Game industry is changing and embracing AI slowly

Game industry is changing and embracing AI slowly

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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:

  • 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.”
    • This is in line with GDC’s State of the Game Industry report
    • 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.”

Artices:

100 Days unemployed as a former Big Tech Software Engineer

100 Days unemployed as a former Big Tech Software Engineer

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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.

37 Majors Have Unemployment Rates Higher Than non-college majors and college now doesn’t pay off in work for majority of men

37 Majors Have Unemployment Rates Higher Than non-college majors and college now doesn’t pay off in work for majority of men

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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.

In other data, these particular majors have the worst unemployment rates. Interesting new ones on the list? Computer Science and Computer Engineering – almost at the top.

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.

College MajorUnemployment Rate
Anthropology7.90%
Computer Engineering7.80%
Fine Arts7.70%
Performing Arts7.00%
Computer Science7.00%
Architecture6.80%
Art History6.70%
Physics6.60%
Early Childhood Education6.60%
Environmental Studies6.30%
Medical Technicians6.20%
International Affairs6.10%
English Language6.10%
Information Systems & Management6.00%
Mathematics5.80%
Commercial Art & Graphic Design5.70%
Advertising and Public Relations5.70%
Pharmacy5.60%
Mass Media5.20%
Philosophy5.10%
Psychology5.00%
Business Analytics5.00%
Ethnic Studies4.90%
Chemical Engineering4.70%
Sociology4.60%
Political Science4.50%
Nutrition Sciences4.50%
General Engineering4.50%
Miscellaneous Biological Science4.40%
Mechanical Engineering4.40%
Marketing4.40%
History4.30%
General Business4.30%
Family and Consumer Sciences4.30%
Chemistry4.30%
Biology4.30%
Industrial Engineering4.20%
Mathnet takes on Agatha Christie

Mathnet takes on Agatha Christie

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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“.

Local installation of Fish Audio on Windows 10

Local installation of Fish Audio on Windows 10

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I’ve been exploring

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

Method 0: Use the free online version

It’s not hard – but expect to be limited in usage. https://fish.audio/app/text-to-speech

Method 1: Fish S2 Pro Zero Docker

  1. Go to the Huggingface Fish Audio S3 Pro project page.
  2. Ensure you’re logged into Huggingface, and you should see the ‘Run Locally’ option Go up in the link
  3. Ensure Docker is installed on the Windows desktop and WSL support is enabled in the Docker options.
  4. Open a WSL session running Ubuntu 24.04 or similar.
  5. 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().

7. Open a browser to localhost:7860

Method 2: Build and run locally

  1. Clone the github project: https://github.com/fishaudio/fish-speech
  2. Open a WSL Ubuntu 24.04 installation.
  3. Ensure you have nVidia support in WSL installed. Rebooting after this is often required.
  4. Follow the installation/build instructions.
    • Run the conda setup steps
    • Run the UV steps for CPU or GPU depending on your install
    • Skip the docker part
  5. WebUI On the left menu, select the ‘Inference’ from the list of items
    • Download the model weights with the hf command
      • You can test using the command line inference steps if you want to test it
    • Scroll down to the WebUI inference
    • Install Gradio if you want the older style (not so much recommended, but easier than Awesome WebUI)
    • Install the ‘Awesome WebUI’
    • Start the ‘Awesome WebUI’ using the python command
  6. Server
    • Select the ‘Server’ item from the list of left-hand items
    • Run the python command to start the server locally.
    • Try out one of the api_client.py commands to test it out

Things you can do with the server:

Other links:

Can cheaper, faster robotics revitalize modern manufacturing and transform the military?

Can cheaper, faster robotics revitalize modern manufacturing and transform the military?

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It feels like the American industrial and manufacturing landscape has been left behind in the digitalization revolution. But recent changes demonstrated in both Ukraine and a small robot company in Pittsburg may be pointing to the coming revolution.

Gecko is a scrappy robot company founded by a college senior that saw workers spending hours putting up dangerous scaffolding to check and fix pipes in a power plant. What if he could build robots to scale around the pipes and check and fix them? It turns out they could – and it is revolutionizing maintenance in refineries and energy infrastructure across the country. The robots now no longer can crawl and inspect/repair – but they can create new digital maps of a plant’s infrastructure. Inspections are taking orders of magnitude less time. Plants can have their actual systems instantly remapped/re-diagrammed instead of relying on out of date schematics.

It turns out someone else has the same problem: the military. Systems like Gecko allow the navy to build and repair ships faster. Gecko’s small robots reduced nuclear submarine inspection times from 300 hours to just 6 hours. But this revolution is bigger than just repair of existing systems.

The war in Ukraine is now being won not by ‘exquisite’, complex, and exorbitantly expensive weapons systems. Instead, it’s being won by swarms of low-cost drones. Million dollar tanks are being disabled by $200 drones carrying explosives. Military experts around the world are watching Ukraine and re-thinking everything. Even before the Ukraine war, the US navy was already started the move from big capital ships to cheaper, faster to build modular ships.

Anduril wrote a paper in 2024 that goes a step further. They claim that these low-cost robotics and AI systems are making existing weapons systems vulnerable and outdated. Modern, 1st world weapon systems are too complex and hugely expensive. They take too long to make in quantities required for something beyond a short war. What is needed is to establish fast, cheap, commercial manufacturing of these systems that can be built and deployed rapidly. This is leading to a revolution of automated manufacturing.

The future is not going to belong to giant, expensive, monolithic systems – but fast, easy to build, capable systems built in large numbers.

Give the article a read.