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Funding Your Own Disruption
21 days ago
- Kodak invented digital camera tech in 1975 but shelved it, dismissing its potential due to profitable film business.
- Netflix pivoted to streaming in 2007 while its DVD-by-mail service was still profitable and growing.
- The BCG Growth Share Matrix explains the strategic choice: reinvest Cash Cow profits into Question Marks to nurture future Stars.
- Kodak defended its Cash Cow (film) despite accurate internal predictions of digital takeover, leading to bankruptcy in 2012.
- Netflix intentionally diverted attention from its DVD business to fund streaming, treating disruption as inevitable.
- Netflix's Qwikster misstep in 2011, splitting services, caused backlash but was quickly corrected, highlighting the importance of fast feedback.
- Key takeaway: Success depends on funding disruption early, when mistakes are affordable, not just recognizing trends.
Nvidia Has Become the Bank Behind the AI Boom
21 days ago
- Nvidia is evolving from a chip designer to a financier and partner in the AI economy.
- The company offers financing, rent-back agreements for idle GPUs, and revenue-sharing with cloud providers.
- This shift means Nvidia now has a financial stake in post-sale outcomes, not just hardware sales.
- Large-scale projects like Firmus and Sharon AI involve hundreds of thousands of GPUs under Nvidia-backed financing.
- Nvidia's CFO involvement signals this is a strategic move, not an isolated experiment.
- This model aligns with earlier deals with companies like CoreWeave and Lambda.
- Financing GPUs can generate longer-term revenue compared to one-time sales, diversifying Nvidia's income.
- Nvidia's new role may create conflicts as it supports smaller providers competing with its major customers like Amazon and Microsoft.
- If demand for AI infrastructure slows, Nvidia could face increased operational risk from financing and utilization guarantees.
- Analyzing Nvidia solely as a chip company is no longer sufficient due to its expanded business model.
Sometimes free isn't cheap enough
20 days ago
- Tech influencers claim code will soon be free, but free isn't always cheap enough due to hidden costs.
- The example of free buffalo shows that acquisition cost is irrelevant when maintenance, time, and risks are high.
- Agentics focuses on using agents effectively, and businesses should not over-index on acquiring free code without considering other costs.
The Myth of the Solo Unicorn
17 days ago
- Ronald Coase's paper 'The Nature of the Firm' explained that firms exist because transaction costs (like finding talent and negotiating) make it cheaper to organize work internally than in the open market.
- AI dramatically reduces production and coordination costs, fueling the myth of a one-person billion-dollar company, but it does not eliminate trust costs, which are crucial for business survival.
- A product can be built solo, but a company requires trust systems for procurement, enterprise sales, compliance, and accountability—areas where a solo founder often fails.
- Procurement in large organizations prioritizes confidence and risk management over product demos, making it difficult for one-person companies to scale due to perceived single points of failure.
- AI can generate infinite content, but effective marketing relies on judgment, resonance, and timing—skills that remain scarce and valuable despite automation.
- The future lies in 'fractional enterprises': small, leveraged teams of full-stack executives orchestrating agents, contractors, and tools, balancing automation with human trust and accountability.
- Key metrics shift from headcount to revenue density (revenue per employee), as companies like WhatsApp demonstrated high value with minimal staff, signaling a new business model.
- AI enables more people to start companies with lower costs, likely increasing the number of firms and jobs, contrary to predictions of job loss from automation.
SaaS Still Has a Moat. Just Not the One We Think
10 days ago
- SaaS's primary competitive advantage is not data lock-in but rather the learned behavior and habitual use of its user interface, which creates significant switching costs for companies.
- The data moat is weakening due to AI-driven data portability, agent-to-agent communication reducing reliance on human UX, and new data paradigms like context graphs and vector stores.
- Five headwinds threatening SaaS dominance: AI forcing data portability, agent-to-agent interactions diminishing UI relevance, new data paradigms making old structures a liability, internal context layers reducing individual SaaS tool necessity, and AI coding flooding the market with cheaper alternatives.
- Large SaaS companies should respond by building robust APIs and MCPs, owning the context orchestration layer, investing in AI-native data backends, and making it easy and affordable for companies to run processes on their platforms.
- Companies tired of SaaS sprawl should start by building a context layer that syncs with existing tools, create agentic workflows on top of it, and gradually phase out SaaS tools as the context layer replaces their value.
- Startups competing with big SaaS should focus on owning the new context layer, syncing with incumbents' data, and building innovative interfaces and capabilities that incumbents cannot easily replicate due to their legacy constraints.
- SaaS is not dying imminently, but pressures are compounding; companies that prepare for an agent-to-agent norm, context layers as systems of record, and AI-native data structures will gain a long-term advantage.

Hasty Briefsbeta

business strategy

Funding Your Own Disruption

Nvidia Has Become the Bank Behind the AI Boom

Sometimes free isn't cheap enough

The Myth of the Solo Unicorn

SaaS Still Has a Moat. Just Not the One We Think