The hottest Funding Models Substack posts right now

Change typically begins with a focus on delivery predictability and reducing work-in-progress, where throughput is treated as the main measure of value.
Introducing goals or OKRs shifts attention toward outcomes, but real outcome orientation only sticks when teams, architecture, funding, and ways of working are redesigned so objectives guide work as testable hypotheses.
The healthiest state is when value models underpin org design, goals, funding, and architecture so technology is inseparable from the business, but there is no final destination—models keep evolving and organizations can regress.

Government involvement in higher education often leads to a focus on pleasing officials rather than genuinely educating students. This can create a cycle that stifles diverse ideas.
There are too many people going to college instead of exploring other options like trade schools or apprenticeships. We need to rethink and expand our education paths.
Instead of just making universities hire more conservative professors, we should consider cutting funding for traditional higher education altogether, and instead support alternative educational models.

Science funding should prioritize producing reliable, useful knowledge and reward being right, supporting both risky exploratory work and goal-oriented projects.
Funders must cut heavy administrative burdens and require open sharing of data and methods so others can verify and build on results quickly.
The funding system should be more flexible and diverse: experiment with new funding models, provide stable support for infrastructure and staff scientists, and distribute support more evenly across career stages.

OpenAI is changing its company structure to better fit its needs for raising and making money. They are moving to a type of corporation that requires them to think about more than just profits.
The new structure will allow OpenAI to balance its mission with the interests of shareholders. This means they must consider how their decisions affect everyone, not just those who own stock.
Although the changes could face challenges, like legal issues with competitors, OpenAI seems focused on preparing itself for future success.

Science often feels inefficient and frustrating for researchers, leading to disillusionment. Many wish for changes that can improve how scientific work gets done.
Funding for science can be improved by connecting researchers with venture capital and industry, which can help new ideas grow. Scientists need more support and training to navigate these opportunities.
The current grant and publication systems are riddled with issues, making it hard for scientists to succeed. Changing these systems could lead to more effective and fairer funding processes and help researchers publish their work without unnecessary hurdles.

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The current venture capital model is flawed with oversupply of capital leading to inflated early-stage valuations.
There is a need for a rethinking of venture capital funding model to balance fund size, number of investments, and ownership stake.
Smaller seed rounds focused on hitting milestones may be more rewarding than accepting large cheques with high valuations.