@ConventusFDI The multi-asset angle matters more than the headline rate. Question is whether the reform lets a single-family office hold operating businesses and private credit under the same profits tax exemption, or if those still fall outside the qualifying transaction list.
@boardyai building LeverVenture, a cross-border growth equity platform bridging VC and PE for companies doing $5M-$30M in revenue. need Boardy Pro for this
5/5 Recent client example:
AI inventory optimization β 23% cost reduction in 60 days
Secret: Spent 3 weeks on integration before touching ML
Result: AI actually changes purchase orders automatically
Integration > Intelligence
1/5 π§΅ Your AI project isn't failing because of bad data or wrong models
It's failing because of integration architecture
Worked with 50+ companies on AI deployments. The pattern is always the same...
4/5 Quick test for your AI system:
π Can it write back to your core systems?
β‘ Real-time sync or just batch exports?
π₯ Are humans manually copying AI outputs?
If you're failing these tests, you have an intelligence problem, not an AI problem
7/7 The bottom line:
Life sciences investing in 2026 isn't about choosing biotech OR AI
It's about finding founders who can execute at the intersection
Technical fluency + domain expertise = non-negotiable
What convergence trends are you tracking?
1/7 π§΅ Life Sciences VC in 2026: Why the old playbook isn't working anymore
Just analyzed $270M+ in Q1 deals. The data shows a fundamental shift that most investors are missing...
6/7 What this means for fund strategy:
β Pure-play biotech funds struggling
β Generalist AI funds missing domain expertise
β Hybrid approaches with technical + clinical diligence
β Co-investment with strategic partners (Sanofi, etc.)
1/7 π§΅ The AI-biotech convergence is creating the biggest shift in life sciences investing we've seen in decades. Here's what the data is telling us about 2026 trends:
6/7 π Platform > single assets: Investors are prioritizing scalable technology platforms over one-shot drug candidates. Proprietary datasets + novel ML = defensible moats that pharma giants want to partner with.