You read right, $2.5 billion in AI usage for a single year. This averages out to about $35,000-$55,000 per developer (depending on how you count their employees), or about $3000-$5000 per dev per month.
This is a staggering expense for a company; and if this is part of the core strategy, an extremely dangerous one. This spending may even be justified today – until the AI companies double or triple or more what they are charging. Which they often do without warning. Overnight you could find this number become twice as much.
2026 Unity Game Development Report reports a lot of things many already know. The game industry – after massive hiring during Covid when people had nothing to do but play games – is now going through one of the worst layoff periods since the video game collapse of the 1980’s. This is causing major shifts in how and what game development teams are building. The report tells us the practical effects of this industry struggle.
The activist elements of the game development industry have been increasingly and staunchly anti-AI. As the survey shows, however, it’s probably a losing position. This is especially true in the hyper-competitive environment that is game development where speed is critical.
I recently read the lament of one seasoned programmer that interviewed really well at nVidia until they got to a set of AI experience questions. The tone changed when he said he didn’t use AI and had little experience coding with it. He went further and said he’d prefer not to use it. He didn’t get the offer and was complaining on the forum as to why that mattered since he was an excellent coder. An nVidia employee popped on and confirmed it. He said that this senior programmer wouldn’t be successful – simply because everyone else would be coding circles around him. You simply cannot write the volume that is expected from you without it. Sticking your head in the sand and just doing everything by hand means you’ll soon be driving 40 on the freeway when everyone else is driving by at 65.
The models that could only churn out slop 12 months ago started getting ‘good enough’ for testing and other menial tasks about 6 months ago. Now, in mid 2026, they’re good enough to create entire software stacks.
As a software engineer myself, the reality is that it’s not a question of if you are using AI or not. If you are not, you simply cannot keep up with the volume of work those that have learned how to us AI can do. AI has it’s problems, but it’s also a powerful force multiplier.
Are jobs being lost to AI – almost certainly. But it doesn’t have to be the end of your career. What it does mean is you have to learn to use the tools and demonstrate you are more proficient than your competition at generating quality output.
Still, the double-whammy is having some predictable outcomes:
Developers are prototyping smaller-scale games as a means of averting the risks of going for bigger projects that end up shuttering the studio if they’re not instant successes.64% of respondents from studios with 10-49 employees say market conditions have led to a focus on smaller, more manageable projects
The majority of developers making their games with Unity (62%) use AI tools for coding assistance, with the second most popular use, writing and narrative design, at 44%. Only 5% of those surveyed responded with “I do not use AI.”
Larger teams are the ones adopting AI tools into their workflow at the highest rate with “79% of polled devs with over 150 team members say that AI tools have helped them improve efficiency.”
Asian Dad Energy is one of the literally 10,000’s of highly skilled software engineers that has been laid off in the last 2 years. Here’s his video of reaching 100 days of continued unemployment (published 3 months ago). His channel covers what a LOT of former software engineers are going through as the industry undergoes a massive reset due to the end of Covid era and the rise of AI.
As I posted before, the unemployment rate for recent college graduates has been significantly higher than the labor market taken as a whole across all workers, according to Federal Reserve Bank of New York data.
Even worse, Gen Z men with college degrees (taken as a whole across all majors) now have the same unemployment rate as non-grads. Meaning higher education no longer pays off for men when it comes to unemployment – though college grads make more than their non-college counterparts over their life.
Anyone that doesn’t think that rampant over hiring during Covid and AI is actively reshaping the entire programming industry is fooling themselves. Especially since so many people are calling software a ‘cooked’ field now.
Back in April this video came up in my feed – and the whole series are some of the best talks on the state of engineering software in the age of AI; but this particular talk by Matt Pocock was particularly good.
Square One TV was a math oriented education show during the late 80’s and early 90’s on PBS. I loved catching the show whenever I could after school. It was a show full of short sketches – one of the regular ones was Mathnet – a mathematical parody of Dragnet.
In one episode – they hit 2 of my favorite things: math and spooky mysteries. They parodied Agatha Christie’s “And Then There Were None” in the episode “The Case of the Mystery Weekend“.
Fish Audio S2 Pro is one of (if not the) best text-to-speech solutions. Getting it installed locally and working, however, isn’t so straightforward on Windows 10. There are at least 2 different ways to get this working. One of which is to download/run
Ensure you’re logged into Huggingface, and you should see the ‘Run Locally’ option Go up in the link
Ensure Docker is installed on the Windows desktop and WSL support is enabled in the Docker options.
