open ai Flashcards
technological
advabacemnets in ai hardware
computational power access
rapid ai innovation
“ ai boom”
socio cultural
ethical ai demands
public perception
privacy expectations
increase in demand for computational resources
political / legal
ai satefy regulations
data usage restriticons
NYT lawsuit–> New York Times sued them for taking data without conscent
also was sued by elon musk bc they breached the benefits all of humnaiity statement
copyright infringement issues
data from public sources could cause issues
economic
funding competition
investor expectations
five forces
supplier
- high
- hardware dependency (nvidias GPU)
- cloud services dependence (microsoft azure)
- hard to get the chips
- hardware providers like google, Nvidia made the gpu and TPU
subtitues
- low level, no comparable technologies outside of the ai domain
- gpu are dofferienated and difficult to replace, the market is also very concentrated
threat of new entrants (moderate)
- high barriers to entry like capital requirements of needing a large investment to start off
- established competitors like microsoft google who are big tech companies
- need to have knowledge about the tech
- easy access to distrubtion channels
buyers
- high
- growing alternatives like anthropic , google bard
- limited differentiation
- low switching costs
rivarly
- high number of competitors (deep mind, gemini, microsoft, meta) who have deep investments
- some of the competitors have created ecosystems like google can be linked to google docs so creates switching costs
- moderate product differentiation (most competitors offer similiar funct9onailites )
- low switching costs which creates price competition
- technological advbacemnts (competition for ai model superiority)
key success factors
- strategic partnerships to get the funding necessary
- good technology
- talent acquisition and retention –> need to have people who are very knowledgeable in the field
- financial resources
- innovation
- compliance with regulations
strengths
innovation and tehcnologcual leadership
strategic partnerships–> Microsoft
talent pool–> top talent
global recognition
adapdabiltu–> they keep adapting their model to become better
first mover advantage
weaknesses
governance instability (their organizational structure is not good, fights over chips, employees wanted to leave because of what happened with Altman )
dependence on external infrastructure (depdnece on suppliers)
high costs and over reliance on their partnership
limited financial resources compared to competition
highoperation costs
maybe their mission statement bc they keep having to live up to it
opportunties
ai expansion into new domains
partnerships
vertical integration to create the gpu chips themselves (backward integration)
pursuing artificial general intellergance
threats
intensifying compeittion–> Claude 4 might surpass chat
regulatory challenges
ethical concerns–> ai might surpass human intelligence and pose risks
resource limitations
chip shortage (there was a shortage of GPU chips)
Nvidia was moving more downstream
human resources
open ai mission attracted top talent in the ai field
Ilya sutksever (ex google researcher), he turned down other places to work at open ai
sam altman
financial resources
capital investment from founders and investors (1 bn)
usd 13 bn investment from Microsoft
revenue streams from premium services
partnerships also need funding
began as non profit
organizational resources
flexible organizational design
they restructured from non profit tp a hybrid structure to raise more capital
limited capability to generate financial resources
couldnt make deicisons resulting sam altman
reputation resources
open ai foundational commitment to developing ai for the common good has enhanced its reputation
top talent wanted to come and work for ai
physical
infrastructure for large scale ai training through its partnership with microsoft