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Most AI products are built around a conversation between a model and a person. TypeSafe starts from a different bet: large-scale automation will be dominated by AI-to-AI and AI-to-software interactions, so the machine interface matters more than the chat interface.
We call this Machine Native Intelligence: AI with software-like properties such as structure, reliability, observability, testability, speed, consistency, and low cost.

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Building prod, not God

TypeSafe is not trying to build a model that does everything. It is designed for production systems where code needs a narrow decision it can inspect and act on. Our expectation is that large-scale AI automation will be closer to 99% machine-to-machine interactions and 1% human interaction. That shifts the design target from responses that feel good to read toward outputs that behave predictably inside software. Read the TypeSafe manifesto.

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Three post-training approaches

Pretrained language models have been adapted in two major ways. TypeSafe adds a third. RLHF and RLVR are shown here for context; TypeSafe’s training path is RLCD.

RLHF

Reinforcement learning from human feedback turned pretrained models into chatbots. It trains models to produce responses people prefer.

RLVR

Reinforcement learning with verifiable rewards created reasoning models that are strong at tasks such as mathematics, but slower and more expensive.

RLCD

Reinforcement learning for calibrated decisions trains TypeSafe to return decisions and calibrated probabilities instead of generated text.
RLHF was used to train InstructGPT and ChatGPT and was co-invented by Diogo Almeida, cofounder of TypeSafe.
Pretrained language models branch into muted RLHF and RLVR paths and an emphasized RLCD decision-model path.Pretrained language models branch into muted RLHF and RLVR paths and an emphasized RLCD decision-model path.

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RLCD and calibrated decisions

RLCD optimizes for a different output contract:
  • The model does not generate text.
  • It returns decisions and probabilities.
  • Higher probability should correspond to a greater chance that the answer is correct.
Calibration makes uncertainty usable by software. Across many predictions from a well-calibrated model:
  • Outcomes assigned a probability of 0.2 should occur about 20% of the time.
  • Outcomes assigned a probability of 0.8 should occur about 80% of the time.
  • Outcomes assigned a probability of 1.0 should occur 100% of the time.
These rates describe groups of predictions, not a guarantee about any single answer. See Confidence for guidance on deciding when software should act or escalate.

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The problems with RLHF

RLHF teaches a model to say things that people prefer. That objective works well for chatbots, but it can also reward sycophancy and confident-sounding hallucinations. Preference optimization also causes mode dropping: the model learns to favor a particular style, such as instruction following, while reducing the probability of other possible outputs.
The probability distribution of a base model compared with a narrowed, mode-dropped distribution after RLHF.The probability distribution of a base model compared with a narrowed, mode-dropped distribution after RLHF.
An output can be compelling to a person without being reliable enough for unattended automation. Human preference and machine trustworthiness are different optimization targets.
Mode dropping is a milder version of mode collapse. In the classic generative-adversarial-network failure mode, a generator learns to produce the same kind of output repeatedly because that output continues to fool the discriminator.
Repeated characters illustrate a GAN suffering from mode collapse.Repeated characters illustrate a GAN suffering from mode collapse.
RLHF remains a good fit for conversational models. TypeSafe’s position is that production automation needs a different training objective—one centered on constrained decisions and calibrated uncertainty.