. Every day there’s some new model, new framework, new tool, new trend. I keep asking myself: Which track do I even choose? Which stack will still be relevant in a few years? If I pivot into something new and invest months learning it, what if the market shifts again? And if I switch stacks, how do I even find jobs in that new area when my previous experience is in a completely different stack and role?

Earlier, things felt more stable. You had your domain, your role, your tech stack, and while things changed, it didn’t feel like the ground beneath you was moving every week. Now it feels like every day there are new updates and suddenly people are saying, “You also need to learn this now.”

I’m genuinely confused whether AI has helped us more than it has harmed us. Yes, productivity has gone up, and some people are benefiting massively. But for a lot of regular people it feels like we’re just scrambling for job security and trying not to become irrelevant.

Sometimes it feels like a select few companies are making billions while everyone else is anxiously trying to keep up.

Am I overthinking this, or are other people feeling the same thing?