Open a WSL session running Ubuntu 24.04 or similar.
Enter the docker command:
docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all \
registry.hf.space/artificialguybr-fish-s2-pro-zero:latest python app.py
6. You’ll see the docker container download along with the models and start up:
(base) me@DESKTOP:/mnt/c/fish-audio-s2$ docker run -it -p 7860:7860 --platform=linux/amd64 --gpus all registry.hf.space/artificialguybr-fish-s2-pro-zero:latest python app.py
Cloning into 'fish-speech'…
remote: Enumerating objects: 6605, done.
remote: Counting objects: 100% (1088/1088), done.
remote: Compressing objects: 100% (292/292), done.
remote: Total 6605 (delta 905), reused 796 (delta 796), pack-reused 5517 (from 2)
Receiving objects: 100% (6605/6605), 28.21 MiB | 10.42 MiB/s, done.
Resolving deltas: 100% (4328/4328), done.
Warning: You are sending unauthenticated requests to the HF Hub. Please set a HF_TOKEN to enable higher rate limits and faster downloads.
Fetching 13 files: 100%|████████████████████████████████████████████████████████████████| 13/13 [02:19<00:00, 10.72s/it]Fetching 13 files: 62%|████████████████████████████████████████ | 8/13 [02:19<01:13, 14.78s/itYou are using a model of type fish_qwen3_omni to instantiate a model of type `. This may be expected if you are loading a checkpoint that shares a subset of the architecture (e.g., loading asam2_video checkpoint intoSam2Model), but is otherwise not supported and can yield errors. Please verify that the checkpoint is compatible with the model you are instantiating. Download complete: : 11.0GB [02:19, 79.0MB/s] 2026-07-03 18:59:16.787 | INFO | fish_speech.models.text2semantic.llama:from_pretrained:504 - Injected Semantic IDs into Config: 151678-155773 2026-07-03 18:59:16.787 | INFO | fish_speech.models.text2semantic.llama:from_pretrained:520 - Loading model from /home/user/.cache/huggingface/hub/models--fishaudio--s2-pro/snapshots/1de9996b6be38b745688de084d87a5633f714e4e, config: DualARModelArgs(model_type='dual_ar', vocab_size=155776, n_layer=36, n_head=32, dim=2560, intermediate_size=9728, n_local_heads=8, head_dim=128, rope_base=1000000, norm_eps=1e-06, max_seq_len=32768, dropout=0.0, tie_word_embeddings=True, attention_qkv_bias=False, attention_o_bias=False, attention_qk_norm=True, codebook_size=4096, num_codebooks=10, semantic_begin_id=151678, semantic_end_id=155773, use_gradient_checkpointing=True, initializer_range=0.01976423537605237, is_reward_model=False, scale_codebook_embeddings=True, audio_embed_dim=2560, n_fast_layer=4, fast_dim=2560, fast_n_head=32, fast_n_local_heads=8, fast_head_dim=128, fast_intermediate_size=9728, fast_attention_qkv_bias=False, fast_attention_qk_norm=False, fast_attention_o_bias=False, norm_fastlayer_input=True) 2026-07-03 18:59:46.228 | INFO | fish_speech.models.text2semantic.llama:from_pretrained:552 - Loading sharded safetensors weights 2026-07-03 18:59:46.717 | INFO | fish_speech.models.text2semantic.llama:from_pretrained:588 - Model weights loaded - Status: <All keys matched successfully> 2026-07-03 18:59:48.707 | INFO | fish_speech.models.text2semantic.inference:init_model:366 - Restored model from checkpoint 2026-07-03 18:59:48.708 | INFO | fish_speech.models.text2semantic.inference:init_model:371 - Using DualARTransformer/usr/local/lib/python3.10/site-packages/torch/nn/utils/weight_norm.py:144: FutureWarning:torch.nn.utils.weight_normis deprecated in favor oftorch.nn.utils.parametrizations.weight_norm`.
WeightNorm.apply(module, name, dim)
* Running on local URL: http://0.0.0.0:7860, with SSR ⚡ (experimental, to disable set ssr_mode=False in launch())
* To create a public link, set share=True in launch().
