[AINews] The Future of Latent Space
- Plans for AINews v3
- Plans for a new home!
- We are open for business - and @supabase are our first sponsors!
This is tracking to be the #1# podcast we've released in all of 2026.
We asked Jev's creator @CompleteSkeptic to explain Jev, and why he does NOT call it a "Decision Model" — why it's called Jev
available wherever the finest podcasts are sold! reviews help us a lot to keep us relevant in the rankings 🙏
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Jev and the System One Model: RLCD, intelligence/$, reliable AI, & the end of chat-first AI
@typesafeai CEO @CompleteSkeptic explains why AI can solve extraordinarily hard problems yet still fail to automate basic work, why Jev is built for reliable decisions inside software instead of chat, why TypeSafe rejects public benchmarks and refusals at the API layer, why data and the right task matter more than brute-force compute, how System One Models could reshape coding agents and software, and why even with $1 billion he wouldn’t pre-train a model from scratch.
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What can games like Diplomacy and 1830: The Game of Railroads and Robber Barons teach models about strategy and other work-related skills? We spoke to @goodstartlabs CEO @alxai_ (an alumni of @every) to find out.
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🆕 Humanity’s Last Invention — with @RichardSocher!
@recursive_si is the latest neolab to burst on the scene with a $5B fundraise and one of the most impressive cofounder lists ever assembled to tackle open ended, self-improving AI for AI research. We dive into the Eureka Machine, Richard's 10 Spaces of Intelligence (so much left until AGI!), DecaNLP vs @alecrad and why AI peer review is broken, and why this may just be the last invention that humanity has to do on our own.
Timestamps
00:00:00 The Eureka Machine and Superintelligence
00:02:23 AI Optimism, Slow Takeoff, and Regulation
00:07:56 AI Safety, Reward Hacking, and Anthropic’s Constitution
00:11:49 Alignment, Personalization, and Open Source AI
00:15:46 Why Richard Started Recursive
00:20:03 Recursive Self-Improvement and the Founding Team
00:22:55 Are Today’s LLMs Enough?
00:29:03 DecaNLP, GPT, and the Rejected Idea Ahead of Its Time
00:34:38 Open-Endedness and Evolutionary AI
00:36:38 What Happens When AI Chooses Its Own Goals?
00:41:16 Superintelligence for Science
00:42:40 GPUs, Compute, and the Limits of AI Takeoff
00:45:07 Recursive’s Results: AI Beating Humans and Their Agents
00:49:14 Reward Engineering and Auto Research
00:53:12 The AI Economist and Simulating Entire Economies
00:58:07 LLM Simulations, Personas, and Mode Collapse
01:03:38 Recursive’s Roadmap, Agents, Search, and Finance
01:09:13 The Upper Bounds and Spaces of Intelligence
01:30:21 Goals, High Agency, and Advice for Builders
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soon on LS:
- Why you are still underrating Prompting
- Claude Code Mods (released yesterday @bcherny)
- Pacing the Frontier
subscribe:
just finished recording on latent space
I’m excited about this one- we get very technical about things we haven’t really talked about much yet
Before co-founding @kepler_ai_hq, @VinooGanesh led Spark at Palantir and built Project Frontline — a pioneering program for Forward Deployed Engineers. He takes us through the best practices of FDEs.
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still think this is/was one of the best takes of the year @eisokant with @swyx and @vibhuuuus on @latentspacepod
very much aligned with the thesis - build your own agent infrastructure.
give the agent an isolated sandbox with a thin set of tools and workflows, and the ability to write and execute code. Keep the harness thin.
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I spent the past few weeks learning and using Centaur. Sharing here as building your own control plane and managing agents on your own infrastructure is surprisingly accessible, and will only become more compelling as agent processes scale.
given events unfolding over the past few weeks - safe to say that wrapping robust, deterministic infrastructure around increasingly intelligent and autonomous agents is a good thing to be doing.
If you expect agentic work/processes to scale (you should), you are going to need a system for managing increasingly autonomous agents.
Most of us are building elaborate systems directly into the agent/harness (memory, tools, permissions, state and logic etc)
This is likely the wrong approach.
Ideally - the agent/harness should be one component inside a deterministic system, not the other way around.
Nutshell - a Rust API with Postgres DB that records state and coordinates execution (control plane). When work needs to happen, the control plane > Kubernetes, creates isolated sandboxes, and inside - your probabilistic machines (agents) do the work.
This whole system becomes more compelling as agent interactions scale.
TLDR - don’t build the system into the agent/harness, make the agent one component inside a deterministic control plane that you own and manage.
Give it an isolated sandbox (kubernetes) with a thin set of tools and workflows and the ability to write and execute code, keep the harness thin, keep the secrets out of the box (iron.proxy).
