Investment Companies Daily HGT CMD take-aways, Equity Strategy, GRID JV completion, CHI final results

Research evidence excerpt

Investment Companies Daily HGT CMD take-aways, Equity Strategy, GRID JV completion, CHI final results

g model is based on number of users of the

software and agentic AI may replace many of those users. Hg contrasts a PE generalist

approach, which might look at using AI to cut some costs with a 1-3 year earnings growth

contribution to its specialist product led approach with a 10-20 year earnings growth

potential.

• Offsetting LLM industry monetisation post huge capex spending: Companies and

their shareholders will want a return on the large capital expenditure that has gone into

data centres, computer chips and other related elements all focused on rolling out LLM

products. This may lead to cost increases for LLMs including higher token rates and

perhaps tiered pricing, where the latest and greatest models command a premium rate or

are released only to premier clients. Hg thinks it can offset some of this potential impact

by building products that can segment queries sent to LLMs with those that are less

intensive, targeted at older, cheaper, LLM models. Hg also has an agreement in place with

Anthropic that gives its portfolio companies a competitive rate for accessing LLM

models.

• AI spend is focused on coding and not deterministic workflows: Hg has analysed

public reported data from AI native start-up companies and found the vast majority of

their revenue comes from coding tasks, replacing work that would have been done by

software engineers, and as yet there is little earnings coming from areas such as

accounting and other deterministic workflows, the area where Hg portfolio companies

operate. Hg thinks that much of what has caused the public software companies sell-off

in 2026 is an information vacuum. It thinks that additional disclosures would show AI