I did a longer explainer video of the entire thing here for any interested:
Centaur is open source, created by @matthuang , @gakonst from @paradigm
Also - created a context app extension for centaur. If anyone is keen to experiment with this, or set it up, please get in touch.
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@sqs runs AMP with no mandatory code review, no local dev, and he is now arguing his own team out of CI.
- Review before main never existed at AMP; the replacement is 20 people who all behave like co-founders
- A local CLI agent inherits every token on your laptop, a cloud agent gets 30 minutes of read-only prod logs
- He shipped a core schema migration from a plane while the agent watched logs and rolled back bad phases
- His line: an agent is the ultimate settings screen for any software
- His call: CLI coding agents basically dead in two months
Via @latentspacepod with @swyx:
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E2B is the default choice for Astra
🆕 project: our Frontier AEO tracker
What does Astra prioritize? what sources does it use? What shifted from Sol to Astra, and Opus to Fable?
all our answers within!
🆕 project: our Frontier AEO tracker
What does Astra prioritize? what sources does it use? What shifted from Sol to Astra, and Opus to Fable?
all our answers within!
for the first time in a year, @OpenAI's model launch has been better received than a Claude launch.
this is a huge upset that we had previously not thought possible this year.
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sorry for the radio silence folks - got sucked into extreme LLM psychosis. but i can confidently say we have crossed over into a new age of AI Engineering and we are never, ever, looking back.
this isnt even EVERYTHING i did with Astra but i'll append more reports as I publish them on LS!
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We spent >20B tokens throwing @openai's Astra at every AI Engineering task we could think of, beyond cute Blender demos and fun games.
Here's everything Astra can do, and do so at <$6 an hour (serious):
- choose and train models
- label data (both helping you label and then using your labels for active learning)
- keep pipelines saturated
- instrument and read logs
- deploy and debug entire systems in one shot
- fan out and command and eval subagents (including agents running other models)
- keep coherence over billions of tokens of a single agent thread.
more to come on @swyx's coverage of the Fable- and Astra-class of 2026!
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GitHub invented pull requests, and for 18 years they've been open by default. But now some of the top AI-native open source projects are shutting PRs off, because they've found a better way.
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I did an interview with @LatentSpacePod about the future of SaaS and where we’re taking Lovable.
I don’t think SaaS disappears, but users will interact with fewer interfaces & websites directly.
People will stop opening 20 different tabs for different tools, but the capabilities behind those products can still be incredibly valuable.
Anyone building vertical SaaS should be careful to build only for humans using their product. The potential leverage of letting AI use the functionality & capabilities is, if not already, going to be a considerably larger opportunity.
And for Lovable, this direction feels very natural. People went from building prototypes, to production products, to building the operational software behind entire companies.
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Lovable is a $13B company with a $500 million annualized revenue run rate. It's just 3 years old, but essentially competes with Vercel & Cloudflare now — amazing story. And now @Lovable is branching out from AI-powered web app creation and into MCP-powered ‘capabilities’.
We talk to CTO @FabianHedin about the future of SaaS and how Lovable is building a "company brain."
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🆕 We’re launching a new Forward Deployed Engineering pod with @realbasilchatha!
In just the last few months, $9B+ has been committed by OpenAI, Anthropic, Google, and Microsoft to deploy AI into the enterprise. Model capabilities are there, but it takes a lot to make them reliable and usable for customers at scale.
In our pilot episode, we dive deep into the practical realities of building enterprise voice agents with @decagon, @vapi, @retell, @smallest_ai, and @daily.
We talk about:
-> Why STT -> LLM -> TTS pipelines are used instead of voice-to-voice models
-> How to balance intelligence vs latency for different use cases
-> Why turn-taking is still not a solved problem
Check it out!
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Models keep absorbing the harness into their weights — soon, it will be a harness for human attention rather than for the model.
A thoughtful analysis of the past and future of agent harnesses by @daniel_mac8
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You are not sufficiently appreciating the scope of RSI.
Here's a brief prehistory.
Token prices keep getting cut. The headline all month is that AI got cheap.
Arvind Jain @jainarvind runs Glean, which sits between big enterprises and every frontier model and watches what they actually spend. He says the bill went the other way.
"on a per token basis, they're more expensive, sometimes double or quadruple the rates of the previous models. And then users actually use them to run much longer tasks. So you're spending, like, 10 times, 20 times, more, on a per user basis, than what you were doing last year"
Then this.
"in the last three months, because AI got so expensive, businesses have started to find it untenable to maintain these AI investments"
A year ago he says open source usage was minuscule and carried a stigma. Now he says most enterprises call it a key part of the strategy.
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